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1
Sjödin, Wågberg Anton. "Prices on electricity and the prices on stocks : -A Vector autoregressive approach". Thesis, Umeå universitet, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-153448.
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This study will investigate if a relationship exists between the price of electricity and the Swedish stock market. This study will also try to investigate what consequences an increase in the price of electricity will have on the return of the Swedish stock market. Economic theory and earlier literature will then be used to try to explain the results obtained in this study. The results from the tests performed in this study imply that a one-way Granger-causality exists between the prices on electricity and the price on the OMX 30. The impulse response functions performed shows that a positive shock in the price on electricity will predict an increase in the return of the OMX 30 in the short run. This effect may come from the existence of a countercyclical risk premium. Although further research needs to be performed to conclude that this is the true reason for the observed result.
2
Bethapudi, Daniel Naveen. "Dynamic interactions between electricity prices and the regional economy". Texas A&M University, 2003. http://hdl.handle.net/1969.1/2275.
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In this thesis we study characterize the dynamic relationships among two electricity
price variables (residential and commercial) and six regional economic variables in order
to examine each individual variable??s role in regional economic activity. We also answer
the question ??Do electricity prices have impact on regional economic variables???
We use two statistical techniques as engines of analysis. First, we use directed acyclic
graphs to discover how surprises (innovations) in prices from each variable are
communicated to other variables in contemporaneous time. Second, we use time series
methods to capture regularities in time lags among the series.
Yearly time series data on two electricity prices and six regional economic
variables for Montgomery County (Texas) are studied using time series methods.
Directed Acyclic Graphs (DAGs) are used to impose restrictions on the Vector Auto
Regression model (VAR). Using Innovation Accounting Analysis of the estimated
Vector Auto Regression (VAR) model we unravel the dynamic relationships between the
eight variables. We conclude that rising electricity prices have a negative impact on allregional economic variables. The commercial average electricity prices lead residential
average electricity prices in the time frame we studied (1969-2000). Rising residential
electricity prices also have a positive impact on income derived from transfer payments.
3
Dongo, Kouadio Kouman. "Forecasting the Chinese Futures Markets Prices of Soy Bean and Green Bean Commodities". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/23.
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Using both single and vector processes, we fitted the Box-Jenkin’s ARIMA model and the Vector Autoregressive model following the Johansen approach, to forecast soy bean and green bean prices on the Chinese futures markets. The results are encouraging and provide empirical evidence that the vector processes perform better than the single series. The co-integration test indicated that the null hypothesis of no co-integration among the relevant variables could be rejected. This is one of the most important findings in this paper. The purposes for analyzing and modeling the series jointly are to understand the dynamic relationships over time among the series and improve the accuracy of forecasts for individuals series by utilizing the additional information available from the related series in the forecasts for each series.
4
Ångman, Josefin. "What is driving house prices in Stockholm?" Thesis, Stockholms universitet, Nationalekonomiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-130692.
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An increased mortgage cap was introduced in 2010, and as of May 1st 2016 an amortization requirement was introduced in an attempt to slow down house price development in Sweden. Fluctuations in the house prices can significantly influence macroeconomic stability, and with house prices in Stockholm rising even more rapidly than Sweden as a whole makes the understanding of Stockholm’s dynamics very important, especially for policy implications. Stockholm house prices between the first quarter of 1996 and the fourth quarter of 2015 is therefore investigated using a Vector Error Correction framework. This approach allows a separation between the long run equilibrium price and short run dynamics. Decreases in the real mortgage rate and increased real financial wealth seem to be most important in explaining rising house prices. Increased real construction costs and increased real disposable income also seem to have an effect. The estimated models suggest that around 40-50 percent, on average, of a short-term deviation from the long-run equilibrium price is closed within a year. As of the last quarter 2015, real house prices are significantly higher compared to the long run equilibrium price modeled. The deviation is found to be around 6-7 percent.
5
Wong, Kin-man y 黃健文. "A vector autoregression (VAR) model of housing starts and housing price in Hong Kong". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hdl.handle.net/10722/194603.
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It is observed that there are many different models about housing price. Yet, this is relatively smaller number of studies about housing starts. This thesis is an empirical study to work out the relationship between housing starts, housing price and other economic and policy instrumental factors. To achieve this objective, a Vector Autoregression (VAR) model is built since there is inter-relationship between housing starts and housing price.
By applying previous models filled with the research gaps, a new VAR model about the housing starts and housing price in Hong Kong is built. Four hypotheses are tested in the thesis. The first and second hypotheses are if housing starts and housing price are affected by the given exogenous variables. The third hypothesis is if the past movement of economic variables reliable in predicting future values of that variable. The last hypothesis is to test if the “high-land-price” policy really pushes up the housing price.
The empirical results found in this thesis are a little bit different to previous studies in Hong Kong and overseas. Factors which are frequently proved to be statistically significant are not significant in this study (e.g. interest rate and tender price index). Developers in Hong Kong are found to care more about the future market rather than the current market conditions. Many factors do not exert an influence directly on housing starts but indirectly through their impact to the change of the change of the housing price.
It is interesting to know that housing starts react negatively to a change in housing price. An increase in the change of housing price is a bullish signal for the developers. They will hold the land for a while until they expect the peak is coming upon the completion of a project. Therefore, the empirical results suggest the government has to introduce some policies which will lead to a fall in housing price in case that she wants to increase the supply of new private residential housing. Developers will accelerate the applications to commence construction when they expect there will be a downward trend in the housing price (which is shown by a negative change of the housing price..published_or_final_version
Real Estate and Construction
Master
Master of Philosophy
6
Persson, Rickard. "The short and long-term interdependencies between stock prices and dividends: A panel vector error correction approach". Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-255666.
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This paper examines the short and long-term interdependencies between stock prices and dividends. I utilize firm level data from FTSE ALL SHARE from 1990-2014 and apply panel vector error correction model estimated with Engle & Grangers (1987) two-step procedure. The results show that there is a bi-directional long-term relationship between stock prices and dividends, i.e. an adjustment process is at work when a disequilibrium occurs. I also find a bi-directional short-term relationship. This paper also shows that Lintners model and the present value model are relevant frameworks in stock valuations.
7
Fischer, Manfred M., Florian Huber, Michael Pfarrhofer y Petra Staufer-Steinnocher. "The dynamic impact of monetary policy on regional housing prices in the US: Evidence based on factor-augmented vector autoregressions". WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6065/1/2018%2D02%2D16_housing_favar_final.pdf.
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In this study interest centers on regional differences in the response of housing prices to monetary
policy shocks in the US. We address this issue by analyzing monthly home price data for
metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian
model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the
autoregressive coefficients and factor loadings, while monetary policy shocks are identified using
high-frequency surprises around policy announcements as external instruments. The empirical
results indicate that monetary policy actions typically have sizeable and significant positive effects
on regional housing prices, revealing differences in magnitude and duration. The largest
effects are observed in regions located in states on both the East and West Coasts, notably California,
Arizona and Florida.Series: Working Papers in Regional Science
8
Borén, Christofer y Felix Ewert. "Assessing the Effect of the Riksbank Repo Rate on National Output and Price Level in Sweden : Focusing on Employment and Housing Prices". Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228969.
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There is no single commonly adapted model that explains the influence that various monetary policy instruments carry for the economy. During 2011-2017, the Swedish inflation rate has remained below the 2 percent target which has led the Riksbank to take measures aimed at stimulating the inflation. As of May 2018, the repo rate has experienced a number of decreases and is now at 0:50% which represents an unprecedentedly low level. With the inflation rate remaining below the target whilst the housing market has experienced substantial growth and recent decline, the question arises regarding what impact the repo rate exerts on various macroeconomic measures. In this paper, a statistical time series analysis is conducted using a Vector Autoregression model and the impulse responses are studied. A model of 7 economic variables is constructed to specially study the effect of the repo rate on employment and housing prices. Results demonstrate that rational expectations exist in the economy. Furthermore, results show that the repo rate influences factors affected by inflation rapidly, exerting maximum influence during the first year after the shock. On the other hand, real variables based on quantitative measures that are adjusted for inflation experience the greatest influence of the repo rate after a delay of 6 to 7 quarters. Employment experiences the greatest negative response to a repo rate shock after 7 quarters, with a magnitude of 0.317 standard deviations per standard deviation in the repo rate shock. Housing prices experience the greatest negative response to a repo rate shock after 4 quarters, with a magnitude of 0.209 standard deviations per standard deviation in the repo rate shock.Det finns ingen allmänt vedertagen modell som beskriver olika penningpolitiska instruments påverkan på ekonomin. Under 2011-2017 har Sveriges inflationstakt legat under 2-procentsmålet vilket har fått Riksbanken att vidta åtgärder i syfte att stimulera inflationen. Fram till maj 2018 har upprepade sänkningar av reporäntan genomförts och den ligger i dagsläget på 0:50% vilket är den lägsta nivån någonsin. Då inflationstakten inte nått målet samtidigt som bostadsmarknaden har upplevt kraftig tillväxt och nylig nedgång uppstår frågan gällande vilken effekt som reporäntan utlovar på diverse makroekonomiska mått. I denna rapport genomförs en statistisk tidsserieanalys med en vektorautoregression och impuls-responserna studeras. En modell med 7 ekonomiska variabler skapas för att specifikt studera effekten av reporäntan på sysselsättning och bostadspriser. Resultaten visar att rationella förväntningar finns i ekonomin. Vidare visar resultaten att reporäntan influerar inflationspåverkade variabler omgående, med maximal påverkan inom det första året efter chocken. Å andra sidan påverkas volymbaserade variabler som justeras för inflation maximalt först efter en fördröjning på 6 till 7 kvartal. Sysselsättningen upplever störst negativ påverkan från en reporäntechock efter 7 kvartal motsvarande 0.317 standardavvikelser per standardavvikelse i chocken. Bostadspriser upplever störst negativ påverkan från en reporäntechock efter 4 kvartal motsvarande 0.209 standardavvikelser per standardavvikelse i chocken.
9
Rostami, Jako y Fredrik Hansson. "Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells". Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385823.
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In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early 2019 as the time series to be forecast. Firstly, we compare theperformance of two machine learning methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the better performing machine learning method for a twelve-month forecast,but that it still does not forecast as well as the ARIMA model for the same forecast period.
10
Tao, Juan. "A re-examination of the relationship between FTSE100 index and futures prices". Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/8071.
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This thesis examines the validity of the cost of carry model for pricing FTSE100 futures contracts and the relationship between FTSE100 spot and futures markets during two sub-periods characterised by different market trading systems employed by the LSE and LIFFE. The empirical work is carried out using three approaches to econometric modeling: a basic VECM for spot and futures prices, a VECM extended with a DCCTGARCH framework to account for the conditional variance-covariance structure for spot and futures prices and a threshold VECM to capture regime-dependent spot-futures price dynamics. Overall, both the basic VECM and the DCC-TGARCH analysis suggest that there are deviations from the cost of carry relationship in the first sub-sample when transactions costs in both markets are relatively high but that the cost of carry relationship tends to be valid in the second sub-sample when transactions costs are lower. This is further confirmed by the evidence of higher conditional correlations between the two markets in the second sub-sample as compared with the first, using the DCC-TGARCH analysis. This implies that the no-arbitrage cost of carry relationship between spot and futures markets is more effectively maintained by index arbitrageurs in the second period when market conditions are closer to perfect market assumptions, and hence the cost of carry model could be more reasonably used as a benchmark for pricing stock index futures. The threshold VECM analysis depicts regime-dependent price dynamics between FTSE100 spot and futures markets and leads to some interesting and important findings: arbitrage may not be practicable under some market conditions, either because it is difficult to find counterparties for the arbitrage transactions, or because there is significant risk associated with arbitrage; as a result, the cost of carry model may not always be suitable for pricing stock index futures. Furthermore, the threshold values yielded from estimating the threshold VECM reflect the average transaction costs for most arbitrageurs that are more reliable and fair than subjective estimations.
11
Fischer, Manfred M., Florian Huber, Michael Pfarrhofer y Petra Staufer-Steinnocher. "The dynamic impact of monetary policy on regional housing prices in the United States". WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6658/1/2018%2D11%2D16_housing_favar_(002).pdf.
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This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary
policy shocks on housing prices across metropolitan and micropolitan regions. To simultaneously
estimate the model parameters and unobserved factors we rely on Bayesian estimation
and inference. Policy shocks are identified using high-frequency suprises around policy
announcements as an external instrument. Impulse reponse functions reveal differences in regional
housing price responses, which in some cases are substantial. The heterogeneity in policy
responses is found to be significantly related to local regulatory environments and housing supply
elasticities. Moreover, housing prices responses tend to be similar within states and adjacent
regions in neighboring states.Series: Working Papers in Regional Science
12
Alsaedi, Yasir H. "An Investigation of the Effects of Solar and Wind Prices on the Australia Electricity Spot and Options Markets: A Time Series Analysis". Thesis, Griffith University, 2021. http://hdl.handle.net/10072/410472.
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Electricity pricing is recognised as being among the most important contemporary policy issues in Australia, and it also represents a critical component of current discussions concerning energy and climate-change policies. Attempts to move forward with energy and climate-change policies have been mostly stymied by concerns regarding potential increases in electricity prices. In relation to such policy discussions, renewable electricity generation is currently considered to be a fundamental factor influencing electricity prices. Due to the increasing penetration of both wind and solar power generation in Australia, which happens to be coinciding with increasing wholesale and retail electricity prices, there is now a widely held belief that the observed wholesale electricity price increases are related to the increased penetration of renewable energy sources.
Overall, the present study aims to investigate the nature and influence of the solar and wind electricity prices on the Australian spot and options markets. To accomplish this aim, the study begins by investigating wind, solar, spot and options pricing and then developing relevant models on the basis of a univariate state-by-state analysis of Australia’s electricity markets. Next, the study investigates the effects of the solar and wind prices on the Australian spot and options markets by means of a multivariate analysis. In addition, the study investigates the impacts of solar and wind pricing on the “global” electricity spot and options markets, with a particular focus on the Australian, German and American electricity markets. Moreover, the study examines how energy companies develop and manage policies concerning electricity production and pricing, as well as the use of solar and wind power, in the Australian markets.
More specifically, the first part of this study (Paper 1) involves a univariate analysis of the solar, wind, spot and options electricity prices intended to facilitate a more in-depth understanding of the nature of each variable in terms of forecasting, correlations and volatilities with regard to the Australian electricity markets. Quantitative data concerning the electricity markets in five Australian states, namely New South Wales (NSW), Queensland (QLD), South Australia (SA), Victoria (VIC) and Tasmania (TAS), are considered in this study. The results of the analyses reveal increases of between 30.46% and 40.42% in relation to the spot electricity prices as well as between 14.80% and 15.13% in relation the options electricity prices within the Australian National Electricity Market (ANEM) with a two-year horizon. The results also show that wind prices are expected to increase by an average of 5.43%, while the average solar electricity price is expected to decrease by 67.7%.
The second part of this study (Paper 2) involves a multivariate analysis designed to examine the dynamics within the Australian electricity markets, particularly those that may exist between the solar and wind prices and the electricity spot and options markets in each Australian state. The results of the Granger causality analysis indicate there to be a significant unidirectional Granger causal relationship between the solar and wind electricity prices and the spot prices in NSW, QLD, VIC and TAS at the 1% significance level, while in the case of SA, the relationship appears to be significant at less than the 10% level. Moreover, the forecast results suggest that the solar and wind electricity prices reduce the spot and options electricity market prices within the ANEM.
The third part of this study (Paper 3) involves a multivariate analysis conducted to investigate the movements within the international electricity markets. The aim was to examine the impacts of the solar and wind prices on the global electricity spot and options markets, with a particular focus on the Australian, German and US or American markets. The results indicate that the electricity markets in Australia, Germany and the United States are interdependent and related to any changes in solar and wind pricing, which means that all the investigated electricity markets are influenced by movements in other electricity markets.
The fourth part of this study (Paper 4) involves the application of in-depth qualitative technique of analysis. This part was used to investigate the nature and influence of policies and regulations concerning solar and wind pricing and their relationship to the Australian electricity spot and options markets. The analysis was based on data gathered through interviews conducted with chief executive officers, energy managers and other significant personnel from within the Australian electricity industry. The interviewees’ responses regarding the solar and wind policies that were considered relevant to the Australian electricity markets were analysed, and the “thick and in-depth” content data was derived data from the interviews. This set was then used to examine how their views and personal politics tend to influence pricing within the electricity markets. The results suggest that renewable energy policies lower the electricity prices, reduce the risk for investors and result in larger deployment mechanisms.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Environment and Sc
Science, Environment, Engineering and Technology
Full Text
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Yu, Tun-Hsiang. "Essays on the Upper Mississippi River and Illinois Waterway and U.S. grain market". Texas A&M University, 2003. http://hdl.handle.net/1969.1/2278.
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This dissertation examines several issues regarding the congestion on the Upper
Mississippi River and Illinois Waterway. Chapter II identifies and measures the impact
of lock congestion on grain barge rates on these waterways. Results indicate grain barge
rates on both rivers are not affected by lagged lock congestion. In present time,
however, lock congestion in the lower reaches of the upper Mississippi and Illinois
Rivers are found to increase barge rates that link the north central United States to the
lower Mississippi Gulf port area. The findings suggest the impact of lock congestion on
grain barge rates is moderate.
Chapter III explores the interaction between grain prices in export and domestic
markets and transportation rates linking these markets over time. Three model
frameworks were evaluated and some consistent results are observed. In general, shocks
in transportation rates (barge, rail, and ocean) explain a great proportion of the variation
in corn and soybean market prices in the long run, suggesting the importance of
transportation in grain price determination. The volatile ocean freight rates are the mostimportant transportation rates contributing to the variation in grain prices, while shocks
in barge rates on the Upper Mississippi River and Illinois Waterway generally explain
less than 15 percent of the variation in grain prices. The dynamic interrelationships
among the six evaluated transportation rates are also found. In addition, the north
central corn markets likely have the most influence over other markets while soybean
export price dominates the soybean market in the long run.
Chapter IV estimates the structural demand for grain barge transportation on both
the upper Mississippi and Illinois Rivers. Results suggest foreign grain demand is the
most influential force affecting grain barge demand on both rivers. Also, results indicate
an inelastic demand for grain barge transportation on the Upper Mississippi in the short
run; demand is price elastic in the long run. The price elasticity for grain barge demand
on the Illinois River is consistently inelastic. Additionally, the winter season and floods
affect demand on the Upper Mississippi negatively, while barge demand increases on the
Illinois River in winter.
14
Hörnell, Fredrik y Melina Hafelt. "Responsiveness of Swedish housing prices to the 2018 amortization requirement : An investigation using a structural Vector autoregressive model to estimate the impact of macro prudential regulation on the Swedish housing market". Thesis, Södertörns högskola, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-35533.
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This thesis analyzed and estimated the impact of the March 1, 2018 loan to income amortization requirement on residential real estate prices in Sweden. A four variables vector autoregressive model (VAR) was used to study the relationships between residential real estate prices, GDP, real mortgage rate and consumer price index over a time period from 2005 to 2017. First, a structural vector autoregressive (SVAR) model was used to test how a structural innovation in the error term for real mortgage rate affected residential real estate prices. Secondly, an unconditional forecast from our reduced VAR was produced to estimate post 2017 price growth of the Swedish housing market. The impulse response function results stand in contradiction to economic intuition i.e. the price puzzle problem. The unconditional forecast indicates that the housing market will enter a period with slower price growth post 2017, which are in line with previous research. This thesis vector autoregressive model can give meaningful results with regard to trend forecasts but with regard to precise statements as anticipating drastic price depreciation, it falls short. We recommend the use of reduced VAR forecasting with regard to the Swedish housing market.
15
Dupré, la Tour Marie-Alix. "Towards a Decarbonized Energy System in Europe in 2050 : Impact of Vector Coupling and Renewable Deployment Limits". Electronic Thesis or Diss., Paris, EHESS, 2023. http://www.theses.fr/2023EHES0014.
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Pour lutter contre le changement climatique, l'Europe s'est engagée à ce que son système énergétique soit décarboné d'ici 2050.Le système électrique présente un important potentiel de décarbonation. Il peut ainsi contribuer à la décarbonation d'autres vecteurs (hydrogène par exemple), qui auront donc tendance à s'électrifier davantage. Cette thèse s'est donc concentrée d’une part sur la décarbonation du système électrique, et d’autre part sur l'interaction entre les différents vecteurs énergétiques. La décarbonation du vecteur électrique reposera sur l'utilisation des énergies renouvelables. Or, les études qui évaluent leur potentiel obtiennent des valeurs très différentes. Dans le premier chapitre, à travers une revue systématique de la littérature des études de potentiel éolien et photovoltaïque en Europe, la variabilité de ces valeurs a été examinée. Ce chapitre montre que les limites surfaciques des potentiels ne sont pas restrictives, et la variabilité des valeurs est due à l'ajout de critères socio-politiques dans les calculs de potentiel. In fine, la limite au développement des énergies renouvelables ne sera pas la faisabilité technique mais la volonté politique et sociétale et les contraintes du secteur industriel, y compris la disponibilité des ressources naturelles nécessaires, comme les métaux par exemple. Dans un deuxième chapitre, le fonctionnement et les prix du système énergétique couplé, à capacités fixes, ont été étudiés. L'influence des couplages sur les prix des vecteurs énergétiques a été soulignée. En particulier, la flexibilité de la demande en gaz de synthèse (via l'électrolyse) pourrait fixer les prix de l'électricité sur une majorité des pas de temps de l'année. De plus, l’importance de la gestion saisonnière des stocks dans la formation des prix du gaz, et donc de l'électricité, a été mise en évidence. Par ailleurs, pour fonctionner de manière optimale, un tel système nécessite un haut niveau de coordination entre les vecteurs : les variantes qui dégradent la coordination montrent une augmentation significative des coûts d'exploitation du système énergétique. Enfin, un troisième chapitre a traité des conséquences des couplages énergétiques sur les besoins en flexibilité du système. Des variantes sur chaque vecteur ont été analysées à travers l'évaluation des besoins en flexibilité via des indicateurs sur plusieurs échelles de temps. En particulier, l'intérêt de la coordination entre les vecteurs a été confirmé : elle permet aussi d'éviter des investissements massifsTo address climate change, Europe is committed to a decarbonized energy system by 2050.The power system has a large potential for decarbonization. It can thus contribute to the decarbonization of other vectors (hydrogen for example), which will therefore tend to become more electrified. Therefore, this thesis has focused on the decarbonization of the power system on the one hand, and the interaction between the various energy vectors on the other.The decarbonization of the power vector will involve the use of renewable energies. However, the studies that evaluate their potential obtain very different values. In the first chapter, which consisted of a systematic literature review of wind and photovoltaic studies of potential in Europe, the variability of these values was examined. The areal limits of the potentials are not restrictive, and the variability of the values is due to the addition of socio-political criteria to the calculations of potential. Ultimately, the limit to the development of renewables will not be technical feasibility but political and societal will and limits of the industrial sector, including the availability of the necessary natural resources (metals, etc.).In a second chapter, the operation and prices of this coupled system with fixed capacities were studied. The influence of the couplings on the prices of the energy vectors was underlined. In particular, the flexibility of the demand for synthesis gas (via electrolysis) could set the electricity prices on a majority of the time steps of the year. The importance of seasonal stock management in the formation of gas prices, and therefore electricity prices, was highlighted. To operate optimally, such a system requires a high level of coordination between vectors. Variants that degrade coordination show a significant increase in the operating costs of the energy system.Finally, a third chapter addressed the consequences of energy couplings on the system flexibility requirements. Variants on each vector were analyzed through the evaluation of the flexibility needs based on indicators on several time scales. In particular, the interest of coordination between vectors was confirmed: it also avoids massive investments
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Friberg, Kent. "Essays on Wage and Price Formation in Sweden". Doctoral thesis, Stockholm : Department of Economics, Stockholm University, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-304.
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Ekman, Sara. "Price Vector Recalculation Optimization". Thesis, Umeå universitet, Institutionen för fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136534.
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DAVO', Federica. "Optimization and Forecasting Models for Electricity Market and Renewable Energies". Doctoral thesis, Università degli studi di Bergamo, 2017. http://hdl.handle.net/10446/77349.
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This thesis presents different optimization and forecasting models, with the focus on energy markets and renewable energy sources. The analysis approach is related to models for wind and solar power forecasts and those for electricity prices forecasts. The first study explores a Principal Component Analysis in combination with two post-processing techniques for the prediction of wind power and of solar irradiance produced over two large areas. The Principal Component Analysis is applied to reduce the datasets dimension. A Neural Network and an Analog Ensemble post-processing are then applied on the PCA output to obtain the final forecasts. The study shows that combining PCA with these post-processing techniques leads to better results when compared to the implementation without the PCA reduction.
The second work explores two different techniques for the prediction of the Italian day-ahead electricity market prices. The predicted Italian prices are the zonal prices and the uniform purchase price (Prezzo Unico Nazionale or PUN). The study is conducted using hourly data of the prices to be predicted and a large set of variables used as predictors (i.e. historical prices, forecast load, wind and solar power forecasts, expected plenty or shortage of hydroelectric production, net transfer capacity available at the interconnections and the gas prices). A Neural Network and a Support Vector Regression are applied on the different predictors to obtain the final forecasts. Different predictors’ combinations are analysed to find the best forecast. The results show that the best configuration is obtained using all the predictors together and applying the Neural Network to find the forecasted prices.
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Brockwell, Erik. "State and industrial actions to influence consumer behavior". Doctoral thesis, Umeå universitet, Nationalekonomi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-93334.
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This thesis consists of an introductory part and three papers. Paper [I] examines how taxes affect consumption of commodities that are detrimental to health and the environment. Specifically, this paper examines if a tax increase leads to a significantly larger change in consumption than a producer price change, which is referred to as the signaling effect from taxation. The analysis uses aggregated cross-sectional time series data and information on major legislation introductions in Sweden, Denmark and the United Kingdom from 1970 to 2009. We find the main result to be that the signaling effect is significant for “Electricity” in Sweden and Denmark and significant for “Electricity” and “Petrol” in the United Kingdom. Paper [II] examines how sin taxation changes long-term consumer behavior regarding commodities which are deemed harmful for both health and the environment. These include tobacco, alcoholic beverages, sugar and confectionary, household energy, and motor fuel. Specifically, we examine the signaling effect from taxation which is seen if a tax increase leads to a significantly larger change in consumption than a producer price change. The empirical analysis is conducted by a US panel data study, during the period 1988-2012 for the four US census regions, using the Almost Ideal Demand System (AIDS). We find the main result to be that the signaling effect from taxation is significant for tobacco as well as for electricity and motor fuel. Paper [III] examines state and industry responses on consumption of cigarettes and petroleum in the United States from 1998-2012. Upon facing consumption choices, the consumer faces two competing sets of messages, one from the government and another from the industry. The objective of the state is to steer consumption in the right direction due to the harmful effects from consumption and asymmetric information among consumers. This is done mainly via taxation and state media expenditures. The industry, on the other hand, seeks to incentivize the public to ignore or reject state research and signals as well as maximizing net economic returns. This is mainly done via industry media and lobbying expenditures. We find that the main results indicate, for cigarettes, industrial media and lobbying expenditure is statistically significant on consumption. For petroleum, we find that producer prices, state media expenditure, and industrial lobbying expenditure are statistically significant on consumption.
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Mendes, Giovanna Miranda. "Efeitos dos ganhos de produtividade total dos fatores da agropecuária sobre os preços agrícolas no Brasil: 1970-2006". Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11132/tde-17112015-084759/.
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A agropecuária brasileira tem crescido nas últimas décadas e os ganhos de produtividade tem sido importante neste bom desempenho do setor. O presente trabalho tem dois objetivos principais. O primeiro deles foi mensurar o crescimento desta produtividade total dos fatores na agropecuária brasileira estadual, decompondo o crescimento da PTF em progresso tecnológico e eficiência técnica. O segundo objetivo foi analisar o efeito do crescimento da PTF da agropecuária brasileira sobre os preços agrícolas, no Brasil, de 1970 a 2006. O crescimento desta produtividade foi mensurado a partir dos insumos terra, trabalho e capital na função de produção translog sob orientação do produto, a partir do método de Fronteira Estocástica de Produção e do índice de produtividade de Malmquist. Para avaliar o efeito do crescimento da PTF sobre os preços agrícolas foi construído o índice de preços agrícolas utilizando-se o Índice de preços de Laspeyres para estimar o vetor autoregressivo em painel (panel- VAR), acrescentando as variáveis produtividade total dos fatores (PTF), salário rural, financiamento agrícola e renda per capita domiciliar. Além disso, foi aplicado o teste de causalidade, no sentido de Granger, e estimada a função impulso resposta. A base de dados utilizada foi, obtida do Censo Agropecuário, a nível estadual, para os anos de 1970, 1975, 1980, 1985, 1995 e 2006. Os resultados indicaram que a taxa de crescimento da PTF foi crescente no Brasil e nos estados, sendo que, na maior parte das vezes, é explicada pelo progresso tecnológico, positivo e crescente para todos os estados. A eficiência técnica variou ao longo dos anos, apresentado taxas de crescimento médias positivas para a maioria dos estados. Em média, os estados estiveram situados abaixo da fronteira de produção da agropecuária brasileira. São Paulo foi o estado com maior nível de eficiência técnica. Embora a taxa de crescimento médio anual tenha sido positiva ao longo do período analisado, a eficiência reduziu para todos os estados analisados em 2006. Da análise dos efeitos do crescimento da PTF sobre os preços agrícolas, a PTF tem causalidade, no sentido de Granger, sobre os preços agrícolas. Na função impulso resposta, o choque inicial na variável PTF reduziu os preços nos primeiros anos. Assim, o crescimento da PTF do setor agropecuário contribuiu para o aumento da oferta de produtos, reduzindo os preços agrícolas. A maior disponibilidade de alimentos e, com a redução dos preços dos alimentos, os consumidores, principalmente os de renda mais baixa puderam ter maior acesso aos alimentos.The Brazilian agriculture has grown in recent decades and productivity gains have been important in this good performance of the sector. This work had two main objectives. The first one was measure the growth of this total factor productivity in agriculture by the Brazilian\'s states, decomposing TFP growth by technological progress, technical efficiency and economies of scale. The second objective was to analyze the effect of TFP growth of Brazilian agriculture on agricultural prices. The growth in productivity was measured from the inputs like labor, gross and capital in the translog production function, from the Stochastic Frontier Analysis and of the outputoriented Malmquist productivity index. To analyze the effect of TFP growth on agricultural prices was constructed an index of agricultural prices through the Laspeyres price index to estimate the vector autoregressive panel (panel-VAR) and establish the relationships between TFP, rural wages, agricultural finance and income per capita household. The Granger causality test and the impulse response function were used to the data panel. The database used obtained from the Agricultural Census, at the state level for the years 1970, 1975, 1980, 1985, 1995 and 2006. The results showed that the growth rate of TFP has been growing in Brazil and in the states, and technological progress explained most of the growth being positive and growing for all states. Technical efficiency varied over the years, presented positive average growth rates for most states. The states were located below the production frontier of Brazilian agriculture and São Paulo was the state with the highest level of technical efficiency. Although the average annual growth rate has been increasing over the period analyzed, the efficiency decreased to all state analyzed in 2006. The results also showed that TFP growth has causality in the sense of Granger, on agricultural prices. In the impulse response function, the initial shock in TFP decreased prices in the early years. Thus, TFP growth of the agricultural sector contributed to the increased supply of agricultural products, reducing agricultural prices. The greater availability of food and with reducing food prices, consumers, especially those from lower income might had greater access to food.
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Day, Robert Warren. "Expressing preferences with price-vector agents in combinatorial auctions". College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1849.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.Thesis research directed by: Applied Mathematics and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Molin, Simon. "House Price Dynamics in Sweden : Vector error-correction model". Thesis, Umeå universitet, Nationalekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172367.
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Movements in house prices can have effects on individuals, financial markets, and the whole economy. After the rapid increase in house prices worldwide since the mid-1990s and after the financial crisis in 2008, many studies have investigated house price dynamics. Furthermore, real house prices in Sweden have increased by more than 200 % since the mid-1990s up until today. This study takes a closer look at the fundamental determinants of house prices to investigate both the long- and short-run dynamics of Swedish house prices. The method of use includes a vector error-correction model, which exposes both long- and short-run dynamics of house prices. The long-run results show that Swedish house prices are currently not overvalued. Furthermore, in the short-run, the results suggest that house prices adjust to their equilibrium level with 7,9 % in each quarter.
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Villarinho, Alvaro Teixeira. "Previsibilidade de retorno das ações no mercado brasileiro, através da aplicação de modelo de valor presente com retornos esperados constantes num contexto de expectativas racionais". reponame:Repositório Institucional do FGV, 2005. http://hdl.handle.net/10438/290.
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Made available in DSpace on 2008-05-13T13:47:40Z (GMT). No. of bitstreams: 1
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Previous issue date: 2005-04-12Using Brazilian financial data for some shares traded in the Brazilian Stock Market (BOVESPA) we test the expectation hypothesis of present value models discounted by a constant factor. This model relates the price of a stock to its expected dividends. To perform econometric testing we use mainly the jointly restriction through Wald Test in a Vector Autoregression framework, as well as alternative testing procedures. The empirical results partially support the present value model discounted by a constant factor to predict prices for stock through its expected dividends.
Através de dados financeiros de ações negociadas na Bolsa de Valores de São Paulo, testa-se a validade do modelo de valor presente (MVP) com retornos esperados constantes ao longo do tempo (Campbell & Schiller, 1987). Esse modelo relaciona o preço de uma ação ao seu esperado fluxo de dividendos trazido a valor presente a uma taxa de desconto constante ao longo do tempo. Por trás desse modelo está a hipótese de expectativas racionais, bem como a hipótese de previsibilidade de preço futuro do ativo, através da inserção dos dividendos esperados no período seguinte. Nesse trabalho é realizada uma análise multivariada num arcabouço de séries temporais, utilizando a técnica de Auto-Regressões Vetoriais. Os resultados empíricos apresentados, embora inconclusivos, permitem apenas admitir que não é possível rejeitar completamente a hipótese de expectativas racionais para os ativos brasileiros.
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Taramasco, Ollivier. "Modélisation non paramétrique du comportement des cours boursiers". Grenoble 1, 1993. http://www.theses.fr/1993GRE10038.
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Ce travail a pour objet d'eclairer le comportement des cours d'actions et indices de valeurs. Le processus des rentabilites boursieres est d'abord examine a l'aide des outils statistiques classiques puis des techniques empruntees a la theorie du chaos. L'etude se focalise ensuite sur la nature de la dependance entre les rentabilites consecutives. Celle-ci est decrite par l'analyse factorielle des correspondances et par l'analyse canonique entre deux vecteurs aleatoires. Les resultats montrent qu'il existe, outre la correlation lineaire, une dependance d'allure parabolique, que ces deux formes de dependance restent relativement stables dans le temps et qu'elles ont une interpretation financiere possible. Ces constats conduisent alors a une representation de type arch non parametrique du processus des rentabilites dont les moments conditionnels peuvent etre exprimes a l'aide des fonctions canoniques
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Jeon, Kyung-Seong. "An examination of stock market properties : vector autoregression approach /". free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841304.
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Petrov, Krassimir M. "Forecasting the dairy price complex : an application of Bayesian Vector autoregression modelling /". The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193272066522.
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Wang, Jiayue. "Essays on oil price shocks and financial markets". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6412.
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This thesis is composed of three chapters, which can be read independently. The first chapter investigates how oil price volatility affects the investment decisions for a panel of Japanese firms. The model is estimated using a system generalized method of moments technique for panel data. The results are presented to show that there is a U-shaped relationship between oil price volatility and Japanese firm investment. The results from subsamples of these data indicate that this U-shaped relationship is more significant for oil-intensive firms and small firms. The second chapter aims to examine the underlying causes of changes in real oil price and their transmission mechanisms in the Japanese stock market. I decompose real oil price changes into three components; namely, oil supply shock, aggregate demand shock and oil-specific demand shock, and then estimate the dynamic effects of each component on stock returns using a structural vector autoregressive (SVAR) model. I find that the responses of aggregate Japanese real stock returns differ substantially with different underlying causes of oil price changes. In the long run, oil shocks account for 43% of the variation in the Japanese real stock returns. The response of Japanese real stock returns to oil price shocks can be attributed in its entirety to the cash flow variations. The third chapter tests the robustness of SVAR and investigates the impact of oil price shocks on the different U.S. stock indices. I find that the responses of real stock returns of alternate stock indices differ substantially depending on the underlying causes of the oil price increase. However, the magnitude and length of the effect depends on the firm size. The response of U.S. stock returns to oil price shocks can be attributed to the variations of expected discount rates and expected cash flows.
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Westerich, Filho Valdemir Angelo. "Transmissão de preços no mercado de milho brasileiro : um estudo das regiões sul e centro-oeste". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/98166.
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O mercado do milho no Brasil tem demonstrado algumas mudanças nos últimos anos aumentando sua importância no agronegócio. Por esse motivo, tem sido maior a necessidade de estudo de suas características. O objetivo da presente dissertação consiste em verificar como se dá a transmissão de preços entre os mercados regionais dessa commodity no Brasil a nível de produtor, com foco nos estados da região Sul e Centro-Oeste, devido à sua importância para a produção nacional. Além disso, também foi buscado analisar como os preços dos estados analisados reagem ao preço cotado na bolsa de valores para saber qual sua relação com o mercado externo. O método de pesquisa utilizado foi: teste de raiz unitária; teste de cointegração; vetor de correção de erro; teste de causalidade de Granger e teste de impulso-resposta. Os resultados do teste de cointegração indicam que há transmissão de preços entre todos os estados analisados, bem como os estados respondem a oscilações de preços do mercado externo a longo prazo. O fato de existir cointegração entre os estados é condição suficiente para se afirmar que existe relação linear de equilíbrio para a qual o sistema converge, validando os pressupostos da Lei do Preço Único e a integração. Todos os estados apresentaram resposta significativa a mudanças de preços no estado de Santa Catarina pelo vetor de correção de erro (VEC), mostrando que esse estado tem forte influência na formação de preços dos estados das duas regiões analisadas. No curto prazo foi observado que os estados de Mato Grosso e Rio Grande do Sul não recebem influência direta das oscilações de preços dos outros mercados, enquanto os estados de Paraná, Santa Catarina e Goiás parecem ser interdependentes a curto prazo, pois apresentam relativa correlação. Além disso, a função impulso resposta demonstra também que um impulso nos preços do estado de Santa Catarina gera resposta significativa nos preços dos outros estados de forma geral, e um impulso no preço do estado de Goiás também gera uma reação forte no preço do estado do Mato Grosso.The corn market in Brazil has shown some changes in recent years increasing its importance in agribusiness. For this reason has increased the need for more studies related to this market’s characteristics . The objective of this dissertation is to check how is the price transmission between regional markets in Brazil at producer level for this commodity, focusing on states of the South and Midwest of the country, because of its importance to the national production. Furthermore, it was also sought to analyze how the prices of the analyzed states react to the price quoted on the stock market, looking for understanding how is its relationship with the external market. The research method used was: the unit root test , cointegration test , vector error correction; Granger causality test and impulse response test. The result of the cointegration test indicates that there is price transmission between all the states analyzed as well as states respond to price fluctuations on the stock market in the long run . The existence of cointegration between the states is sufficient to say that there is a linear equilibrium relationship to which converges the sistem, validating the assumptions of the Law of One Price and the integration condition. All states showed significant responses to price changes in the state of Santa Catarina by the vector error correction ( VEC ) , showing that this state has a strong influence on the pricing of the states on the two regions. In the short term it was observed that the states of Mato Grosso and Rio Grande do Sul receive no direct influence from the prices of other markets, while the states of Paraná, Santa Catarina and Goiás seem to be interdependent in the short term because they present a correlation. As well, the impulse response function also shows that a surge in prices in the state of Santa Catarina generates a significant response in prices of other states in general, and a boost in the price of Goias also generates a strong reaction in the price of Mato Grosso.
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Perez, Tomas Rene. "Oil Price and the Stock Market: A Structural VAR Model Identified with an External Instrument". Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1595877677072786.
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Hansen, Patrik y Sandi Vojcic. "Stock Market Forecasting Using SVM With Price and News Analysis". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293854.
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Many machine learning approaches have been usedfor financial forecasting to estimate stock trends in the future. Thefocus of this project is to implement a Support Vector Machinewith price and news analysis for companies within the technologysector as inputs to predict if the price of the stock is going torise or fall in the coming days and to observe the impact on theprediction accuracy by adding news to the technical analysis.The price analysis is compiled of 9 different financial indicatorsused to indicate changes in price, and the news analysis uses thebag-of-words method to rate headlines as positive or negative.There is a slight indication of the news improving the resultsif the validation data is randomly sampled the testing accuracyincreases. When testing on the last fifth of the data of eachcompany, there was only a small difference in the results whenadding news to the calculation and such no clear correlation canbe seen. The resulting program has a mean and median testingaccuracy over 50 % for almost all settings. Complications whenusing SVM for the purpose of price forecasting in the stockmarket is also discussed.Många metoder för maskininlärning har använts i syfte av finansiell prognos för att uppskatta aktie trender i framtiden. Fokus för detta projekt är att implementera en Support Vector Machine med pris- och nyhetsanalys för företag inom teknologisektorn som inmatning för att förutsäga om priset på aktien kommer att öka eller minska under de kommande dagarna och för att observera påverkan på förutsägelsens noggrannhet av att lägga till nyheter till den tekniska analysen. Prisanalysen består av 9 olika finansiella indikatorer som används för att indikera prisändringar, och nyhetsanalysen använder metoden bag-of-word för att betygsätta rubriker som positiva eller negativa. Det finns en liten indikation på att nyheterna förbättrar resultat där om valideringsdata stickas ur slumpmässigt provningsnoggrannheten ökar. När man testade den sista femte delen av inmatningsdatan från varje företag, fanns det bara en liten skillnad i resultaten när nyheterna beräknades vilket leder till att en tydlig korrelation kan inte ses. Det resulterande programmet har en genomsnittlig och median test nogrannhet över 50 % för nästan alla inställningar. Komplikationer när SVM används för prisprognoser på aktiemarknaden diskuteras också.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
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Antonakakis, Nikolaos, Ioannis Chatziantoniou y George Filis. "Dynamic Spillovers of Oil Price Shocks and Economic Policy Uncertainty". WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4082/1/wp166.pdf.
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This study examines the dynamic relationship between changes in oil prices and the economic policy uncertainty index for a sample of both net oil-exporting and net oil-importing countries over the period 1997:01-2013:06. To achieve that, we extend the Diebold and Yilmaz (2009, 2012)
dynamic spillover index using structural decomposition. The results reveal that economic policy uncertainty (oil price shocks) responds negatively to aggregate demand oil price shocks (economic policy uncertainty shocks). Furthermore, during the Great Recession of 2007-2009, total spillovers
increase considerably, reaching unprecedented heights. Moreover, in net terms, economic policy uncertainty becomes the dominant transmitter of shocks between 1997 and 2009, while in the post-2009 period there is a significant role for supply-side and oil specific demand shocks, as net transmitters of spillover effects. These results are important for policy makers, as well as, investors
interested in the oil market. (authors' abstract)Series: Department of Economics Working Paper Series
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Wang, Ruolin. "Essays on the information flow between equity and credit markets: Before, during and after the financial crisis". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200152/1/Ruolin_Wang_Thesis.pdf.
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This thesis examines the information flow between equity, credit default swap (CDS) and bond markets between 2003 and 2017 using firm-level data for developed and emerging countries. The findings suggest that the information flow between financial markets is dependent on the market condition. The research demonstrates that the relationship between equity and credit markets has been restructured since the global financial crisis with more rapid adjustment of CDS market to equity market returns. The strength of interaction between equity and credit markets is found to be related to the creditworthiness of the concerned firms.
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Fox, David. "Dynamic demand modelling and pricing decision support systems for petroleum". Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/dynamic-demand-modelling-and-pricing-decision-support-systems-for-petroleum(2ce6efed-a7eb-4d10-b325-4d4590ba57ad).html.
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Pricing decision support systems have been developed in order to help retail companies optimise the prices they set when selling their goods and services. This research aims to enhance the essential forecasting and optimisation techniques that underlie these systems. This is first done by applying the method of Dynamic Linear Models in order to provide sales forecasts of a higher accuracy compared with current methods. Secondly, the method of Support Vector Regression is used to forecast future competitor prices. This new technique aims to produce forecasts of greater accuracy compared with the assumption currentlyused in pricing decision support systems that each competitor's price will simply remain unchanged. Thirdly, when competitor prices aren't forecasted, a new pricing optimisation technique is presented which provides the highest guaranteed profit. Existing pricing decision support systems optimise price assuming that competitor prices will remain unchanged but this optimisation can't be trusted since competitor prices are never actually forecasted. Finally, when competitor prices are forecasted, an exhaustive search of a game-tree is presented as a new way to optimise a retailer's price. This optimisation incorporates future competitor price moves, something which is vital when analysing the success of a pricing strategy but is absent from current pricing decision support systems. Each approach is applied to the forecasting and optimisation of daily retail vehicle fuel pricing using real commercial data, showing the improved results in each case.
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Akra, Abraham. "Modelling the Four-Party Billing Payment Scheme: The Case of BPAY". Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9515.
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My thesis involves developing a detailed understanding of the dynamics of a two-sided four-party Bill Payment market utilising proprietary data obtained from BPAY. The first chapter of my thesis analyses the market from a macroeconomic perspective, whilst the second and third chapter takes a microeconomic approach. The first chapter models the demand for merchant acceptance and consumer usage of a four - party payment scheme in the Bill Payment market. Within a cointegrating framework, demand equations are estimated using vector error correction models using proprietary data between March 2003 and December 2010. Results illustrate the importance of network effects in determining consumer usage and merchant demand. Additionally, price elasticities suggest the market for payments in Australia is competitive. The second chapter exploits a unique data set that details the demographics and transactions of individuals over a 30 month observational window. Survival analysis techniques are employed to quantify the risks of individuals leaving the platform. Results suggest support for the Hayashi and Klee (2003) finding in the Bill Payment market with individuals having a credit card less likely to leave the BPAY platform at any point in time. The motivation of the third chapter was to establish whether a link existed between the usage of the BPAY platform by consumers with the adoption of prior payment method technologies, given by credit card holding. Unlike Hayashi and Klee (2003), there is an added layer of complexity as credit cards are another payment instrument individuals can use for bill payments. An ordered generalised ordered logit model is estimated to determine the influence of credit card holding on frequency of usage. The results lend support to the Hayashi and Klee (2003) hypothesis that the adoption of a technology based payment instrument is influenced by the usage of prior technologies.
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Akram, Muhammad. "Do crude oil price changes affect economic growth of India, Pakistan and Bangladesh? : A multivariate time series analysis". Thesis, Högskolan Dalarna, Nationalekonomi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-10723.
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This paper analyzes empirically the effect of crude oil price change on the economic growth of Indian-Subcontinent (India, Pakistan and Bangladesh). We use a multivariate Vector Autoregressive analysis followed by Wald Granger causality test and Impulse Response Function (IRF). Wald Granger causality test results show that only India’s economic growth is significantly affected when crude oil price decreases. Impact of crude oil price increase is insignificantly negative for all three countries during first year. In second year, impact is negative but smaller than first year for India, negative but larger for Bangladesh and positive for Pakistan.
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Hu, Linlin. "A novel hybrid technique for short-term electricity price forecasting in deregulated electricity markets". Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4498.
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Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program. In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007. Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid technique considerably improves the electricity price forecasting.
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Mvita, Mpinda Freddy. "The impact of dividend policy on shareholders' wealth : evidence from the Vector Error Correction Model". Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/31010.
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Dividend policy is widely researched in financial management, but determining whether it affects the market price per share is difficult. There has been much published on the subject, which presented theories such as the Modigliani, Miller, Gordon, Lintner, Walter and Richardson propositions and the relevance and irrelevance theories. However, little research has been done on the impact of dividend policy on shareholders’ wealth while considering the short- and long-run effects. The Vector Error Correction Model (VECM) was used to describe the short-run and long-run dynamics or the adjustment of the cointegrated variables towards their equilibrium values in South Africa. This study attempts to explain the effect of dividend policy on the market price per share. A sample of 46 companies listed on the Johannesburg Securities Exchange (JSE) was selected for the period 1995-2010. Three variables were used, namely the market price per share, the dividend per share and the earnings per share. The market price per share was used as a proxy in measuring shareholders’ wealth and the dividend per share was used as a proxy in measuring the dividend policy. Fixed and random effects models were applied to panel data to determine the relation between dividend policy and market price per share. The fixed effects method was used to control the stable characteristics of the companies over a fixed period. The random effects model was applied when the companies’ characteristics differed. Results for both models indicated that dividend yield is positively related to market price per share, while earnings per share do not have a significant impact on the market price per share. To test the strength of the long-run relationship, the VECM was applied. The coefficient for dividend per share in the co-integrating equation was positive, while the coefficient for earnings per share was negative. This confirms previous research findings. The results suggest that there is a long-run relationship between dividend per share and market price per share. The Granger causality test indicates there is bi-directional Granger causality between market price per share and dividend per share in South Africa. Therefore dividend policy does have a significant long-run impact on the share price and therefore provides a signal about the company’s financial success.Dissertation (MCom)--University of Pretoria, 2012.
Financial Management
Unrestricted
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Voronin, Yegor A. "Investigation of initiation of reverse transcription in retroviruses using vectors with two primer-binding sites". Morgantown, W. Va. : [West Virginia University Libraries], 2003. http://etd.wvu.edu/templates/showETD.cfm?recnum=3136.
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MICHELE, ANELLI. "The price discovery process of the sovereign and bank credit risk in a high-volatility framework". Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1095780.
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This research study presents three distinct and separate (but logically linked) essays focused on the price discovery
process of credit risk.
The aim of the first essay (working paper n. 1) is to analyse the long lasting dynamic relationship between
the credit default swap (CDS) premia and the government bond spreads (GBS), by focusing particularly on the
sovereign credit risk, in order to evaluate the lead-lag markets in the price discovery process against the backdrop of
a deep crisis. The focus of this study concerns the case of Italy, one of the major countries subject to international
speculative attacks by market operators because of the weak GDP growth, the high public debt and the political
fragility, for the period 2007-2017.
In the second essay (working paper n. 2) the analysis is extended to the lead-lag relationship between the PIIGS - excp Greece
10-year CDS premia and the respective government bond spreads (GBS) series by employing daily data, from January
2007 to October 2017, provided by Bloomberg. The time interval has been considered as whole in the first part
of the analysis, without distinguishing the different stages of development of the recent crisis, while in the second
part I focused on the sovereign debt crisis impact on the lead-lag relationship.
In the third essay (working paper n. 3) It has been evaluated, as a preliminary stage of the investigation, the lead-lag
relationship between the Italian sovereign 5Y CDS premia and the Italian banks proxy 5Y CDS premia series
by employing daily data, for the interval Q2 2007- Q3 2018 (provided by Bloomberg). The latter series was built
up by using the Intesa San Paolo 5y CDS contracts and the Unicredit 5y CDS contracts series weighted by the
respective market capitalization. In the second part of the study, I extended the determinants inspired by the classic Merton (1974 ) model in order to investigate on the drivers of Italian bank credit risk during the most volatile phases of this decade: the financial crisis (August 2007- October 2009 ), the sovereign debt crisis (October 2009 - July 2012 ) and the anti-establishment
Government/pre-Italy’s budget update (March 2018 - September 2018 ) period.
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Lloyd, Amanda Lian. "Cloning, characterisation and sequencing of promoters of Helicobacter pylori 4187E". University of Western Australia. Microbiology Discipline Group, 2005. http://theses.library.uwa.edu.au/adt-WU2005.0112.
Texto completoResumen
Published information on the structure and regulation of H. pylori promoters is limited. The work presented in this thesis describes the cloning and characterisation of promoter regions from a clinical isolate of H. pylori, and the development of an alternative, non-radioactive method for verifying the location of transcriptional start sites of bacterial promoters. H. pylori 4187E promoters were randomly cloned into the promoter-trap vector pKK232-8 in Escherichia coli DH5α using two sets of restriction enzymes. Vector pKK232-8 contains a promoterless chloramphenicol acetyltransferase (CAT) gene. Seventy-four promoter-containing clones were isolated from selective media based on their resistance to chloramphenicol. The strength of each promoter was analysed qualitatively, using chloramphenicol minimum inhibitory concentrations, and quantitatively, using CAT assays following exposure of the clones to pH 4 and pH 7. Selected promoter fragments were subcloned into the GFP reporter vector pFPV25, containing a promoterless gfp gene. The subclones were exposed to buffered LB broth at pH 4, 5, 6, 7 and 8, for varying lengths of time, to study acid-induced regulation of gene expression. Subclones were examined qualitatively, using visual examination of GFP fluorescence and fluorescence microscopy, and quantitatively, using flow cytometry following acid shock. DNA sequences were determined for 61 of the 74 H. pylori promoters, and sequence alignments with the published H. pylori strains (26695 and J99) were performed. The transcriptional start site of 27 H. pylori promoter fragments was experimentally mapped using a fluorescence-based primer extension protocol developed by our group. Potential -35 and -10 sequences were identified for each promoter, and a new consensus sequence for H. pylori promoters was proposed based upon these results. This study has considerably expanded knowledge of H. pylori promoter sequences and transcriptional start sites based on those which also function in E. coli. It has also revealed several H. pylori promoters which appear to respond to acid stress
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Li, Qi. "Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model". Thesis, Portland State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10979352.
Texto completoResumen
In this study, a Prediction Accuracy Based Hill Climbing Feature Selection Algorithm (AHCFS) is created and compared with an Error Rate Based Sequential Feature Selection Algorithm (ERFS) which is an existing Matlab algorithm. The goal of the study is to create a new piece of an algorithm that has potential to outperform the existing Matlab sequential feature selection algorithm in predicting the movement of S&P 500 (
GSPC) prices under certain circumstances. The twoalgorithms are tested based on historical data of
GSPC, and SupportVector Machine (SVM) is employed by both as the classifier. A prediction without feature selection algorithm implemented is carried out and used as a baseline for comparison between the two algorithms. The prediction horizon set in this study for both algorithms varies from one to 60 days. The study results show that AHCFS reaches higher prediction accuracy than ERFS in the majority of the cases.
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Bentivoglio, Deborah. "Analisi della Sostenibilità Socio-economica ed Ambientale dei Biocarburanti nel Contesto Europeo e Brasiliano". Doctoral thesis, Università Politecnica delle Marche, 2015. http://hdl.handle.net/11566/243058.
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Negli ultimi anni la produzione e il consumo dei biocarburanti sono aumentati a livello globale. Tale incremento è stato incentivato soprattutto grazie al supporto e agli incentivi adottati dai vari Paesi promotori finalizzati alla riduzione delle emissioni di gas serra e alla sicurezza energetica.
Ad oggi il mercato globale è dominato dall’etanolo (79%) e dal biodiesel (21%). In particolare, l'Unione Europea domina il mercato del biodiesel mentre il Brasile è il più grande produttore ed esportatore mondiale di zucchero, nonché il più grande produttore al mondo e consumatore di etanolo da canna da zucchero per il settore del trasporto.
Tuttavia, recentemente, sono state sollevate incertezze relative alla reale sostenibilità dei biocombustibili, sia dal punto di vista ambientale che socio-economico. Accanto ai dubbi relativi alle loro performance in termini di riduzione di emissioni di GHG rispetto ai combustibili fossili, sono emerse critiche relative all’impatto che tali biocarburanti hanno sull’utilizzo del suolo (ILUC), sulla perdita di biodiversità e sull’aumento dei prezzi dei mercati delle commodities agricole usate come materie prime per la loro produzione.
A tal proposito, la ricerca condotta si inserisce a pieno all’interno di tale dibattito e si pone l’obiettivo di valutare la sostenibilità ambientale e le implicazioni socio-economiche della produzione di biocombustibili nel contesto europeo che in quello brasiliano, soffermandosi in particolare sull’analisi econometrica dei prezzi delle commodities agricole utilizzate come materie prime di origine.
Al fine di valutare la sostenibilità ambientale è stata applicata un’analisi multicriteriale grazie all’applicazione di uno studio meta-analitico volto ad identificare i criteri ambientali per la stima dell’impatto della filiera biodiesel. Lo scopo è stata quello di individuare l’alternativa migliore tra le diverse tipologie di biomassa utilizzata per la produzione del biodiesel in Europa (olio di colza, di girasole, di palma e di soia).
La valutazione della sostenibilità socio-economica è stata invece effettuata tramite l’analisi delle serie storiche e la successiva applicazione di un modello a correzione di errore (VECM) al fine di verificare se i prezzi delle commodities agricole, (olio di colza nel contesto europeo e zucchero nel contesto brasiliano) siano influenzati dai prezzi dei biocombustibili di riferimento, quali biodiesel ed etanolo, e viceversa. Tale relazione è stata studiata anche in relazione al combustibile fossile di riferimento, ossia la benzina (o gasolina) per il Brasile e il diesel per l’Europa.
Sintetizzando i risultati, per quanto riguarda l’impatto ambientale, dall’applicazione multicriteria, si evince che nel caso in cui si considerino prioritari i criteri di sostenibilità ambientale, la soluzione migliore a livello europeo sarebbe quella del biodiesel da olio di girasole. Tale soluzione risulterebbe molto interessante per l’Europa e in particolar modo per l’Italia. Tuttavia la filiera del girasole risulta inaccessibile dal punto di vista economico aziendale. Va da sé che nell’ipotesi in cui si consideri prioritario il criterio economico, la produzione di biodiesel da olio di palma, di derivazione estera, risulterebbe la soluzione migliore, creando squilibri dal punto di vista ambientale e in particolare di cambiamento di uso del suolo (ILUC).
I risultati dello studio socio-economico tramite l’analisi delle serie storiche indica, ceteris paribus, che i prezzi dei biocarburanti sono influenzati soprattutto dai prezzi delle materie prime anche se emerge con chiarezza che le variazioni dei prezzi dei biocarburanti non influenzano in misura sostanziale i prezzi degli alimenti.The last decade has seen a rapid increase in the production and consumption of biofuels at global level. This development has been especially stimulated by policy as a means to promote energy security and to reduce the emissions of greenhouse gases. Nowadays, world biofuel markets are dominated by ethanol (79%) and biodiesel (21%). In particular, Biodiesel market is dominated by the European Union, at the same time Brazil is the world’s biggest sugar producer and exporter, as well as the world’s largest producer and consumer of sugarcane ethanol as a transportation fuel. However, several authors have recently raised concerns about the environmental benefits and social-economic implications of biofuels production such as underlying uncertainties over the life cycle emissions of greenhouse gas emissions (GHG), possible deforestation for feedstock production, degradation of soil (ILUC) and air quality, increased water consumption, possible loss of biodiversity, possible competition with food production, and other potential social imbalances. The aim of this work is to investigate the impacts of biofuels on the environmental aspects and food prices in the European and Brazilian context. In order to assess the environmental performance this work aims to identify environmental criteria in order to evaluate the impact of the entire biodiesel production chain thought an exploratory meta-analysis of international scientific research. The information from the meta-analysis enabled the design and implementation of a multi-criteria methodology to define the best alternative between different agricultural raw materials used for biodiesel production (rapeseed oil, sunflower oil and palm oil) according to the principles of sustainability expressed by current EU policy. In order to explore relationship between food commodity and biofuel prices a time series models is used. In particular, both the impact of EU biodiesel prices on diesel and rapeseed oil prices and Brazilian ethanol prices on sugar and gasoline prices are investigated using a vector error corrections model (VECM). The multi-criteria shows that from an environmental perspective the best solution at European level is biodiesel production based on sunflower oil. This solution would be very interesting for Europe and especially for Italy. However, the sunflower chain is not feasible from the economic point of view, especially for the biodiesel company. In fact, if the economic aspect is priority, the palm oil from Malaysia is the best alternative. Finally, the results from the time series analysis suggest that biofuels prices are mainly affected by feedstock prices, but there is no strong evidence that changes in biofuels prices affect food prices, for the market and time period considered.
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Caley, Jeffrey Allan. "A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers". PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/2001.
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In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification rate, modified Sharpe ratio and profit gained over the test period is used to evaluate each strategy. The findings showed nearest neighbor classification using genetic algorithm input feature reduction produced the best results, achieving higher profits than buy-and-hold for a majority of the companies.
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Raksong, Saranya. "The stability of money demand and monetary transmission mechanism in Thailand". Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/612.
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The major objective of this thesis is to investigate whether there exists a stable long run and short run equilibrium relationship between real money balances (M1 or M2) and their determinants in Thailand. A cointegration analysis and the Vector Error Correction Model (VECM) are conducted on quarterly data over two data set periods, 1980Q1 to 2007Q1 and 1993Q1 to 2007Q1. The results indicate that there exists a long run equilibrium relationship between real money demand (both M1 and M2) and its determinants: real income, price level, exchange rates, and external interest rates.The thesis also used the Vector Autoregression model (VAR) to test the monetary transmission mechanism in Thailand in three different channels of monetary policy: the interest rate channel, the credit channel, and the exchange rate channel. The results find that a change in the M1 money demand has more effect on economic growth while a change in M2 has a stronger effect on the price level. In addition, the results also show that the M1 money demand is responsive to the transmission mechanism in all channels tested in the thesis.
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Ouyang, Quinglin. "Time to purchase your ownhouse : The resistance of housing investments againstmacroeconomic shocks". Thesis, KTH, Fastigheter och byggande, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277084.
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Housing is both a durable good and an investment vehicle, which makes it importantin people’s daily life aswell as for a nation’s economy. This thesis innovatively applies the Sharpe ratio on evaluating the performance of the US residentialhousing market within the time period from 2005:Q1 to 2019:Q3, andinvestigates how this performance would react upon macroeconomic shocks,including sudden changes in GDP growth rate and personal income growthrate, by establishing a vector auto-regression model with the lag order of four.The main results are that: (1)in the long run, direct residential investments are not significantly more profitable than treasury bills but not disappointing compared to the market portfolio of Dow Jones Industrial Average; (2)the performance of residential investments seem to slightly and positively co-move withGDP and personal income growth rate; (3)the long-term impacts that sudden GDP and personal income growths have on the performance seem inconspicuous and tend to mitigate within about three years and (4) limited evidence supports the hypothesis that current housing market performance can help predictfuture GDP growth rate. Based on housing’s two purpose of consumption andinvestment and the empirical results showing that direct investments on residentialproperties have similar risk-adjusted return level to short-term treasurybills, I suggest that financially feasible households purchase their own houseinstead of renting for a long time, and that speculative investors avoid puttingmoney in residential properties unless they have access to inside information.Bostäder kan betraktas både som en hållbar vara och som ett investeringsinstrument.De är essentiella för människors vardag och har en viktig roll förett lands ekonomi. Denna avhandling använder innovativt Sharpe-förhållandet för att utvärdera hur den amerikanska bostadsmarknaden presterade under perioden2005: kvartal 1 till 2019: kvartal 3. Den försöker även undersöka om denna prestation påverkas av makroekonomiska chocker inklusive plötsligaförändringar i BNP-tillväxttakt och personliga inkomsttillväxthastighet. Detta görs genom att upprätta en vektor autoregression modell med en fördröjningsordningför fyra. De viktigaste resultaten är att: (1) på långsikt är direktabostadsinvesteringar inte betydligt mer lönsamma än statsskuldväxlar dock är det hellre inte en besvikelse jämfört med en marknadsportföljen av Dow JonesIndustrial Average; (2) Prestationen av bostadsinvesteringar verkar vara svagt och samverkar positivit både med BNP och tillväxttakten för personinkomst.(3) De långsiktiga effekterna av plötsliga tillväxter av BNP och personliga inkomster har på utvecklingen verkar vara vaga och tenderar att mildra inomcirka tre år och (4) begränsade bevis stöder hypotesen om att nuvarande bostadsmarknadsresultat kan bidra till att förutsäga framtida BNP-tillväxttakten.Baserat på bostädernas två syften inom konsumtion och investeringar, visar deempiriska resultaten att direkta investeringar i bostadsfastigheter har en liknande riskjusterad avkastningsnivå som kortfristiga statsskuldväxla. Därför föreslår jag att ekonomisk stabila hushåll borde köpa ett eget hus istället för att hyraunder en lång tid, och att spekulativa investerare borde undvika att satsa pengar inom bostadsfastigheter såvida de inte har tillgång till insider-information.
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Figueiredo, Marta Isabel Fragoso Peralta de. "Análise da modelação dos preços do mercado de habitação na área de Lisboa entre 1972 e 2011". Master's thesis, Instituto Superior de Economia e Gestão, 2012. http://hdl.handle.net/10400.5/10453.
Texto completoResumen
Mestrado em Gestão e Avaliação ImobiliáriaO propósito deste estudo é investigarmos empiricamente os determinantes que influenciaram a formação do preço da habitação em Portugal. A evolução dos preços da habitação em Portugal reveste-se de grande importância para os profissionais do sector. Conhecer, estudar e analisar a evolução deste mercado ao longo dos últimos anos permite aos profissionais tomar decisões fundamentadas em análises profundas e cuidadas sobre quais foram os determinantes que influenciaram a procura e a oferta que por sua vez determinaram os preços. Pretendemos conhecer o comportamento do mercado imobiliário e qual a sua relação de causalidade com as variáveis macroeconómicas que influenciam o desenvolvimento económico do país. Usamos modelos Vetoriais Autorregressivos (VAR) para identificar os principais fatores macroeconómicos que influenciaram a formação dos preços ao longo dos últimos vinte e seis anos. Para a análise utilizamos dados trimestrais, referentes ao período de 1985 a 2011 e observámos as variáveis: Índice de Preços da Habitação (IPH), Produto Interno Bruto, Rendimento Disponível dos particulares, Taxa de desemprego e Taxa de Juro Implícitas no crédito hipotecário. Os resultados empíricos obtidos evidenciaram que existe uma relação de causalidade entre os preços da habitação e o PIB e a Taxa de Juro aplicada ao crédito hipotecário. O teste de causalidade à Granger revelou não existir relação de causalidade entre o Índice de Preços da Habitação e as variáveis Rendimento disponível dos particulares e Taxa de desemprego.
The purpose of this study is to empirically investigate the determinants that influenced the formation of the housing price in Portugal. The evolution of housing prices in Portugal is of great importance to industry professionals. Knowing, studying and analyzing the evolution of this market over the last few years allows professionals to make informed decisions based on in-depth and careful analysis about what were the determinants that influenced the demand and supply which in turn determined the prices. We intend to understand the behavior of the housing market and what is its causal relationship with macroeconomic variables that influence the economic development of the country. We use vector autoregressive models (VAR) to identify the main macroeconomic factors that influenced the formation of prices over the past twenty-six years. For the analysis we use quarterly data for the period 1985 to 2011 and the observed variables: Housing Price Index (HPI), GDP, disposable income of households, unemployment rate and interest rate implied in mortgages. The empirical results showed that there is a causal relationship between housing prices and GDP and the interest rate applied to the mortgage. The Granger Causality Test revealed no causal relationship between the Housing Price Index and the variables Households' disposable income and unemployment rate.
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Wei, Honghong. "Essays in energy, environmental and health economics". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/202081/1/Honghong_Wei_Thesis.pdf.
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This thesis comprises of three essays that explore issues related to energy economics, environmental economics and health economics. The first essay discusses the monetary policy response to commodity price shocks in the U.S., Canada and Mexico in the presence of NAFTA. The second essay investigates the relationship between carbon emissions and urbanization in different sectors across economies. The third essay studies the theoretical and empirical relationship between inequality aversion and public health expenditures. The common thread running through these essays is the use of cross-country analysis for deriving evidence-informed insights of relevance to policies in these domains.
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"The estimation of vector multiplicative error model on contaminated data and its applications in forecasting volatilities". 2013. http://library.cuhk.edu.hk/record=b5549844.
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这篇论文研究了当假设的数据分布与实际不符时估计多维乘积误差模型参数的方法，和该模型在预测领域的应用。论文的第一部分讨论了两种在以前的文献中被用来估计该模型的估计方法：最大似然估计法和广义矩估计法。并在对数据做了不同的干扰后比较了这两种方法。比较结果显示这两种方法都易受偏离值的影响。因此论文的第二部分提出了一种新的估计方法：权重经验似然估计法。在模拟实验和使用包含了当前经济危机间断数据的标准普尔指数的实际实验中，对比最大似然估计法和广义矩估计法，权重经验似然函数显示出了对偏离值有更好的抗性。论文的第三部分进一步研究了多维乘积误差模型在预测中的应用。并且这一部分还提出了实波动性的一种新的分解方式。分解得到的两个新的变量可以被多维乘积误差模型所模拟。通过比较标准普尔指数和纳斯达克指数的预测结果，比起以前用来估计实波动性的三种模型，多维乘积向量模型和新的分解方式显示出了更强的预测能力。This thesis studies the estimations of vector Multiplicative Error Model (MEM) under different kinds of model mismatches and its application in forecasting. In the first part of the thesis, two estimation methods, Maximum Likelihood (ML) method and Generalized Method of Moments (GMM), which have previously been used on vector MEM, are compared through different situations of data contaminations. From the comparison results it is found that both ML and GMM estimators are suspected to outliers in data. Therefore in the second part of the thesis a novel estimator is proposed: Weighted Empirical Likelihood (WEL) estimator. It is shown to be more robust than ML and GMM estimators in simulations, and also in forecasting realized volatility and bipower volatility of S&P 500 stock index including the current financial crisis period. The forecast ability of vector MEM is further addressed in the third part of the thesis, where an alternative decomposition of realized volatility is proposed, and vector MEM is used to model and forecast the two components of realized volatility. From the realized volatility forecasts of S&P 500, NASDAQ and Dow Jones, this decomposition together with vector MEM are illustrated to have superior performances over three competing models which have been applied on forecasting realized volatility before.
Detailed summary in vernacular field only.
Ding, Hao.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2013.
Includes bibliographical references (leaves 203-213).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstracts also in Chinese.
Abstract --- p.i
Acknowledgement --- p.iv
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Outline of the thesis --- p.5
Chapter 1.2 --- Conclusion --- p.7
Chapter 2 --- Background study --- p.9
Chapter 2.1 --- Multiplicative Error Model --- p.9
Chapter 2.1.1 --- Introduction --- p.9
Chapter 2.1.2 --- Developments of MEM --- p.12
Chapter 2.1.3 --- Vector MEM --- p.17
Chapter 2.2 --- Two functions for multivariate analysis --- p.25
Chapter 2.2.1 --- Copula function --- p.25
Chapter 2.2.2 --- Depth function --- p.32
Chapter 3 --- Two Estimators for Vector MEM --- p.39
Chapter 3.1 --- Two Stage Maximum Likelihood --- p.40
Chapter 3.1.1 --- Introduction --- p.41
Chapter 3.1.2 --- Simulation of two stage ML --- p.44
Chapter 3.2 --- Maximum Likelihood estimator --- p.48
Chapter 3.2.1 --- Derivatives of score function --- p.50
Chapter 3.3 --- GMM estimator --- p.57
Chapter 3.4 --- Comparing ML and GMM through simulations --- p.60
Chapter 3.4.1 --- Generation of clean data --- p.61
Chapter 3.4.2 --- Data contamination --- p.62
Chapter 3.4.3 --- Optimization --- p.64
Chapter 3.4.4 --- Resutls on clean data --- p.65
Chapter 3.4.5 --- Results on contaminated data --- p.66
Chapter 3.5 --- conclusion --- p.69
Chapter 4 --- Weighted Empirical Likelihood Estimator --- p.77
Chapter 4.1 --- Introduction --- p.78
Chapter 4.2 --- Vector multiplicative error model and two estimation methods --- p.83
Chapter 4.3 --- Weighted Empirical Likelihood --- p.88
Chapter 4.3.1 --- Inner optimization --- p.93
Chapter 4.3.2 --- Calculation of weights --- p.97
Chapter 4.4 --- Simulation study on outliers --- p.101
Chapter 4.4.1 --- Clean data --- p.103
Chapter 4.4.2 --- Outliers --- p.105
Chapter 4.4.3 --- Simulation results --- p.108
Chapter 4.5 --- Computations of high dimension vector MEM --- p.111
Chapter 4.5.1 --- The influences of dimension on ML --- p.111
Chapter 4.5.2 --- The influences of dimension on GMM --- p.113
Chapter 4.5.3 --- The influences of dimension on WEL --- p.115
Chapter 4.5.4 --- Simulation --- p.116
Chapter 4.6 --- Compare weighted empirical likelihood and empirical likelihood --- p.118
Chapter 4.7 --- Empirical example --- p.121
Chapter 4.7.1 --- Model --- p.123
Chapter 4.7.2 --- Forecast comparison criteria --- p.125
Chapter 4.7.3 --- Results --- p.126
Chapter 4.8 --- Conclusions --- p.127
Chapter 5 --- Forecast RV by Vector MEM --- p.142
Chapter 5.1 --- Introduction --- p.143
Chapter 5.2 --- Multiplicative jump and vector MEM --- p.148
Chapter 5.2.1 --- Multiplicative jump --- p.148
Chapter 5.2.2 --- Vector MEM for jump and continuous components --- p.153
Chapter 5.3 --- Empirical analysis --- p.156
Chapter 5.3.1 --- Data summary --- p.157
Chapter 5.3.2 --- Models --- p.160
Chapter 5.3.3 --- Forecast comparison criteria --- p.164
Chapter 5.3.4 --- Before-crisis period --- p.166
Chapter 5.3.5 --- Crisis period --- p.172
Chapter 5.3.6 --- Comparing M-jump and log M-jump --- p.176
Chapter 5.3.7 --- Conclusion on empirical analysis --- p.183
Chapter 5.4 --- Conclusion --- p.185
Chapter 6 --- Conclusion and future Work --- p.198
Bibliography --- p.203
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Wei-Tsung, Wang y 王偉聰. "An Analysis of the Fresh Tuna Sashimi Auction Prices in Taiwan and the Import Prices in Japan-An Application of Vector ARMA Model". Thesis, 1999. http://ndltd.ncl.edu.tw/handle/19122010481780803600.
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Hung-Ming, Chen y 陳宏銘. "An Analysis of U.S.A. Seafood Market Wholesale Prices -- An Application of the Vector ARMA Model". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/69628220356499003158.
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碩士國立海洋大學
應用經濟研究所
91
After Taiwan joined the World Trade Organization in January 2002, a significant tariff reduction had lowered the import cost of the fishery products and had increased the import demand significantly. Salmon, cod, shrimp and lobster are top four major import seafood groups in Taiwan and their demand will be more sensitive to the international seafood prices after trade liberalization. In this study, we try to collect the international seafood prices in order to measure the competitiveness of the domestic seafood supply in Taiwan. Since U.S.A. is the second largest import source country to Taiwan and it is also the second largest seafood importing country in the world, it is interesting to find that salmon, cod, shrimp and lobster are also the four major seafood imports in U.S.A. Even though Japan is the largest importing country of fishery products in the world, its import value has declined by 4.02% annually since 1995 to 2001. During the same period of time, the import value of seafood in U.S.A. has increased by approximately 6.42% annually and accounts for 17.24% of the total value of seafood imports in the world in 2001. Therefore, the seafood import market in U.S.A. will be an ideal market to provide us the information of international price of salmon, cod, shrimp and lobster in the world. This study collects the weekly wholesale prices of 15 product items of salmon, cod, shrimp and lobster started from the first week of January 1994 to the fourth week of December 2002 from Urner Barry Publications, Inc. For each of the four major imported product groups, the average F.O.B. prices before tariff in Taiwan are all lower than the average wholesale prices in U.S.A. during the above period. It is possible that the importers in Taiwan tend to import low-price and low-quality seafood or they may tend to under estimate their import value to avoid paying tariff to the government. Furthermore, this study used the non-nested causality test to investigate the lead-lag relationship between various wholesale price variables. The vector ARMA model is used to estimate the relationship for each of the four groups of fishery products in U.S.A. Results show that the wholesale prices are all correlated in one or two weeks lag, which suggest that the seafood price in the following week may be tracked by the price information in the past two weeks. The major findings in this study are as follows, (1) the import price of Chilean fresh fillets salmon (SACA) is a leading price index to two other salmon products; (2) the price of U.S.A. domestic dressed frozen halibut (CHDT) is a price leading index to three other cod products; (3) Among four imported sources of the shrimp products, the prices of Central and South American shrimp (SHCS), U.S.A. domestic brown shrimp (SHGB), and U.S.A. domestic white shrimp (SHGW) exhibit feedback relationships among each other; (4) Among all three kinds of imported lobster products, the prices of West Australian lobster tails (LOTA), Brazil lobster tails (LOTB) and Caribbean lobster tails (LOTC) exhibit feedback relationships with each other. The RMSPE of the in-sample price forecasts of cod, shrimp and lobster are all smaller than 3%, which indicates that the VARMA model performed very well in forecasting. This study recommends the government to establish a price-monitoring system to ensure the competitiveness of the domestic fishery products in Taiwan and to avoid a loss of the tariff revenue. Results of this study could provide a way to master the trend of international wholesale seafood prices and to provide the government a double-checking basis of the seafood import prices in Taiwan.