Dissertations / Theses on the topic 'Price of time'

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Esta tese tem como objetivo a comparação entre procedimentos para dessazonalização de séries temporais. As metodologias usadas serão a de Modelos Estruturais Clássicos e Bayesianos e a metodologia padrão de dessazonalização X11 ARIMA. Os dados utilizados são as 35 séries reais de índice de preços ao consumidor - IPC para a Região Metropolitana do Rio de Janeiro, fornecidas pelo Instituto Brasileiro de Geografia e Pesquisa - IBGE, no período de janeiro de 1991 até dezembro de 1997. Os pacotes computacionais utilizados no decorrer do trabalho são FORECAST PRO (X11 ARIMA0, STAMP (Estruturais Clássicos) e BATS (Estruturais Bayesianos). Além disso, foram também utilizadas séries simuladas com sazonalidade, para melhor analisar os resultados desejados.
The aim of this thesis is a comparisson study among three existing procedures for seasonal adjustment of time series, namely: the tradicional X11 ARIMA and those based on the structural model formulation, i.e., the classical approach of A. Harvey and the Bayesian counterpart of Harrison and Stevens. The data used are 25 real time series of Consumer Price Index for Metropolitan area from Rio de Janeiro from 1991 to 1997, supllied by the Instituto Brasileiro de Geografia e Estatística - IBGE. The computacional packages used during the thesis were SPSS and FORECAST PRO (X11 ARIMA), STAMP (structural classical approach) and BATS (structural bayesian approach). Also, simulated seasonal data were to provide a better understanding of the procedures.

American-style options are contracts traded on financial markets. These are derivatives of some underlying security or securities that in contrast to European-style options allow their holders to exercise at any point before the contracts expire. However, this advantage aggravates the mathematical formulation of an option's value considerably, explaining why essentially no exact closed-formed pricing formulas exist. Numerous price approximation methods are although available, but their possible areas of application as well as performance, measured by speed and accuracy, differ. A clearing house offering real-time solutions are especially dependent on fast pricing methods to calculate portfolio risk, where accuracy is assumed to be an important factor to guarantee low-discrepancy estimations. Conversely, overly biased risk estimates may worsen a clearing house's ability to manage great losses, endangering the stability of a financial market it operates. The purpose of this project was to find methods with optimal performance and to investigate if price approximation errors induce biases in option portfolios' risk estimates. Regarding performance, a Quasi-Monte Carlo least squares method was found suitable for at least one type of exotic option. Yet none of the analyzed closed-form approximation methods could be assessed as optimal because of their varying strengths, where although the Binomial Tree model performed most consistently. Moreover, the answer to which method entails the best risk estimates remains inconclusive since only one set of parameters was used due to heavy calculations. A larger study involving a broader range of parameter values must therefore be performed in order to answer this reliably. However, it was revealed that large errors in risk estimates are avoided only if American standard options are priced with any of the analyzed methods and not when a faster European formula is employed. Furthermore, those that were analyzed can yield rather different risk estimates, implying that relatively large errors may arise if an inadequate method is applied.

The guiding theme of this thesis is the empirical analysis of recent food price behavior. It is composed of three applied studies that address the impacts of energy prices on both food price levels and volatility, as well as the impact of public information release on futures markets of major agricultural commodities. Non-structural time series econometric techniques are applied for such purpose. In the first chapter, the impact of the Spanish biodiesel industry on agricultural feedstock prices is investigated. Both price level and volatility interactions are evaluated. Three relevant prices are considered: the international crude oil price, the Spanish biodiesel blend price and the Spanish sunflower oil price. Weekly Prices are observed from November 2006 to October 2010, yielding a total of 205 observations. Blended biodiesel, sunflower and crude oil prices are found to be interrelated in the long-run. This parity is preserved by the biodiesel industry in order to be in equilibrium. The impact of biodiesel on sunflower oil price levels is found to be very modest, which is reasonable given the small size of the Spanish biodiesel industry. Volatility spillovers between sunflower and biodiesel markets are found to be significant. Evidence of asymmetries in price volatility patterns is also found, with price declines causing more price instability than price increases. Asymmetries can be triggered by the availability of alternative feedstocks in the market, as well as by the unwillingness of biodiesel producers to increase food prices when feedstocks become more expensive. In the second chapter, the impact of the EU biodiesel market on agricultural feedstock prices is analyzed. The study comprises the period between 06/11/2008 to 14/06/2012, and is based on 189 weekly prices. Cointegration analysis suggests that the three prices have a long-run equilibrium relationship that is preserved by the pure biodiesel price. Biodiesel prices are not found to have an effect on rapeseed oil prices. Volatility of pure biodiesel price is affected by its own past volatility and past pure biodiesel and rapeseed market shocks. Also, evidence is found of asymmetries in price volatility, with negative market shocks having a greater impact than positive ones. While pure biodiesel prices cannot affect rapeseed oil price-levels, they can bring instability to these prices. Inventory building and the euro-dollar exchange rate are found to be relevant risk management instruments that can be used to mitigate the biodiesel and rapeseed oil price volatilities. In the third chapter, the impact of public information in the form of USDA-NASS crop production reports on daily corn and soybeans futures prices is evaluated. The study period is between 1970 to 2004, with a total of 700 observations. Results show that USDA-NASS crop production reports significantly affect futures price levels. Report releases at the beginning and at the end of the harvest season are usually the ones exerting a stronger impact. Report releases are not however found to have an effect on price volatility, which suggests gradual price-level changes as a response to published information. Cross-market effects of news are also found to be significant.

While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; we have also shown that this application greatly reduces the time a reseller would need to spend on trading activities, since the bulk of market research is now done automatically with the help of the learned model.

The production of cattle in the United State is a very large business. Production begins at the cow-calf level, where a calf is born and raised to a specific weight. This weight is the weaning weight and averages between 300-600 pounds. The calf is then typically shipped to a feedlot, where it is fed a high corn ration which increases the weight of animal quickly and cost effectively to reach a sufficient slaughter weight. Cattle production takes place primarily in 5 different geographical locations which include the North Central, Southeast, Northern Plains, Southern Plains, and West regions. Due to the relationships between fed cattle prices, feeder cattle prices and feed costs, lighter weight feeder cattle typically sell for a higher price per pound than heavier weight feeder cattle. This decrease in price per pound for heavier feeders is often referred to as a feeder cattle price slide. This study is to determine how price slides have reacted over time and space due to the relative changes in fed and feeder cattle prices and the cost of feed. Weekly data was obtained from the Livestock Marketing Information Center (LMIC) on the auction price for feeder cattle at different weights for both steers and heifers. Weekly data on the futures price of live cattle and corn were also obtained from the LMIC. To determine if feeder cattle price slides had changed over time, regression analysis was used to evaluate the relationship between feeder cattle prices at varying weights with the price of fed cattle and the price of corn. Two different time periods were used for the same location: the first period was from 1992 to 1996 and the second period was from 2005 to 2015. Price slides were also examined across space. There were five different geographical locations analyzed: Oklahoma, Nebraska, Georgia, Kansas, and Montana. Each region was regressed individually and then compared. Prices slides were calculated as the difference in the regressed feeder cattle price at each weight. A combination of the time and space was used to evaluate changes in the same model. Results from the regression models returned feeder cattle prices at varying weights for steers and heifers and price slides were calculated from those estimated prices. It was found that price slides are not constant over time and that price slides are geographically specific. In the third objective, it is shown that time and space are both factors in determining price slides for feeder cattle. The implications of this study are to help cattle producers be more aware of market conditions specific to changes in feeding cost. It is also to make aware that price slides are not constant over time and space and therefore, must be reevaluated on a consistent basis.

Intuitively, quoting dynamic lead time and price to customers based on real-time system state provides more efficient capacity utilization and increases revenue compared with quoting static lead time and price. However, dynamic quotation may require higher operational costs for the firm and it is often inconvenient to customers. This study aims to compare dynamic and static lead time and price quotations under fixed capacity and different potential demand rates. We hypothesize that there exists a potential demand rate under which the additional costs of dynamic quotation and the additional profit from dynamic quotation are equal. Thus static quotation may yield better performance under certain potential demand rates. We use an M/M/1 queuing model to model the supply system of a firm and formulate profit maximization models in an average reward criterion under both static and dynamic lead time and price quotations. Numerical analyses are presented to illustrate performances of both static and dynamic lead time and price quotation and thus find the threshold potential demand rate. Besides, we study performance of two different kinds of dynamic lead time quotation and find that when firm can decide their price, performance of dynamic lead time quotation is good enough and when firm cannot decide their price, the dynamic lead time quotation is good only when lead time sensitive factor is small and potential demand rate is big.

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Accurate price forecasting of agricultural commodities is very important for raising income of the farmers as well as for avoiding market risk. However, due to biological nature of production of agricultural commodities, forecasting of their prices become a challenging task. These challenges become more severe when structural breaks are present in the observed agricultural price series due to factors like major changes in technology, sudden changes in economic policy, etc. In this study, an effort has been made to account for the structural break along with the other complex patterns like non-stationarity, non-linearity, long memory and cointegration present in the agricultural price series.. Generally, single model may not be able to capture all complex patterns present in the data series concurrently. Therefore, to capture various complex patterns in the data along with structural break, hybridization of statistical model that account for structural break with artificial intelligence model has been done. Accordingly, for agricultural price volatility forecasting in the presence of structural break, a hybrid model based on Markov-Switching GARCH (MS-GARCH) and Extreme Learning Machine (ELM) is proposed. The performance of the proposed hybrid MS-GARCH–ELM model is evaluated on the weekly potato price of Delhi market, monthly international Groundnut oil and Palm oil price series, and it is found that the proposed model outperformed its counterparts. Empirical results of agricultural price series that contain long memory property with structural break show that the forecasting performance of the proposed hybrid model based on ARFIMA with dummy variable combined with ELM is better than the individual model. Further, the effect of structural break in the co-integrated system has also been evaluated. Accordingly, spatial market integration among major Potato markets in India are investigated in the absence and presence of structural break. The overall co-integration test results indicated that selected potato markets in India are well integrated and have long-run price association across them.
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Earlier studies examining house pricing have mainly focused on the secondary market and have often overlooked the primary market and newly produced housing units. This paper studies the pricing strategies in the primary housing market, as that segment differs from the secondary market. By using data from one newly produced housing project, we are able to exclude a number of project-specific factors, as they are nearly identical for all observations. This allows us to focus on factors that are directly observable and require very little assessment or evaluation in our estimations of list prices, selling prices and selling times. The empirical results exhibit a close relationship between list- and selling prices, but a few factors differ significantly between the two. Such differences could indicate a misinterpretation of the market by the seller. The time-on-market model shows that a number of factors affect selling times as well. The results indicate a relationship between "mispriced" factors and their impact on the selling times, where "over-priced" factors seem to prolong the time-on-market and "under-priced" factors seem to shorten the time-on-market. By dividing the units into different price ranges, it becomes clear that high-priced housing is more difficult to price and take longer to sell. This relationship is strengthened by a degree-of-overpricing variable, which exhibits a positive sign in the time-on-market model. The effect is the strongest in low-priced units and not significant for higher-priced units. Other factors that affect pricing strategies require a broader discussion. Analogies from similar consumer good markets indicate that pricing strategies are dependent on the types of customers in the target groups as well as the stage in the project life-cycle.

The pulp and paper industry has a significant role in Europe’s economy and society, and its significance is still growing. The pulp market and the customers’ requirements are highly affected by the pulp market prices and the requested kind of pulp, i.e., Elementary Chlorine Free (ECF) or Total Chlorine Free (TCF). There is a need to predict different market aspects, where the market price is one, to gain a better understanding of a business situation. Understanding market dynamics can support organizations to optimize their processes and production. Forecasting future pulp prices has not recently been done, but it would help businesses to make decisions that are more informed about where to sell their product. The studies existing about the pulp industry and forecast of market prices were completed over 20 years ago, and the market has changed since then in terms of, e.g., demand and production volume. There is a research gap within the pulp industry from a market price perspective. The pulp market is similar to, e.g., the energy industry in some aspects, and time-series analysis has been used to forecast electricity prices to support decision making by electricity producers and retailers. Autoregressive Integrated Moving Average (ARIMA) is one time-series analysis method that is used when data are collected with a constant frequency and when the average is not constant. Holt-Winters model is a well-known and simple time-series analysis. In this thesis, time-series analysis is used to predict the weekly market price for pulp the three upcoming months, with the research question “With what accuracy can time-series analysis be used to forecast the European PIX price on pulp on a week-ahead basis?”. The research method in this thesis is a case study where data are collected through the data collection method documents. First, articles are studied to gain understanding within the problem area leading to the use of the artefact time-series analyses and a case study. Then, historical data are collected from the organization FOEX Fastmarkets, where a new market price of pulp has been released every Tuesday since September 1996. The dataset has a total of 1200 data points. After data cleaning, it is merged to 1196 data points that are used for the analysis. To evaluate the results from the time-series analysis models ARIMA and Holt-Winter, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used. The software RStudio is used for programming. The results shows that the ARIMA model provides the most accurate results. The mean value for MAE is 16,59 for ARIMA and 44,61 for Holt-Winters. The mean value for MAPE is 1,99% for ARIMA and 5,37% for Holt-Winters.

Share price returns on different time scales can be well modeled by a superstatistical dynamics. We provide an investigation which type of superstatistics is most suitable to properly describe share price dynamics on various time scales. It is shown that while chi-square-superstatistics works well on a time scale of days, on a much smaller time scale of minutes the price changes are better described by lognormal superstatistics. The system dynamics thus exhibits a transition from lognormal to chi-square-superstatistics as a function of time scale. We discuss a more general model interpolating between both statistics which fits the observed data very well. We also present results on correlation functions of the extracted superstatistical volatility parameter, which exhibits exponential decay for returns on large time scales, whereas for returns on small time scales there are long-range correlations and power-law decays. We also apply the symbolic dynamics technique from dynamical system theory to analyse the coarse-grained evolution of share price returns. A nontrivial spectrum of Renyi entropies is found. We study how the spectrum depends on the time scale of returns, the sector of stocks considered, as well as the number of symbols used for the symbolic description. Overall our analysis confirms that in the symbol space transition probabilities of observed share price returns depend on the entire history of previous symbols, thus emphasizing the need for a model of share price evolution based on non-Markovian stochastic processes. Our method allows for quantitative comparisons of entirely different complex systems, for example the statistics of coarse-grained share price returns using 4 symbols can be compared with that of other complex systems.

This study surveyed the impacts of the expanding Real-Time Price (RTP) scheme on residential electricity consumption when households shift from fixed price to hourly spot prices. A unique and detailed data of electricity consumption had been used. The data are based on working days of winter and summer for Swedish detached houses from 2005 to 2008. Solar power is valuable energy with low emission, which can be achieved by installing solar panels on the household’s roof. Also, it reduces the system cost and provides quick access to energy for customers. The preliminary photovoltaic production evaluated through HARMONIE Numerical Weather Prediction data. Four types of households are analyzed based on various patterns of prices, elasticities, and the share of households in RTP program with and without solar panels. The results of this study demonstrate that putting more residential customers on RTP contracts will shift load, decrease electricity demand, total capacity, and increase economic welfare. The simulations show that the social welfare gained from increasing the share of customers on RTP are notable. Also, the estimated cost saving indicates that the effect of shifting from a flat rate to RTP is positive. Furthermore, the effect of small-scale solar production on electricity consumption is considered. The combination of RTP with solar energy would lead to a significant decrease in electricity consumption during off-peak periods in winter and both peak and off-peak load in summer.

The main thrust of time-series forecasting models in recent years has gone in the direction of pattern-based learning, in which the input variable for the models is a vector of past observations of the variable itself to predict. The most used models based on this traditional pattern-based approach are the autoregressive integrated moving average model (ARIMA) and long short-term memory neural networks (LSTM). The main drawback of the mentioned approaches is their inability to react when the underlying relationships in the data change resulting in a degrading predictive performance of the models. In order to solve this problem, various studies seek to incorporate external factors into the models treating the system as a black box using a machine learning approach which generates complex models that require a large amount of data for their training and have little interpretability. In this thesis, three different algorithms have been proposed to incorporate additional external factors into these pattern-based models, obtaining a good balance between forecast accuracy and model interpretability. After applying these algorithms in a study case of Ethereum price time-series forecasting, it is shown that the prediction error can be efficiently reduced by taking into account these influential external factors compared to traditional approaches while maintaining full interpretability of the model.
Huvudinstrumentet för prognosmodeller för tidsserier de senaste åren har gått i riktning mot mönsterbaserat lärande, där ingångsvariablerna för modellerna är en vektor av tidigare observationer för variabeln som ska förutsägas. De mest använda modellerna baserade på detta traditionella mönsterbaserade tillvägagångssätt är auto-regressiv integrerad rörlig genomsnittsmodell (ARIMA) och långa kortvariga neurala nätverk (LSTM). Den huvudsakliga nackdelen med de nämnda tillvägagångssätten är att de inte kan reagera när de underliggande förhållandena i data förändras vilket resulterar i en försämrad prediktiv prestanda för modellerna. För att lösa detta problem försöker olika studier integrera externa faktorer i modellerna som behandlar systemet som en svart låda med en maskininlärningsmetod som genererar komplexa modeller som kräver en stor mängd data för deras inlärning och har liten förklarande kapacitet. I denna uppsatsen har tre olika algoritmer föreslagits för att införliva ytterligare externa faktorer i dessa mönsterbaserade modeller, vilket ger en bra balans mellan prognosnoggrannhet och modelltolkbarhet. Efter att ha använt dessa algoritmer i ett studiefall av prognoser för Ethereums pristidsserier, visas det att förutsägelsefelet effektivt kan minskas genom att ta hänsyn till dessa inflytelserika externa faktorer jämfört med traditionella tillvägagångssätt med bibehållen full tolkbarhet av modellen.

The aim of the thesis is to broaden the research in the field of housing economics using the statistical tool of meta-analysis. The thesis examines the relationship between the selling price of a house and the time the house spends at the housing market. Although the research investigating this relation is of a wide comprehension, the results arising from various primary studies differ a lot. The goal of the thesis is to explain the source of this heterogeneity and determine the factors causing this variation. According to the results, it can be concluded that the effect size is influenced mainly by number of observations, modelling technique and specification of the model. Median income or location are other factors also determining the size of estimated coefficients. Also publication bias has been investigated although its presence is not confirmed in this thesis.

In this report six different models for predicting the electrical spot price on the Nordic power exchange, Nord Pool, are developed and compared. They are evaluated against the already existing model as well as the naive test, which is a forecast where the last week’s observations are used as a prognosis for the coming week. The models developed are constructed so that the models for different time resolutions are combined to create a full model. Harmonic regression with a linear trend are used to identify a yearly trend while SARIMAX/SARIMA time series models are used on a daily and hourly basis to reveal dependencies in the data.   The model with the best prediction performance is shown to be a SARIMAX model with temperature as exogenous variable on a daily resolution, together with a SARIMA model on an hourly resolution. With an average MAPE of 12.69% and a MAPE2 of 6.90% it has the smallest prediction error out of all of the competing models when doing one week forecasts on the whole year 2009.

Value shares are notorious for remaining stagnant for extended periods of time, forcing value investors to remain locked in their investments often for excessive periods. This research study applied the price acceleration momentum indicator of Bird and Casavecchia (2007) on a value portfolio with the objective of improving the timing of value share acquisitions.A time series study was conducted, taking into account the top 160 JSE shares over the period 1 January 1985 to 31 August 2012. A price acceleration momentum indicator was applied to enhance a value portfolio formed on the basis of book-tomarket ratio, dividend yield and EBITDA/EV. Cumulative average abnormal returns (CAAR) were used to compare portfolio results statistically.A substantial contribution is made to the literature by proving that a value-only portfolio can be significantly enhanced by the combination of price acceleration momentum. Results indicated an increase in CAAR from 199.83% to 321.29%. Risk-adjusted returns (Sharpe ratio) were also improved without the detriment of increased share price volatility (standard deviation). This research study further contributes to the literature by proving that a price acceleration momentum indicator adds no additional value over a value portfolio combined with ordinary price momentum.
Dissertation (MBA)--University of Pretoria, 2012.
Gordon Institute of Business Science (GIBS)
unrestricted

The inference of causal relationships between stock markets constitutes a major research topic in the field of financial time series analysis. A successful reconstruction of the underlying causality structure represents an important step towards the overall aim of improving stock market price forecasting. In this thesis, I utilise the concept of Granger-causality for the identification of causal relationships. One major challenge is the possible presence of latent variables that affect the measured components. An instantaneous interaction can arise in the inferred network of stock market relationships either spuriously due to the existence of a latent confounder or truly as a result of hidden agreements between market players. I investigate the implications of such a scenario; proposing a new method that allows for the first time to distinguish between instantaneous interactions caused by a latent confounder and those resulting from hidden agreements. Another challenge is the implicit assumption of existing Granger-causality analysis techniques that the interactions have a time delay either equal to or a multiple of the observed data. Two sub-cases of this scenario are discussed: (i) when the collected data is simultaneously recorded, (ii) when the collected data is non-simultaneously recorded. I propose two modified approaches based on time series shifting that provide correct inferences of the complete causal interaction structure. To investigate the performance of the above mentioned method improvements in predictions, I present a modified version of the building block model for modelling stock prices allowing causality structure between stock prices to be modelled. To assess the forecasting ability of the extended model, I compare predictions resulting from network reconstruction methods developed throughout this thesis to predictions made based on standard correlation analysis using stock market data. The findings show that predictions based on the developed methods provide more accurate forecasts than predictions resulting from correlation analysis.

We present a dynamic asset pricing model based on a heterogenous class of traders. These traders are homogenous in the sense that they are fundamentalists who base their investment decisions on an exogenoulsy given fundamental value. They are heterogenous in the sense that each trader is working with a different frequency of the underlying price data. As a result we have a system of interacting investors who together influence the market price. We derive a system that characterizes out-of-equilibrium dynamics of prices in this market which is structurally equivalent to the Nosé-Hoover thermostat equation in non-equilibrium thermodynamics. We explore the time series properties of these prices and find that they exhibit fat tails of returns distributions, volatility clustering and power laws. (author's abstract)
Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

This thesis presents a comparison for modeling and forecasting Chinese futures market of copper and aluminum with single time series and multivariate time series under linear restrictions. For single time series, data transformation for stationary purpose has been tested and performed before ARIMA model was built. For multivariate time series, co-integration rank test has been performed and included before VECM model was built. Based on selected models, the forecasting shows multivariate time series analysis has a better result than single time series, which indicates utilizing the relationships among the series can improve the accuracy of time series forecasting.

Price trends on the Swedish housing market has risen sharply in recent decades and is at the moment up to the highest price level ever. The sharp price movements have opened up for discussion about a possible housing bubble. To prevent this the Riksbank can change the repo rate, which in turn aects the lenders' lending rates. Finansinspektionen introduced in autumn 2010, a mortgage cap which means that the house will be mortgaged to a maximum of 85 percent of its market value. The purpose of this was to cool the housing market and prevent the unsustainable development of household debt. The purpose of this study is to examine in particular the repo rates and the mortgage caps eect on house prices in Sweden. Although other variables that aect supply and demand in the housing market from a macroeconomic perspective will be included in the model, such as GDP, unemployment and the nancial crisis of 2008. This study has been done by using a quantitative analysis, consisting of time series analysis. The results conrm all the investigated variables expected impact on house prices. As for the repo rate and the mortgage cap the results showed that these have a negative eect on house prices in Sweden.
Prisutvecklingen pa den Svenska bostadsmarknaden har stigit kraftigt under de senaste decennierna och ar just nu uppe i den hogsta prisnivan nagonsin. Den kraftiga prisutvecklingen har oppnat for diskussion om en eventuell bostadsbubbla. For att motverka detta kan Riksbanken andra reporantan som i sin tur paverkar kreditgivarnas utlaningsranta. Finansinspektionen inforde under hosten 2010 ett bolanetak som innebar att bostaden hogst ska belanas till 85 procent av marknadsvardet. Detta for att kyla bostadsmarknaden och motverka den ohallbara utvecklingen av hushallens skuldsattning. Syftet med denna studie ar att framforallt undersoka reporantans och bolanetakets eekt pa smahuspriser i Sverige. Aven andra variabler som paverkar utbudet och efterfragan pa bostadsmarknaden ur ett makroekonomiskt perspektiv kommer att inga i modellen, till exempel BNP, arbetsloshet och nanskrisen 2008. Detta genomfors med hjalp av en kvantitativ analys, bestaende av tidsserieanalys. Resultatet bekraftar alla undersokta variablers vantade eekter pa smahuspriser. Vad galler reporantan och bolanetaket sa visade resultatet pa att dessa har negativ eekt pa smahuspriser i Sverige.

Many methods have been used to forecast real-time electricity prices in various regions around the world. The problem is difficult because of market volatility affected by a wide range of exogenous variables from weather to natural gas prices, and accurate price forecasting could help both suppliers and consumers plan effective business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with large numbers of inputs, and convolutional neural networks conspicuously lacking from current literature in this field, convolutional neural networks are used for this time series forecasting problem and show some promising results. This document fulfills both MSEE Master's Thesis and BSCPE Senior Project requirements.

Wheat is an extremely important agricultural commodity, due to its crucial role in everyday nutrition, food security, and in terms of incomes of a large body of farmers worldwide. This study examines the dynamics of wheat prices in Turkey in a framework that allows for regime switching. Due to their simplicity, threshold autoregressive (TAR) models are used to capture the effects of factors such as transaction costs and other institutional arrangements that generate discontinuous adjustment to equilibrium price level. The results are compared with standard linear model estimations. Results indicate that there is strong evidence for asymmetric adjustment of wheat prices in Turkey to the equilibrium price, hence models allowing for regime switching are more preferable over the linear ones. However, the diagnostics of the TAR model reveal that specification of a TAR model allowing for more than two regimes, or a smooth transition autoregressive (STAR) model that allows for smooth transition through a continuum of regimes might be more appropriate.

In this thesis we study three basic issues related to international black tea markets: Are black tea markets integrated? Where is the price of black tea discovered? Are there leaders and followers in black tea markets? We use two statistical techniques as engines of analysis. First, we use time series methods to capture regularities in time lags among price series. Second, we use directed acyclic graphs to discover how surprises (innovations) in prices from each market are communicated to other markets in contemporaneous time. Weekly time series data on black tea prices from seven markets around the world are studied using time series methods. The study follows two paths. We study these prices in a common currency, the US dollar. We also study prices in each country's local currency. Results from unit root tests suggest that prices from three Indian markets are not generated through random walk-like behavior. We conclude that the Indian markets are not weak form efficient. However, prices from all non-Indian markets cannot be distinguished from random walk-like behavior. These latter markets are weak form efficient. Further analysis on these latter markets is conducted to determine whether information among the markets is shared. Vector Autoregressions (VARs) on the non-Indian markets are studied using directed acyclic graphs, impulse response functions and forecast error decomposition analyses. In both local currencies and dollar-converted series, the Sri Lankan and Indonesian markets are price leaders in contemporaneous time. Kenya is an information sink. It is endogenous in current time. Malawi is an exogenous price leader in dollar terms, but it is endogenous in local currency in contemporaneous time. In the long run, Sri Lanka, Indonesia and Malawi are price leaders in US dollar terms. In local currency series, Indonesia, Kenya and Malawi are price leaders in the long run. We use Theil's U-statistic to test the forecasting ability of the VAR models. We find for most markets in either dollars or on local currencies that a random walk forecast outperforms the VAR generated forecasts. This last result suggests the non-Indian markets are both weak form and semi-strong form efficient.

Les architectes doivent faire face à des transformations constantes. Nos projets doivent s’adapter aux usages de demain. Mais comment pouvons-nous concevoir ces métamorphoses ?A contrario de l’espace enseigné en architecture, l’angle de cette recherche est le temps : il faudrait apprendre à insuffler le passage du temps dans les projets, afin de les rendre malléables au changement. L’architecte pourrait jouer avec le temps, en l’apprivoisant et le tissant dans son projet.Comme l’espace, le temps est un mot primitif impossible à définir. De nombreux philosophes, historiens et scientifiques en ont pourtant décrit les qualités, comme Henri Bergson dépeignant la « réalité mobile », Georg Friedrich Hegel « le rythme du tout organique » ou encore Reinhart Koselleck le « futur passé ». Et si peu d’architectes s’y sont confrontés, un Britannique s’est démarqué des autres dans les années 1960. La société était en pleine transformation, les théories des modernes renversées par la critique. À la recherche d’une architecture en accord avec sa période, Cedric Price a pris le temps comme un paramètre de conception. Ses structures radicales constituent des cas d’études propices à ce décryptage conceptuel. Dans les allers-retours entre les textes philosophiques et les visions de l’architecte, trois caractères du temps se sont dégagés : la mobilité, le rythme, le présent. Six projets des années 1960 à 1980 ont été étudiés en fonction de ces thématiques, avec l’appui des archives du Centre Canadien d’Architecture de Montréal.La thèse dévoile six « jeux du temps » : ceux du renouvellement et de l’opportunité, de l’obsolescence et de l’immédiateté planifiées, de la distorsion consciente et de l’incertitude calculée. Les mots sont propres à Cedric Price et ils témoignent de son expérience singulière au temps, comme du contexte de l’époque. Sur chacun, les approches temporelles ont été croisées avec les images des quatorze catégories de sa dernière exposition Mean time, offrant un lexique illustré. Réunis en trois grandes parties, ils révèlent le passage entre le temps du monde et celui de l’architecture : de la « réalité mobile » au mobile, du tempo du milieu au temporaire, du présent à la présence. Les diagrammes utilisés par l’architecte y sont respectivement décryptés comme des moyens d’attraper, créer et raconter le temps. Et ainsi en est-il aussi des architectures produites. Car au fil de la recherche, il est apparu qu’elles étaient avant tout des cadres pour saisir le changement. Ce sont des dispositifs flexibles et ouverts. Sans forme, ils se per-forment. Ils s’affirment comme des processus.Cette conception n’entraine pas une esthétique forte et unique mais des expériences esthétiques, révélant les interactions ordinaires de l’homme et de l’environnement à l’architecture. Face à l’incertitude qu’entraine l’inévitable passage du temps, l’architecture peut cultiver les « délices de l’inconnu », comme aimait le dire Cedric Price. La thèse en propose des variations avec des réalisations du début XXème, de l’avant-garde des années 1960 à 1980 et d’aujourd’hui. Des ouvertures sont proposées avec l’architecture japonaise, dont les paysages artificiels dévoilent une même attention à l’impermanence et complètent les théâtres de Cedric Price.Les six jeux du temps proposés sont des guides pour apprivoiser le temps et le vivre, et non le maitriser ou le subir. Ils illustrent des manières de concevoir avec le temps, en différentes intensités. Aux concepteurs ensuite de s’y essayer, inventant à leur tour leurs propres architectures de l’impermanence
Architects have to face constant transformations. Our projects must adapt themselves to the uses of tomorrow. But how can we perceive theses metamorphosis?In contrast to space taught in architecture, the angle of this research is time: it would be needed to learn to inject the passage of time in projects, in order to make them malleable to change. The architect could play with time by taming and forging it in its project.Just as space, time is a primitive world impossible to define. However, numerous philosophers, historians and scientists have described its qualities, like Henri Bergson painting the “mobile reality”, Georg Friedrich Hegel “the rhythm of the organic whole” or yet Reinhart Koselleck the “past future”. And if few architects confronted themselves with it, one British stood out from the others in the 1960’s. The society was rapidly transforming, the moderns’ theories turned down by the critique. Seeking an architecture in agreement with its period, Cedric Price took time as a factor of conception. Its radical structures make up suitable study cases for this conceptual deciphering. Going back and forth between the philosophical texts and the architect views, three characteristics of the time emerged: the mobility, the rhythm, the present. Six projects from between 1960 and 1980 have been studied in accordance with these themes, with the support from the archives of the Canadian Centre for Architecture of Montréal.The thesis unveils six “time designs”: those of renewal and opportunity, planned obsolescence and immediacy, conscious distortion and calculated uncertainty. The words are proper to Cedric Price and they show its singular experience with time, as well as the context of this period. On each, the temporal approaches have been crossed with the pictures of the fourteen categories of his last exhibition Mean time, giving an illustrated lexical. Reunited in three big sections, they reveal the passage between the time of the world and the one of the architecture: from the “mobile reality” to the mobile, from the tempo from the middle to the temporary, from the present to the presence. The diagrams used by the architect are respectively deciphered as means to catch, create and narrate time. Thus, that’s how is the produced architectures too. Indeed, it appeared along the research that they were firstly settings to understand change. They are flexible and open plans. Without form, they per-form themselves. They assert themselves as processes.This concept does not produce a strong and unique aesthetics but aesthetic experiences, revealing the ordinary interactions of the man and the environment to architecture. Faced with the uncertainty that produces the unavoidable passage of time, architecture can cultivate the “joys of the unknown”, as Cedric Price liked to say. The thesis suggests some variations with realizations from the early twentieth century, the avant-garde of the years 1960 to 1980 and today. Openings are proposed with the Japanese architecture, of which the artificial landscapes unveil the same attention to impermanence and complete Cedric Price theatres.The six time games suggested are guides to tame time and live it, and not control or suffer it. They illustrate ways of designing with time, of different intensities. Then it is up to the creators to try it, by coming up too with their own architectures of impermanence

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.

This study investigates how increased wind power production (in MWh) in Sweden has affected electricity price volatility in the Nordic wholesale electricity exchange Nord Pool. The importance and growth of wind power have emerged in light of its low marginal costs of production and it being a renewable, zero-carbon electricity generation source. Previous studies have found that while increased wind power production generally lowers the average wholesale price of electricity, volatility tends to increase due to the intermittent character of wind power production. By using daily price and wind power data from the Nordic exchange market Nord Pool during the period 2015-2017, a GARCH model was used to investigate how wind power has affected price volatility. The results indicate that electricity price volatility increases in the long run when wind power production increases. The reasons behind this could be found in the inflexibility of baseload power production. However, the Swedish electric power system also benefits from a high degree of flexibility due to the presence of hydropower reservoirs.

The most important market segmentation in sports and entertainment industry is the competition between customers that buy bundle and single tickets. A common selling practice is starting the selling season with bundle ticket sales and switching to selling single tickets later on. The aim of this practice is to increase the number of customers that buy bundles, to create a fund before the season starts and to increase the load factor of the games with low demand. In this thesis, we investigate the effect of time dependent demand on dynamic switching times and the potential revenue gain over the case where the demand rate is assumed to be constant with time.

Rapid increases in consumer food price beginning in 2007 generated interest in identifying the main factors influencing these increases. In subsequent years, food prices have fluctuated, but generally have continued their ascent. The effects of crude oil, gasoline, corn, and ethanol prices, as well as, the relative foreign exchange rate of the U.S. dollar and producer price indexes for food manufacturing and fuel products on domestic food prices are examined. Because the data series are non-stationary and cointegrated, a vector error correction model is estimated. Weak exogeneity and exclusion tests in the cointegration space are performed. Directed acyclical graphs are used to specify contemporaneous causal relationships. Dynamic interactions among the series are given by impulse response functions and forecast error variance decompositions. Weak exogeneity tests indicate all eight series work to bring the system back into equilibrium following a shock to the system. Further, exclusion tests suggest crude oil, gasoline, food CPI, ethanol, and food PPI variables are not in the long-run relationships. Dynamic analyses suggest the following relationships. Ethanol price is not a major factor in domestic food prices, suggesting that food prices are largely unaffected by the recent increased use of corn-based ethanol for fuel. Crude oil prices, corn prices, and the relative foreign exchange rate of the U.S. dollar, however, do influence domestic food prices with corn price contributing the most to food price variability. Innovation accounting inferences are robust to potential different contemporaneous causal specifications.

Introduction Commodity markets are distinct from other product markets due to the existence of forward sales and futures contracts. Forward selling and the trading of a commodity derivative implies prices are subject to the influence of economic agents who are not directly engaged in consumption or production of the commodity. As a result, even when the forces of supply and demand are in equilibrium, prices may still move and vary purely due to activities of agents operating in the futures markets. Inspired by this observation, the dissertation provides a new analysis of the role of futures markets trading on the dynamics of commodity prices over different time scales. Throughout the dissertation, the underlying economy can be conceived of as populated by a commodity producing firm with access to a stochastic production technology that yields new output of the commodity every time period. The firm also has access to a storage technology which it can use to hold inventory. The firm’s sales are made either in a spot market for cash or can be sold ahead of production for future delivery using a forward contract specifying the price and date of delivery to the holder or buyer. When such forward contracts are traded or exchanged in a centralized market, they become futures contracts. Stochastic production and consumption of the commodity implies that the firm faces a risk of losses from volatile prices. The firm would therefore like to sell forward as much of its output as it can. Those who buy the firm’s contract take on the risk of changing prices (are subject to loss) and demand a risk–premium as compensation for taking over the firm’s risk. In commodity markets, this risk premium is measured either as the basis, the contemporaneous difference between the current spot price and the forward price or the expected return, the difference between the expected future spot price and the forward price (Yang, 2013). These risk premiums depend on transaction costs incurred when trading futures in a commodity exchange (Hasbrouck, 2009) and will have an effect on the firms investments in physical production of the commodity – as it reflects the cost of hedging. Given that the risk premiums are a function of the transaction costs, quantifying the size of these costs has become an important endeavor in understanding of price dynamics. The first paper takes on this task by developing parametric models that can be used to measure liquidity costs using exchange traded futures transactions prices only. Simple dynamic linear regressions with switching are used in this task. The models treat underlying price processes and liquidity costs as unobserved components in state space systems with trade direction indicators of buyer and seller initiated transactions being the outcomes of hidden Markov processes. Simulation studies show that the model provides accurate effective transaction cost estimates and beats the tick-rule method of signing trades using prices. 1 Having developed a way to accurately measure the liquidity costs, focus turns to what is driving price changes observed at a high frequency tick-by-tick level. The second paper presents a new theory of history dependent price setting in limit order book market for commodity futures. In traditional financial markets theory, the price discovery process is a form of tâtonnement; informed agents trading against liquidity providers or market markers slowly reveals private information which is incorporated into prices. The marketmarkers adjust their quotations to reflect the information revealed by the informed agents transactions until a new equilibrium is attained. However, when trading contracts of physically delivered commodities, the transactions are directly informative of expected future supply and demand since they reflect production and consumption intentions. Transactions therefore have price impact: a buyer initiated trade tends to push prices upwards with the opposite effect following a seller initiated trade. The history dependent framework takes this hypothesis to the data and shows that agents trading in a limit order book market for commodity futures adjust their prices in response to order flow – the sequence of trade originator signs. Over long time periods, commodity price time series exhibit boom–bust cycles that may be accompanied by periods of either high or low volatility. One way to model time series subject to such boom and bust cycles is the hidden Markov or regime switching model popularized in economics by Hamilton (1990). The standard regime switching model assumes that the growth and volatility phases of a time series coincide and that autoregressive lag lengths are similar across regimes. This assumption results into biases in estimates of unconditional variances across different regimes. To overcome these problems, the third paper presents a new “Double Mixture Autoregressive” model for time series subject to potentially independent changes in level and volatility. This model allows for the autocorrelation structure of the data generating process to vary across variance regimes. By accounting for the change in the lag length of time series across the different volatility periods, more precise estimates of the unconditional moments are obtained. The model is applied to set of industrial commodity prices and is shown to accurately represent the boom–bust cycles and volatility switches that characterize the time series. The dissertation is divided into three related chapters/papers. The first chapter/paper, “New Open to Old Close: Signs and Spreads in Daily Prices” presents state space models that can be used to obtain accurate measures of transaction costs using daily summaries of trading activity: open, close, max and min prices. The second chapter/paper, “Price impact as reaction to order flow imbalance”, develops and successfully tests a theory of history dependent price formation in a limit order book market of commodity futures. Finally, the third chapter/paper, “A Double Mixture Autoregressive Model of Commodity Prices”, presents a new type of non-linear econometric model that captures the boom– bust cycles and volatility switches that characterize the long term behavior of commodity price time series. I now give compact overviews of each chapter, followed by a brief conclusion of how the works are all related. (I) New Open to Old Close: Signs and Spreads in Daily Prices This chapter shows how to estimate bid-ask spreads using observed transactions prices only. The main contribution of this chapter is to provide a method that almost always guarantees positive estimates of the transaction costs. Concretely, let pt = logPt be the log price of a commodity futures contract. The price follows: pt = mt + sqt , where mt is the unobserved efficient or fundamental price process, s ≥ 0 is the bid-ask spread and qt = ±1 is a trade initiator indicator: qt = +1 if a transaction is buyer initiated, −1 if seller initiated. The fundamental price follows the process mt = mt−1 +u m t where u m t is a zero-mean disturbance uncorrelated with qt . Bid and ask prices are: p Bid t = mt−1 + s and p Ask t = mt−1 − s which imply the pre-trade mid–prices are given by midt = 1 2 (p Bid t + p Ask t ) = mt−1 and bid–ask spreads are p Bid t − p Ask t = 2s. Log returns can be written as: ∆pt = s∆qt +u m t . If qt is observed, we have: sbMLE = Cov(∆pt ,∆qt) Var(∆qt) = s. But qt may not be observed or recorded in some datasets, e.g. open outcry markets. Assuming Prob(qt = +1) = Prob(qt = −1) = 1 2 , Roll (1984) estimated s by: sbRoll = p −Cov(∆pt ,∆pt−1). One major shortcoming of this estimator is that if the sample autocovariance is positive, then sbRoll is undefined. This had led to an active research area with alternatives to Roll’s estimator: Gibbs sampling approach of Hasbrouck (2002), a time consuming and difficult to implement method; Non-parametric estimators of Abdi and Ranaldo (2017), similar to Roll’s estimator: gives +ve autocovariances; and the empirical characteristic function of Chen, Linton and Yi (2017) which is useful but incomplete. We propose an alternative parametric estimator that is: easy and fast to implement, more informative: estimates mt , s and qt , and based on transaction prices only. We make the following assumptions: (i). transaction prices are generated by pt = mt + st qt , mt = mt−1 +u m t , qt = ±1, u m t ∼ N(0,σ 2 m) where {u m t ,qt } ∞ t=1 is a strictly stationary process; (ii). st is a random variable defined by st = s +u s t , where u s t ∼ N ¡ 0,σ 2 s ¢ with u s t ⊥ u m t ; (iii.) the trade initiator indicator is the outcome of a first order Markov process defined by the transition matrix: P = h pj k i where pj k = Prob(qt = k|qt−1 = j), for j,k = 1, 2 and qt = ±1 are transition probabilities. The price process pt = mt + st qt can be written in state space form as: yt ≡ ∆pt = mt − mt−1 + st qt − st−1qt−1 = At xt where xt = ³ mt ,mt−1,st ,st−1 ´0 is an unobserved state VAR(1) process: xt = φxt−1 + γs + ut , and At = h 1,−1,qt ,−qt−1 i is a measurement/observation matrix taking on 4 distinct values. At is a first order Markov process, inheriting properties of qt . The error vector ut = ¡ u m t , 0,u s t , 0¢0 is i.i.d N(0,Σu) where Σu is a (4×4) variance–covariance matrix with off–diagonal elements equal to zero and diagonal (σ 2 m, 0,σ 2 s , 0). To test the model’s ability to give reliable estimates of bid-ask spread and mid–prices, we generate artificial data following Hasbrouck (2004). We assume that: (i) log-prices generated by the equation pt = mt + st qt , with m0 = 100, σ 2 m = 0.012 , st = s = 0.01 each day, (ii) trades per day are drawn from the set {15, 16,..., 25} for 100 days giving 1, 981 observations with a median of 20 trades per day. The model is able to reproduce these vales in after maximum likelihood estimation, providing estimates as precise as the Gibbs sampling estimator of Hasbrouck (2002). Using qt = +1 if Prob[qt = +1|ψt−1] > 1 2 labels 76% of 3 trades correctly which beats the “tick rule” method used for signing trades in the absence of quotes. (II) Price Impact as Reaction Order Flow Imbalance Most modern financial markets are organized around a limit order book (LOB): when a buy(sell) order is submitted, it is matched against still unmatched sell(buy) orders, in which case a transaction occurs. If not immediately matched, remains active in the book until a match against a future incoming order or canceled. We postulate a theory of price dynamics in the LOB market of commodity futures. We begin by assuming: (i) the buyer–seller initiator indicators qt = ±1 are Markovian with transition matrix P as in chapter 1; (ii) the spread st is time varying and, (iii) the LOB’s mid-price/fundamental value mt is updated in a history dependent manner. Our hypothesis is that agents submitting orders to the LOB adjust their prices such that the mid–price evolves according to the price update rule: mt+1 = mt + st+1(qt − qbt+1)+u m t+1 where qbt+1 = E £ qt+1|qt ¤ is the prediction of the next trade sign given the sign of the last observed transaction and u m t+1 ∼ N ¡ 0,σ 2 m ¢ is an innovation to the mid price reflecting public information unrelated to the sequence {qt } ∞ t=1 . Markovian trade signs imply the best linear one-step forecast: qbt+1 = E £ qt+1|qt ¤ = qt ×Prob(qt+1 = qt)− qt ×Prob(qt+1 6= qt) = (1−2π)qt , where π = Prob(qt+1 6= qt) = 1−(π1p11+π2p22), is the probability of a sign reversal. The expected price change is therefore: Et∆mt+1 = 2πsqt . The three assumptions lead to the following properties. (i) Martingale Prices: the transaction price process is a martingale, i.e.: E(pt+1) = pt . (ii) Bid-Ask Spread: regret free price quotations require that the ask and bid prices are respectively set such that: p Ask t = Et £ pt+1|qt+1 = +1 ¤ = mt +(1+2πqt)s and p Bid t = Et £ pt+1|qt+1 = −1 ¤ = mt −(1−2πqt)s, which implies the bid–ask spread given by: p Ask t − p Bid t = 2s. (iii) No Quasi-Arbitrage: the transaction price process pt does not admit quasi-arbitrage or price-manipulation of Huberman and Stanzl (2004). The three assumptions also lead to testable predictions: lag-1 unconditional impact of Bouchaud, Kockelkoren and Potters (2006), defined as : R(1) := ­ (mt+1 −mt)· qt ® t , where the empirical average 〈·〉t is taken over all transactions of any volume. For any k > 0, we can define the lag-k response function: R(k) = E £ (mt+k −mt)· qt ¤ ≡ 〈(mt+k −mt)· qt〉 t , which measures the information content of the current trade on the mid-price k trades into the future. Defining the symbols a = (π1 − π2) 2 , b = 4π1π2 and λ = 1 − p12 − p21 where π1 = p21 p12+p21 and the lag-k anti-correlation function: C(k) = a(k − 1) − b ³ 1 − 1−λ k 1−λ ´ , for k > 0, with C(1) = 0, we find the following one-to-one relationships between lag-1 and lag-k response functions: R(1) = 1 1+C(k) · R(k) for k = 1, 2,.... Stochastic volatility over k trades is the average: 1 k Pk `=1 £ ∆pt+` ¤2 . The price difference between any two trades is ∆pt+1 ≈ ∆mt+1 u 2πst+1qt and we can approximate volatility over k trades by the empirical average: 1 k Pk `=1 E £ ∆pt+` ¤2 ≈ ­ 4π 2 × ¡ s 2 t+1 +σ 2 s ¢® k . We use data from the Tokyo Commodity Exchange (TOCOM) for two of the most liquid commodity futures contracts: Gold Standard (TOCOM Product Code: 11, Bloomberg: JGA ) and Platinum Standard (TOCOM Product Code: 13, Bloomberg: JAA), with the delivery month of February 2020, over the day-time trading session, from 8:45 a.m. to 3:15 p.m. Japanese Standard Time on the 24th April 2019. Each contract has a minimum price increment of JPY 1 per gram. We estimate the model described in Chapter 1 and compute the response functions: R(1) to R(k) and run the regressions R(1) = α + β 1 1+C(k) · R(k) for k = 2, 3, 4, 5 and find that statistically α = 0 and β = 1 with R 2 ≥ 60% in all cases. For stochastic volatility that at least 85% is explained by the update rule. (III) A Double Mixture Autoregressive Model of Commodity Price Many commodity prices exhibit boom–bust type behavior: sustained periods of price increases are followed by sudden sharp collapses. Since around the year 2000, booms have become longer while busts have tended to be short but steep, suggesting a structural change in growth and persistence. We model these features of the data using a novel double mixture autoregression with two independent hidden Markov chains. One chain models shifts in mean growth rates that accounts for rising and falling prices, while a second chain tracks changes in volatility and lag-structure. While the two chains are independent, the persistence of price growth depends on the volatility state, which allows the lag-structure to vary across variance regimes. Let yt = ∆log(Pt) represent a time series of the change in the log price of a commodity. Let S m t and S υ t represent, respectively, indicators of a mean and variance regime. Here S υ t = {0, 1} captures volatility changes characterizing many commodity price series while S m t = {0, 1} represents shifts in the growth rate related to price boom–bust cycles. The regimes S m t and S υ t are each the outcome of an independent first order Markov chain with transition matrices: P m = £ p m j|i ¤ and P υ = £ p υ j|i ¤ ,i, j = {0, 1}, respectively. The two components correspond to a restricted four regime model, with state St = S m t ×S υ t and transition matrix: P m ⊗P υ . The state St defines a dynamic linear model: yt = µS m t + P`S υ t ) l=1 φS υ t ,l ³ yt−l −µS m t−l ´ + σS υ t et , et ∼ i.i.d N(0, 1), with time varying intercepts µS m t and volatilities σS υ t . The lag length `(S υ t ) is potentially changing across variance regimes. This is the first novelty in the paper. In the original model of Hamilton (1989), there are no volatility changes and the state σ can be thought of as a nuisance parameter, in the sense of Sartori (2003) or Elliott, Müller and Watson (2015), which we are not interested in. In the present context, we are interested in modeling the boom–bust related shifts in mean growth rates while treating the change in volatility as incidental shift parameters in the sense of Neyman and Scott (1948, Example 1). This conceptual approach allows us to form a profile likelihood and filtering technique that can be used to estimate the model in two stages. Initially, location related parameters are estimated while suppressing the underlying autoregressive structure. These parameters are then held fixed while the optimal lag-structure across variance regimes is determined. We apply the model to three industrial commodities price time series: Crude Oil, Aluminium and Rubber. We find that in each case, the model captures boom and bust cycles, with data from more recent periods exhibiting higher volatility, longer price rallies 5 and steeper collapses. In order to show the models relevance for other applications in macroeconomics such the identification by heteroskedasticity method of Lütkepohl and Velinov (2016), we aggregate monthly frequency data to quarterly. This temporal aggregation allows us to show that the methods can be used for instance in a structural vector autoregression that includes data only available at the quarterly frequency such as GDP. This avoids using the more complex mixed frequency type models such as those advanced by Christensen, Posch and Van Der Wel (2016). Conclusion The analysis covers a variety of commodities at different observation frequencies or time scales. The first uses commodity price series at a daily frequency for periods of up to one year, approximately 252 days of market open to close futures prices from a commodity exchange. The second paper uses high-frequency trade-by-trade or tick-level data from a continuous trading session in a single day. Finally the third paper uses long time series spanning decades. It is important to look at data from the microscopic(ticklevel) to the macroscopic(decades) time scale in order to obtain a holistic view of the behavior of prices. Over very short time periods, the tick size is the smallest movement over any two prices and price changes can be viewed as random walks over a grid, with jumps occurring at arbitrary times (Curato and Lillo, 2014). This calls for the modeling of the microstructural features of the data such as the bid–ask spread; usually equal to half a tick for highly liquid assets, and the sequence of buy–sell orders which may predict the direction of short term price movements. At a coarser time-scale of months, quarters or years, the microstructural issues can be dispensed with and a more traditional time series approach used to describe price dynamics. While I separate the analysis based on the observation time scales, multi frequency models of price dynamics are possible, albeit with a more complex structure to capture the volatility components at play over every time scale (Calvet and Fisher, 2001, 2004).

碩士
國立交通大學
外國語文學系外國文學與語言學碩士班
104
Lesbian pulp fiction is often remembered as a cheap form of entertainment in which sleazy stories of taboo relationships were sensationalized for profit. However, it is within this disreputable genre that the first happy endings for fictional queer women were imagined. This thesis examines The Price of Salt, the first lesbian novel in the twentieth century to have a happy ending, and how time functions within the novel to produce a queer temporality. I argue that Carol and Therese create the possibility of a future that differs from the heteroreproductive social script. This thesis contains three sections. The first section explores the genre conventions of lesbian pulp fiction and how The Price of Salt includes but also destroys some of these tropes. The second section is concerned with how the novel subverts the domestic ideology of the Cold War era. The third section examines the concepts of chrononormativity and reproductive futurism and how they apply to The Price of Salt.

Access to abstract permanently restricted to Ball State community only.
Estimation and forecasting of time series data -- Fitting of Saudi stock price by deterministic models -- Determination and fitting of the ARIMA models for Saudi stock price data -- Evaluation of forecasts by cross validation.
Access to thesis permanently restricted to Ball State community only.
Department of Mathematical Sciences

碩士
國立交通大學
工業工程與管理系所
92
This research presents a profit model to study time-based pricing policies for make-to-order manufacturing systems facing two types of customer demand. One type of customer demand is lead-time-sensitive and the other type of customer demand is price-sensitive. Each type of customer demand is denoted as a function of price and lead-time. In order to meet customer demand for each type, manufacturer might provide multiple lead-time services with different prices. The difference between these prices is relevant to the loss of throughput to shorten manufacturing cycle time for lead-time-sensitive customer demand. However, to define the relationship between throughput and manufacturing cycle time is difficult. In this research, we use simulation model to find the relationship between throughput and manufacturing cycle time. Based on this relationship and the given customer demand functions, we solve the proposed profit model to determine the appropriate committed lead-time and price for each type of customer demand, respectively.

碩士
國立臺灣大學
資訊管理學研究所
104
Nowadays, price fluctuation point forecast is usually relying on the human judgments, and cause many opportunities of saving cost missed. For a company, buying material at a lower price and selling products at a higher price are the straightest way to obtain higher revenue. If there is a way to predict the price fluctuation of material or products accurately, a company can maximize its profit by taking a right action at a right time. This study introduces a novel forecast procedure for price fluctuation points forecast. This study proposes a price fluctuation forecast model: Price Fluctuation Point Forecast Approach (PFPFA). We not only forecast the price change degree, but also the price change time. Since the transaction data are non-uniform sampled time series, we will use quantity to present time to solve this problem. The main process of PFPFA has four phases: (1) transforming data based on the number of fluctuation points; (2) calculating times with different forecast models; (3) calculating prices based on the results of P2 with different forecast models; and (4) evaluating and selecting the best forecast model combination for groups. In this paper, we propose four models for time forecast and three models for price forecast. In consequence, for a single product, there would be twelve different forecast outcomes. we applied PFPFA in a real world case, and compare the result with the Exponential Smoothing (ES) which is commonly and currently used. The time forecast result is acceptable and the price forecast result shows that PFPFA has better performance than ES.

博士
國立臺灣大學
財務金融學研究所
100
The purpose of this paper is to investigate the approximated arbitrage bounds of option prices in the discrete time and incomplete market setting. The gain-loss ratio method of Bernardo and Ledoit (2000) is employed but market-implied risk-neutral distribution discovered by Rubinstein (1994) is used instead of the model-based pricing kernel. This modified gain-loss bounds replace the strong assumptions of the equilibrium model such as complete markets and individual’s utility, risk preference and thus the underlying asset’s distribution by the real-data implied risk attitude and distribution. Therefore, our implied gain-loss bounds of option prices are preference-free and parametric-free and avoid the misspecification error (incorrect model risk) of subjective choosing on the benchmark model. The result shows that deep-in-the-money (or deep-out-of-money) implied gain-loss option pricing bounds fall out of the model-based pricing bounds even taking the possible mispricing into consideration. This means that some good-deal investment opportunities are exist if we use Black-Scholes formula in option pricing.

碩士
國立臺中科技大學
資訊管理系碩士班
100
The prediction of stock markets is an important and widely research issue since it could be had significant benefits and impacts, and the fuzzy time-series models have been often utilized to be the forecast models to make reasonably accurate predictions. For promoting the forecasting performance of fuzzy time-series models, this thesis proposed a new model, which incorporates the concept of the entropy-based discretization partitioning, equal-width pre-partitioning and equal-depth pre-partitioning based on fuzzy time-series models. In order to evaluate our proposed approach, the source data was using actual trading data from Taiwan Stock Exchange (TAIEX), and the experimental period is selected from 1997 to 2003 as the datasets for verifications. Finally, the experimental results showed that our proposed approach was effective in improving the forecasting errors on forecasting stock price significantly. Furthermore, the performances in terms of root mean squared error (RMSE) indicate that the proposed model is superior to the compared models suggested by Chen (1996), Karaboga et al. (2009), Cheng et al. (2009) and Chang et al. (2011) earlier. It is evident that the proposed model is a good approach to improve the forecasting performance fuzzy time-series models.

碩士
國立臺灣科技大學
電子工程系
104
Time-varying pricing is known able to in uence customers' behavior in elec- tricity usage , and has been used for peak load shedding in power grid. In this thesis, we propose an price-incentive load management algorithm in or- der to achieve real-time power balance in a neighborhood with several of load customers and renewable energy sources (RES). To design a price model that can improve the power balance, we consider real-time pricing combined with inclining block rates tari s. In our problem formulation, we take into ac- count di erent types of load models such as deferrable loads, storage devices, and EVs. Thus, the research issue amounts to minimizing the electricity pay- ment of users, subject to the individual constraints of the loads. The problem can be solved by a linear programming method. Simulation results con rm that the proposed algorithm can improve the power balance signi cantly. By applying the price model combined with inclining block rates tari s, the proposed pricing scheme drastically reduces the electricity payments of the customers.

碩士
國立臺灣大學
資訊管理學研究所
105
Real-Time Bidding (RTB) has changed a game changer of online advertisement. In RTB, many researchers have focus on how to maximize the profit of Demand-side platform (DSP). These researches usually consider that winning price can express as a probability distribution. However, in RTB, if a DSP lose in an auction, it will not know the winning price of that bid. Which means, what DSPs own in their data base is a partial unobserved data. In this research, we will focus on how to recover the original distribution from partial unobserved data. We propose a new model, Shaded Forest, to deal with this kind of partial unobserved data in RTB. The results of experiment show that shaded forest the accuracy of predicting winning price is better than other algorithms and have good ability to handle data with high percentage of truncation.

This study examines price formation in one of the largest wholesale electricity markets in the world: the Pennsylvania Jersey Maryland Interconnection, which serves 13 states and the District of Columbia with over 60 million consumers. The contribution of this thesis is to apply a variety of time series models offered in the literature to a large data set describing a single market, allowing for a comparison of their performance as well as demonstrating their validity. A central question that drives market deregulation is if it has created efficiency gains. To formalize this notion of efficiency, we implement tests for stationarity to measure the degree of randomness over time, finding that short run volatility can result in the outcomes for these tests that are inconclusive. We explore this volatility structure using Asymmetrical Power Autoregressive Conditional Heteroskedastic (APARCH) framework which captures the asymmetric nature of price shocks, finding that this behavior is unique to electricity returns, and that APARCH offers a better modelling alternative than simpler representations. Additionally, we account for long memory given the seasonal drivers of electricity prices which are persistent using Autoregressive Fractionally Integrated Moving Averages (ARFIMA). Temperature related market drivers are further modelled using Fourier based seasonality functions which enable us to capture cycles over multiple frequencies. Lastly, we provide an application of Markov Regime Switching models to account for the possibility of multiple states. Although appealing from a theoretical perspective, we find that the increased complexity of the model does not necessarily translate to better performance over simpler non-switching alternatives. These findings highlight the importance of establishing the features of the time series before selecting an appropriate model, and motivating it with economic rationale.
Graduate