Academic literature on the topic 'Stocks Prices Australia Econometric models'
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Journal articles on the topic "Stocks Prices Australia Econometric models"
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Akbulaev, Nurkhodzha, Basti Aliyeva, and Shehla Rzayeva. "Analysis of the Influence of the Price of Raw Oil and Natural Gas on the Prices of Indices and Shares of the Turkish Stock Exchange." Pénzügyi Szemle = Public Finance Quarterly 66, no. 1 (2021): 151–66. http://dx.doi.org/10.35551/pfq_2021_1_8.
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This article is a review on the impact of prices and their dependence on the cost of oil and natural gas on the world stock markets. The main studies and results achieved in the field of the impact of prices on both the stock index and industrial stocks and the dependence on the level of oil prices are presented. The paper presents an econometric study on the choice of offers on the securities market that allows us to identify the main specifics of changes in prices for the stock index and industrial shares in the daily period from 13. 05. 2012 to 01. 12. 2019. The article uses methods for estimating the impact of the price of natural gas and WTI crude oil using the Gretl statistical program, taking into account the selection of the main correlation features of the price matrix. Of the 13 proposed research models, only one model showed its statistical insignificance. A paired linear model of the CocaCola share price dependence and its dependence on NGFO prices was presented and analyzed in detail. Based on the results of econometric modeling, linear regression models were constructed for the dependence of stock prices on the NGFO and WTISPOT prices. The Gretl environment allows you to evaluate the situation in the econometric environment and make a forecast based on the obtained models of the dependence of stock prices and make appropriate conclusions.
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Zhu, Rong, Zuo Quan Zhang, Xiao Yue Li, Xuan Wu, and Su Zhang. "The Study on the Plasticity Theoretical Models of the Volatility of Stock Prices." Advanced Materials Research 518-523 (May 2012): 5963–67. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5963.
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This paper analyzes the characteristics of the stock price fluctuation compared with elastic-plastic theory in mechanics and introduces the concept of stock equilibrium price, plasticity of stock price analogically. A basic model of the stock plasticity under the relationship between stock price fluctuation and trading volume changes is also built. Tested by 20 kinds of stocks from Shanghai and Shenzhen stock markets in China by using the econometric analysis software Eviews3.0 afterwards, the basic model is improved, and three developed models are built from it. Finally, this paper obtains more scientific and reasonable stock price plasticity model after the comparative analysis of the four previous models.
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Shi, Chao, and Xiaosheng Zhuang. "A Study Concerning Soft Computing Approaches for Stock Price Forecasting." Axioms 8, no. 4 (October 18, 2019): 116. http://dx.doi.org/10.3390/axioms8040116.
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Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.
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Nautiyal, Neeraj, and P. C. Kavidayal. "Analysis of Institutional Factors Affecting Share Prices: The Case of National Stock Exchange." Global Business Review 19, no. 3 (March 14, 2018): 707–21. http://dx.doi.org/10.1177/0972150917713865.
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This study offers empirical findings on the impact of institutional variables on firm’s stock market price performance. In order to identify the influence of companies financial on NIFTY 50 Index, our sample consists of balanced panel of 30 actively traded companies (that becomes the study’s index representative) over a massive transition period, 1995–2014. Attempts have been made with a wide range of econometric models and estimators, from the relatively straightforward to (static) more complex (dynamic panel analyses) to deal with the relevant econometric issues. Results indicate that increasing debt in capital structure does not establish any significant relation with the stock prices. Earnings per share (EPS) shows a poor explanation of price variation. Economic value added (EVA) indicates a positive relation with current as well as previous year’s stock price performances. However, dividend payout (DIVP) and dividend per share (DPS) achieve negative relationship at moderately significant level. The present study confirms that performance of companies fundamental ratios will be essential and immensely helpful to investors and analysts in assessing the better stocks that belong to different industry groups.
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Ma, Le, Richard Reed, and Jian Liang. "Separating owner-occupier and investor demands for housing in the Australian states." Journal of Property Investment & Finance 37, no. 2 (March 4, 2019): 215–32. http://dx.doi.org/10.1108/jpif-07-2018-0045.
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PurposeThere has been declining home ownership and increased acceptance of long-term renting in many western countries including Australia; this has created a problem when examining housing markets as there are dual demand and include both owner-occupiers and investors. The purpose of this paper is to examine the long-run relationship between house prices, housing supply and demand, and to estimate the effects of the two types of demand (i.e. owner-occupier and investor) on house prices.Design/methodology/approachThe econometric techniques for cointegration with vector error correction models are used to specify the proposed models, where the housing markets in the Australian states and territories illustrate the models.FindingsThe results highlight the regional long-run equilibrium and associated patterns in house prices, the level of new housing supply, owner-occupier demand for housing and investor demand for housing. Different types of markets were identified.Practical implicationsThe findings suggest that policies that depress the investment demand can effectively prevent the housing bubble from further building up in the Australian states. The empirical findings shed light in the strategy of maintaining levels of housing affordability in regions where owner-occupiers have been priced out of the housing market.Originality/valueThere has been declining home ownership and increased acceptance of long-term renting in many western countries including Australia; this has created a problem when examining housing markets as there are dual demand and include both owner-occupiers and investors. This research has given to the relationship between supply and dual demand, which includes owner-occupation and investment, for housing and the influence on house prices.
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Khoa, Bui Thanh, and Tran Trong Huynh. "Forecasting stock price movement direction by machine learning algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6625. http://dx.doi.org/10.11591/ijece.v12i6.pp6625-6634.
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<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including <a name="_Hlk106797328"></a>support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.</span></p>
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MISSAOUI, Sahbi, and Nizar RAISSI. "Underpricing Process of IPOs in Tunis Stock Exchange: An Agent-Based Modelling Approach." Accounting and Finance Research 10, no. 2 (April 7, 2021): 1. http://dx.doi.org/10.5430/afr.v10n2p1.
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The fundamental problematic treated in our study was an attempt to explain an anomaly in the issuance of new stocks in IPOs process. The objective of this research is to analyze the effect of certain variables on the level of undervaluation by presenting certain econometric models issued from Agent-based modelling approach. Certain variables can be predictive of the phenomenon of undervaluation such as: the Stock equity distributed to institutional investors, liquidity in the secondary market measured by the price range and the type of investor who can be insiders or outsiders, in addition to these variables we have introduced some control variables which in turn help explain the level of underpricing and which are the age of the company, its size and dimension, the volume of trade and the volatility. Empirically and based on a sample of 16 companies, we were able to respond to our problematic. In fact, according to the hypotheses tests, the prices of the newly introduced stocks on the stock exchange are mostly undervalued which were aligned with our study. Thereby, the methodology adopted based to Dynamic linear models (DLM) that allows offering a very generic framework to analyse time series data. The results of this research were, in part, consistent with work done in developed countries (especially in USA and Europe). Indeed, the undervaluation is in a positive relationship with certain explanatory variables such as the Institutional ownership (INST), Insiders ownership (INSID), Price range (FOUR), etc. On the other hand, we were able to identify significant negative relationships between the initial undervaluation and the basic variable Outsiders ownership (OUTSID), the size of companies listed on the Tunis Stock exchange (BVMT) and the volume of issued stocks.
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Ghosh, Papiya, and Brishti Guha. "THE STUDY OF RELATIONSHIP BETWEEN TOBIN’S Q AND US STOCK PERFORMANCE OF SELECTED FIRMS." International Journal of Advanced Economics 1, no. 2 (June 22, 2020): 85–94. http://dx.doi.org/10.51594/ijae.v1i2.56.
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The objective of study was to test the dynamic effects of changes in Tobin’Q on stock prices of selected 249 US public companies of different industry categories. Panel unit roots tests and cointegration tests are implemented. Next, DOLS and GMM models are estimated. Annual data for the 2004-2012 period are used for the above selected US companies. Panel unit root tests provide somewhat mixed evidence of non-stationarity of both variables. There is clear evidence of cointegration between the above variables. The negative coefficient of the error-correction term shows convergence toward long-run equilibrium, though at slow pace. The estimates also reveal shortrun net positive interactive feedback effects between the variables. Both DOLS and GMM estimates display similar picture of overvaluation of stocks in terms of upward movement in Tobin’s Q beyond 0-to-1 range. For most parts of the sample period, the US stock market was in declining mode due to heightening of economic uncertainties during the Great Recession and several years beyond. Tobin’s Q should be improved to boost stock prices. This is more of a long-run phenomenon. In the short run, both reinforce each other. The topic is unique and the existing literature on this topic is scant. Relatively new econometric techniques have been applied for estimation using panel data. The results are quite insightful, in our view.
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Rahman, Matiur, and Muhammad Mustafa. "Dynamics of Tobin’s Q and US Stock Performance." International Review of Business and Economics 2, no. 2 (2018): 52–68. http://dx.doi.org/10.56902/irbe.2018.2.2.3.
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To study the dynamic effects of changes in Tobin’s Q on stock prices of selected 249 US public companies of different industry categories. Panel unit roots tests and cointegration tests are implemented. Next, DOLS and GMM models are estimated. Annual data for the 2004-2012 period are used for the above selected US companies. Panel unit root tests provide somewhat mixed evidence of non-stationarity of both variables. There is clear evidence of cointegration between the above variables. The negative coefficient of the error-correction term shows convergence toward long-run equilibrium, though at slow pace. The estimates also reveal shortrun net positive interactive feedback effects between the variables. Both DOLS and GMM estimates display similar picture of overvaluation of stocks in terms of upward movement in Tobin’s Q beyond 0-to-1 range. For most parts of the sample period, the US stock market was in declining mode due to heightening of economic uncertainties during the Great Recession and several years beyond. Tobin’s Q should be improved to boost stock prices. This is more of a long-run phenomenon. In the short run, both reinforce each other. The topic is unique and the existing literature on this topic is scant. Relatively new econometric techniques have been applied for estimation using panel data. The results are quite insightful, in our view.
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Reichert, Bianca, and Adriano Mendonça Souza. "Can the Heston Model Forecast Energy Generation? A Systematic Literature Review." International Journal of Energy Economics and Policy 12, no. 1 (January 19, 2022): 289–95. http://dx.doi.org/10.32479/ijeep.11975.
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The ability to predict the price of stock exchange assets has attracted the attention of economists and physicists around the world, as physical models are useful to predict volatility behaviors. Knowing that volatility is crucial for energy sector planning, the research aim was to investigate whether the Heston pricing model is useful to predict energy generation, trough the steps established by the systematic review protocol. In a corpus of 25 documents, it was possible to identify: lots of financial studies, energy and demography researches; a low level of interaction among universities; the largest number of publications from Australia and China; the most important journal; and the advantages of applying Econophysics models to solve volatility problems. In conclusion, the Heston model can be applied to predict energy generation, since it is a closed-form model and capable of modeling the stochastic volatility, reversing it to the predicted value of average energy generation.
Dissertations / Theses on the topic "Stocks Prices Australia Econometric models"
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Eadie, Edward Norman. "Small resource stock share price behaviour and prediction." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.
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Yang, Wenling. "M-GARCH Hedge Ratios And Hedging Effectiveness In Australian Futures Markets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2000. https://ro.ecu.edu.au/theses/1530.
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This study deals with the estimation of the optimal hedge ratios using various econometric models. Most of the recent papers have demonstrated that the conventional ordinary least squares (OLS) method of estimating constant hedge ratios is inappropriate, other more complicated models however seem to produce no more efficient hedge ratios. Using daily AOIs and SPI futures on the Australian market, optimal hedge ratios are calculated from four different models: the OLS regression model, the bivariate vector autoaggressive model (BVAR), the error-correction model (ECM) and the multivariate diagonal Vcc GARCH Model. The performance of each hedge ratio is then compared. The hedging effectiveness is measured in terms of ex-post and ex-ante risk-return traHe-off at various forcasting horizons. It is generally found that the GARCH time varying hedge ratios provide the greatest portfolio risk reduction, particularly for longer hedging horizons, but hey so not generate the highest portfolio return.
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Oliveira, Lima Jorge Claudio Cavalcante de. "Fractional integration and long memory models of stock price volatility : the evidence of the emerging markets." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=38164.
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Following the important work on unit roots and cointegration which started in the mid-1980s, a great deal of econometric works has been devoted to the study of the subtleties and varieties of near nonstationarity and persistence that characterize so many economic and financial time series. In recent years research activity has gained importance with outstanding contributions made on estimation and testing of a wide variety of long memory processes, together with many interesting and imaginative applications over a wide variety of different fields of economics and finance. For these reasons, this study provides empirical evidence to an aspect of fractional differencing and long memory processes, or the long memory of volatility. Evidence of long memory persistence is explored using stock price indices for eight emerging economies in both Asian and Latin American markets. The concern with the presence of long memory in higher moments of return series was first drawn by Ding, Granger and Engle (1993), using asset returns. Baillie, Bollerslev and Mikkelsen (1996) developed the fractionally integrated GARCH, or FIGARCH, process to represent long memory in volatility. The measure of long-memory persistence in the volatility is employed either using the original rescaled range statistic by Hurst (1951) and its modified version proposed by Lo (1991). Further analysis of the presence of long memory persistence is conducted using autocorrelation analysis. All the findings point in the same direction, that is, the existence of long memory in volatility irrespective of the measure chosen. Estimation of different models of volatility is undertaken beginning with the ARCH specification and until the FIGARCH model. The results show the effects to be higher in Latin American countries than in the Asian ones. This result seems consistent with the degree of intervention in the Latin American markets, known to be much higher.Other possible explanations for the occurrence of long term persistence are also pursued such as the Regime Switching modelisation proposed first by Hamilton and Susnel (1994) with the SWARCH approach. Results show that this approach can bring another possible explanation for persistence, specially in economies like Brazil that, have very different regimes for the period covered in this study.
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Limkriangkrai, Manapon. "An empirical investigation of asset-pricing models in Australia." University of Western Australia. Faculty of Business, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0197.
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[Truncated abstract] This thesis examines competing asset-pricing models in Australia with the goal of establishing the model which best explains cross-sectional stock returns. The research employs Australian equity data over the period 1980-2001, with the major analyses covering the more recent period 1990-2001. The study first documents that existing asset-pricing models namely the capital asset pricing model (CAPM) and domestic Fama-French three-factor model fail to meet the widely applied Merton?s zero-intercept criterion for a well-specified pricing model. This study instead documents that the US three-factor model provides the best description of Australian stock returns. The three US Fama-French factors are statistically significant for the majority of portfolios consisting of large stocks. However, no significant coefficients are found for portfolios in the smallest size quintile. This result initially suggests that the largest firms in the Australian market are globally integrated with the US market while the smallest firms are not. Therefore, the evidence at this point implies domestic segmentation in the Australian market. This is an unsatisfying outcome, considering that the goal of this research is to establish the pricing model that best describes portfolio returns. Given pervasive evidence that liquidity is strongly related to stock returns, the second part of the major analyses derives and incorporates this potentially priced factor to the specified pricing models ... This study also introduces a methodology for individual security analysis, which implements the portfolio analysis, in this part of analyses. The technique makes use of visual impressions conveyed by the histogram plots of coefficients' p-values. A statistically significant coefficient will have its p-values concentrated at below a 5% level of significance; a histogram of p-values will not have a uniform distribution ... The final stage of this study employs daily return data as an examination of what is indeed the best pricing model as well as to provide a robustness check on monthly return results. The daily result indicates that all three US Fama-French factors, namely the US market, size and book-to-market factors as well as LIQT are statistically significant, while the Australian three-factor model only exhibits one significant market factor. This study has discovered that it is in fact the US three-factor model with LIQT and not the domestic model, which qualifies for the criterion of a well-specified asset-pricing model and that it best describes Australian stock returns.
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Magliolo, Jacques. "The relevance and fairness of the JSE ALTX PRE-IPO share pricing methodologies." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1018652.
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This three year indepth study was prompted after a decade of working as a corporate advisor for numerous stockbroking firms' corporate advisory and listing divisions. An overwhelming lack of discernible pricing methodology for IPOs on the JSE's Main Board and failed Venture Capital and Development Capital Markets was transferred to the new Alternative Exchange (AltX). This prompted lengthly discussions with former head of JSE's AltX Noah Greenhill. Such discussions are set out in this dissertation and relate to pricing methodologies and the lack of guidance or legislation as set out in the JSE's schedule 21 of Listing requirements. The focus of this dissertation is thus centred on whether the current adopted methodologies to establish a fair and reasonable pre-IPO share price is effective. To achieve this, global pricing methodologies were assessed within the framework of various valuation techniques used by South African Designated Advisors.
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Milunovich, George Economics Australian School of Business UNSW. "Modelling and valuing multivariate interdependencies in financial time series." Awarded by:University of New South Wales. School of Economics, 2006. http://handle.unsw.edu.au/1959.4/25162.
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This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this
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King, Daniel Jonathan. "Modelling stock return volatility dynamics in selected African markets." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1006452.
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Stock return volatility has been shown to occasionally exhibit discrete structural shifts. These shifts are particularly evident in the transition from ‘normal’ to crisis periods, and tend to be more pronounced in developing markets. This study aims to establish whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected African markets for the period 2002-2012. In the univariate portion of the study, the performances of various Markov-switching models are tested against a single-state benchmark model through the use of in-sample goodness-of-fit and predictive ability measures. In the multivariate context, the single-state and Markov-switching models are comparatively assessed according to their usefulness in constructing optimal stock portfolios. It is found that, even after accounting for structural breaks in the conditional variance process, conventional GARCH effects remain important to capturing the heteroscedasticity evident in the data. However, those univariate models which include a GARCH term are shown to perform comparatively poorly when used for forecasting purposes. Additionally, in the multivariate study, the use of Markov-switching variance-covariance estimates improves risk-adjusted portfolio returns when compared to portfolios that are constructed using the more conventional single-state models. While there is evidence that the use of some Markov-switching models can result in better forecasts and higher risk-adjusted returns than those models which include GARCH effects, the inability of the simpler Markov-switching models to fully capture the heteroscedasticity in the data remains problematic.
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O'Grady, Thomas A. "The profitability of technical analysis and stock returns from a traditional and bootstrap perspective : evidence from Australia, Hong Kong, Malaysia and Thailand." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2012. https://ro.ecu.edu.au/theses/506.
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This research questions whether technical trading rules can help predict stock price movements for a sample of stocks selected from four equity markets from the Asia-Pacific region: Australia, Malaysia, Hong Kong and Thailand for the period 1989-2008. The research is split into two stages. Stage-1 of the research tests the predictability of technical trading rules against a buyand- hold strategy. The variable moving average (VMA), fixed moving average (FMA) and the trading range break (TRB) trading rules are applied to this research. Economic predictability of these rules is examined by comparing returns conditional on a trading rule buy (sell) signal against an unconditional buy-and-hold return. Any existence of excess returns can thus be established. This follows with a statistical analysis of returns using a traditional t-test methodology. Traditional statistical tests assume normally distributed returns with independent observations and a non-changing distribution across time. In Stage-2 of this research a bootstrap checks whether features such as non-normality, time-varying moments and serial correlation bias test statistics. The bootstrap involves assumptions regarding the underlying returns generating process (RGP) and allows returns conditional on a trading rule buy (sell) signal from the original stock price series to be compared with conditional returns simulated from four common null models: RW, AR (1), GARCH-M and E-GARCH models. Simulated p-values are calculated in conjunction with simulated distributions and are applied in lieu of the theoretical normal distribution. Given this process it is possible to infer as to whether non-linear dependencies in returns can be captured by any of the three trading rules. Given the null model output standard t-test outcomes of predictability of technical trading rules may be diminished and/or eliminated. Conclusions are drawn as to the predictability and profitability of the VMA, FMA and TRB trading rules when applied to the chosen stock samples. Findings of this research indicate returns conditional on technical trading rules exceed unconditional buy-and-hold returns for all stocks. Thai sample output indicates strong support in favour of the predictability of standard test results supporting the use of technical trading rules. Output for Australia, Hong Kong and Malaysia indicates that previous standard t-test outcomes of predictability may be diminished and/or eliminated. This implies that the underlying RGP may be characterised by underlying features of some/all of the stochastic models.
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Mnjama, Gladys Susan. "Exchange rate pass-through to domestic prices in Kenya." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1002709.
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In 1993, Kenya liberalised its trade policy and allowed the Kenyan Shillings to freely float. This openness has left Kenya's domestic prices vulnerable to the effects of exchange rate fluctuations. One of the objectives of the Central Bank of Kenya is to maintain inflation levels at sustainable levels. Thus it has become necessary to determine the influence that exchange rate changes have on domestic prices given that one of the major determinants of inflation is exchange rate movements. For this reason, this thesis examines the magnitude and speed of exchange rate pass-through (ERPT) to domestic prices in Kenya. In addition, it takes into account the direction and size of changes in the exchange rates to determine whether the exchange rate fluctuations are symmetric or asymmetric. The thesis uses quarterly data ranging from 1993:Ql - 2008:Q4 as it takes into account the period when the process of liberalization occurred. The empirical estimation was done in two stages. The first stage was estimated using the Johansen (1991) and (1995) co integration techniques and a vector error correction model (VECM). The second stage entailed estimating the impulse response and variance decomposition functions as well as conducting block exogeneity Wald tests. In determining the asymmetric aspect of the analysis, the study followed Pollard and Coughlin (2004) and Webber (2000) frameworks in analysing asymmetry with respect to appreciation and depreciation and large and small changes in the exchange rate to import prices. The results obtained showed that ERPT to Kenya is incomplete but relatively low at about 36 percent in the long run. In terms of asymmetry, the results showed that ERPT is found to be higher in periods of appreciation than depreciation. This is in support of market share and binding quantity constraints theory. In relation to size changes, the results show that size changes have no significant impact on ERPT in Kenya.
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Casas, Villalba Isabel. "Statistical inference in continuous-time models with short-range and/or long-range dependence." University of Western Australia. School of Mathematics and Statistics, 2006. http://theses.library.uwa.edu.au/adt-WU2006.0133.
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The aim of this thesis is to estimate the volatility function of continuoustime stochastic models. The estimation of the volatility of the following wellknown international stock market indexes is presented as an application: Dow Jones Industrial Average, Standard and Poor’s 500, NIKKEI 225, CAC 40, DAX 30, FTSE 100 and IBEX 35. This estimation is studied from two different perspectives: a) assuming that the volatility of the stock market indexes displays shortrange dependence (SRD), and b) extending the previous model for processes with longrange dependence (LRD), intermediaterange dependence (IRD) or SRD. Under the efficient market hypothesis (EMH), the compatibility of the Vasicek, the CIR, the Anh and Gao, and the CKLS models with the stock market indexes is being tested. Nonparametric techniques are presented to test the affinity of these parametric volatility functions with the volatility observed from the data. Under the assumption of possible statistical patterns in the volatility process, a new estimation procedure based on the Whittle estimation is proposed. This procedure is theoretically and empirically proven. In addition, its application to the stock market indexes provides interesting results.
Books on the topic "Stocks Prices Australia Econometric models"
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Lo, Andrew W. Econometric models of limit-order executions. Cambridge, MA: National Bureau of Economic Research, 1997.
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Guidolin, Massimo. Size and value anomalies under regime shifts. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.
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Weil, Philippe. On the possibility of price decreasing bubbles. Cambridge, MA: National Bureau of Economic Research, 1989.
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Grinblatt, Mark. The disposition effect and momentum. Cambridge, MA: National Bureau of Economic Research, 2002.
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Ghysels, Eric. There is a risk-return tradeoff after all. Cambridge, MA: National Bureau of Economic Research, 2004.
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Wright, Jonathan H. Log-periodogram estimation of long memory volatility dependencies with conditionally heavy tailed returns. Washington, D.C: Federal Reserve Board, 2000.
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Santos, Tano. Cash-flow risk, discount risk, and the value premium. Cambridge, Mass: National Bureau of Economic Research, 2005.
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Ghysels, Eric. There is a risk-return tradeoff after all. Cambridge, Mass: National Bureau of Economic Research, 2004.
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Chan, Louis K. C. Momentum strategies. Cambridge, MA: National Bureau of Economic Research, 1995.
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Lin, Wen-Ling. Do bulls and bears move across borders?: International transmission of stock returns and volatility as the world turns. Cambridge, MA: National Bureau of Economic Research, 1991.
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