Academic literature on the topic 'Stocks Prices Australia Mathematical models'
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Journal articles on the topic "Stocks Prices Australia Mathematical models"
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Hua, Jie, Maolin Huang, and Chengshun Huang. "Centrality Metrics’ Performance Comparisons on Stock Market Datasets." Symmetry 11, no. 7 (July 15, 2019): 916. http://dx.doi.org/10.3390/sym11070916.
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The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.
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Chen, You-Shyang, Chih-Lung (Jerome) Chou, Yau-Jung (Mike) Lee, Su-Fen Chen, and Wen-Ju Hsiao. "Identifying Stock Prices Using an Advanced Hybrid ARIMA-Based Model: A Case of Games Catalogs." Axioms 11, no. 10 (September 24, 2022): 499. http://dx.doi.org/10.3390/axioms11100499.
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At the beginning of 2020, the COVID-19 pandemic struck the world, affecting the pace of life and the economic behavioral patterns of people around the world, with an impact exceeding that of the 2008 financial crisis, causing a global stock market crash and even the first recorded negative oil prices. Under the impact of this pandemic, due to the global large-scale quarantine and lockdown measures, game stocks belonging to the stay-at-home economy have become the focus of investors from all over the world. Therefore, under such incentives, this study aims to construct a set of effective prediction models for the price of game stocks, which could help relevant stakeholders—especially investors—to make efficient predictions so as to achieve a profitable investment niche. Moreover, because stock prices have the characteristics of a time series, and based on the relevant discussion in the literature, we know that ARIMA (the autoregressive integrated moving average) prediction models have excellent prediction performance. In conclusion, this study aims to establish an advanced hybrid model based on ARIMA as an excellent prediction technology for the price of game stocks, and to construct four groups of different investment strategies to determine which technical models of investment strategies are suitable for different game stocks. There are six important directions, experimental results, and research findings in the construction of advanced models: (1) In terms of the experiment, the data are collected from the daily closing prices of game-related stocks on the Taiwan Stock Exchange, and the sample range is from 2014 to 2020. (2) In terms of the performance verification, the return on investment is used as the evaluation standard to verify the availability of the ARIMA prediction model. (3) In terms of the research results, the accuracy of the model in predicting the prices of listed stocks can reach the 95% confidence interval predicted by the model 14 days after the closing price, and the OTC stocks fall within the 95% confidence interval for 3 days. (4) In terms of the empirical study of the rate of return, the investors can obtain a better rate of return than the benchmark strategy by trading the game stocks based on the indices set by the ARIMA model in this study. (5) In terms of the research findings, this study further compares the rate of return of trading strategies with reference to the ARIMA index and the rate of return of trading strategies with reference to the monitoring indicator, finding no significant difference between the two. (6) Different game stocks apply for different technical models of investment strategies.
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Hidayana, Rizki Apriva, Herlina Napitupulu, and Jumadil Saputra. "Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks." Operations Research: International Conference Series 2, no. 3 (September 4, 2021): 62–66. http://dx.doi.org/10.47194/orics.v2i3.176.
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Stocks are common investments that are in great demand by investors. Stocks are also an investment instrument that provides returns but tends to be riskier. The return time series is easier to handle than the price time series. In investment activities, there are the most important components, namely volatility and risk. All financial evaluations require accurate volatility predictions. Volatility is identical to the conditional standard deviation of stock price returns. The most frequently used risk calculation is Value-at-Risk (VaR). Mathematical models can be used to predict future stock prices, the model that will be used is the Glosten Jagannathan Runkle-generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The purpose of this study was to determine the value of the risk obtained by using the time series model. GJR-GARCH is a development of GARCH by including the leverage effect. The effect of leverage is related to the concept of asymmetry. Asymmetry generally arises because of the difference between price changes and value volatility. The method used in this study is a literature and experimental study through secondary data simulations in the form of daily data from BCA shares and BNI shares. Data processing by looking at the heteroscedasticity of the data, then continued by using the GARCH model and seeing whether there is an asymmetry in the data. If there is an asymmetric effect on the processed data, then it is continued by using the GJR-GARCH model. The results obtained on the two stocks can be explained that the analyzed stock has a stock return volatility value for the leverage effect because the GJR-GARCH coefficient value is > 0. So, the risk value obtained by using VaR measurements on BCA stocks is 0.047247 and on BNI stocks. is 0.037355. Therefore, the ARMA-GJR-GARCH model is good for determining the value of stock risk using VaR.
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Zhou, Dehui. "Financial Market Prediction and Simulation Based on the FEPA Model." Journal of Mathematics 2021 (December 26, 2021): 1–11. http://dx.doi.org/10.1155/2021/5955375.
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Since the birth of the financial market, the industry and academia want to find a method to accurately predict the future trend of the financial market. The ultimate goal of this paper is to build a mathematical model that can effectively predict the short-term trend of the financial time series. This paper presents a new combined forecasting model: its name is Financial Time Series-Empirical Mode Decomposition-Principal Component Analysis-Artificial Neural Network (FEPA) model. This model is mainly composed of three components, which are based on financial time series special empirical mode decomposition (FTA-EMD), principal component analysis (PCA), and artificial neural network. This model is mainly used to model and predict the complex financial time series. At the same time, the model also predicts the stock market index and exchange rate and studies the hot fields of the financial market. The results show that the empirical mode decomposition back propagation neural network (EMD-BPNN) model has better prediction effect than the autoregressive comprehensive moving average model (ARIMA), which is mainly reflected in the accuracy of prediction. This shows that the prediction method of decomposing and recombining nonlinear and nonstationary financial time series can effectively improve the prediction accuracy. When predicting the closing price of Australian stock index, the hit rate (DS) of the FEPA model decomposition method is 72.22%, 10.86% higher than the EMD-BPNN model and 3.23% higher than the EMD-LPP-BPNN model. When the FEPA model predicts the Australian stock index, the hit rate is improved to a certain extent, and the effect is better than other models.
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Ogwuche, O. I., M. R. Odekunle, and M. O. Egwurube. "A Stochastic Model of the Dynamics of Change in Stock Price." NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES 6 (December 28, 2015): 99–105. http://dx.doi.org/10.46912/napas.14.
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The solutions of many mathematical models resulting in stochastic differential equations are based on the assumption that the drift and the volatility coefficients were linear functions of the solutions. We formulated a model whose basic parameters could be derived from observations over discretized time intervals rather than the assumption that the drift and the volatility coefficients were linear functions of the solutions. We took into consideration the possibility of an asset gaining, losing or stable in a small interval of time instead of the assumption of the Binomial Asset pricing models that posited that the price could appreciate by a factor p or depreciate by a factor 1-p. A multi-dimensional stochastic differential equation was obtained whose drift is the expectation vector and the volatility the covariance of the stocks with respect to each other. The resulting system of stochastic differential equations was solved numerically using the Euler Maruyama Scheme for multi-dimensional stochastic differential equations through the use of a computer program written in MatLab. We obtained a realization of the evolutions of their prices over a chosen interval of time
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Robson, Edward, and Vinayak V. Dixit. "Constructing a Database for Computable General Equilibrium Modeling of Sydney, Australia, Transport Network." Transportation Research Record: Journal of the Transportation Research Board 2606, no. 1 (January 2017): 54–62. http://dx.doi.org/10.3141/2606-07.
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In the search for benefits to justify transport projects, economic appraisals have increasingly incorporated the valuation of impacts to the wider economy. Computable general equilibrium (CGE) models provide a framework to estimate these impacts by simulating the interactions of urban economies and transport networks. In CGE models, households and firms are represented by microeconomic behavioral functions, and markets adjust according to prices. As markets both inside and outside the transport network are taken into account, a wide variety of measures that can assist in economic appraisals can be extracted. However, urban CGE models are computationally burdensome and require detailed, spatially disaggregate data. This paper discusses the methodology used to develop a database, including an input–output table, for the calibration of an urban CGE model for Sydney, Australia. Official and publicly available data sources were manipulated by using a number of mathematical and statistical techniques to compile a table for 249 regions and 20 sectors across Sydney. Issues, such as determining the appropriate level of aggregation, generating incomplete data, and managing conflicting data, that other input–output table developers may encounter when constructing multiregional tables were addressed in the study. The table entries themselves were mapped and explored, as they provide a useful study of the spatial economy of Sydney. Future work will focus on streamlining the construction of input–output tables and incorporating new data sources.
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Duppati, Geeta, and Mengying Zhu. "Oil prices changes and volatility in sector stock returns: Evidence from Australia, New Zealand, China, Germany and Norway." Corporate Ownership and Control 13, no. 2 (2016): 351–70. http://dx.doi.org/10.22495/cocv13i2clp4.
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The paper examines the exposure of sectoral stock returns to oil price changes in Australia, China, Germany, New Zealand and Norway over the period 2000-2015 using weekly data drawn from DataStream. The issue of volatility has important implications for the theory of finance and as is well-known accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management (e.g. in the calculation of hedge ratios and Value-at-Risk measures), and trading strategies (David and Ruiz, 2009). This study adopts GARCH and EGARCH to understand the relationship between the returns and volatility. The findings using GARCH (EGARCH) models suggests that in the case of Germany eight (nine) out of ten sectors returns can be explained by the volatility of past oil price in Germany, while in the case of Australia, six (seven) out of ten sector returns are sensitive to the oil price changes with the exception of Industrials, Consumer Goods, Health care and Utilities. While in China and New Zealand five sectors are found sensitive to oil price changes and three sectors in Norway, namely Oil & Gas, Consumer Services and Financials. Secondly, this paper also investigated the exposure of the stock returns to oil price changes using market index data as a proxy using GARCH or EGARCH model. The results indicated that the stock returns are sensitive to the oil price changes and have leverage effects for all the five countries. Further, the findings also suggests that sector with more constituents is likely to have leverage effects and vice versa. The results have implications to market participants to make informed decisions about a better portfolio diversification for minimizing risk and adding value to the stocks.
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Lalwani, Vaibhav, and Madhumita Chakraborty. "Multi-factor asset pricing models in emerging and developed markets." Managerial Finance 46, no. 3 (December 2, 2019): 360–80. http://dx.doi.org/10.1108/mf-12-2018-0607.
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Purpose The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets. Design/methodology/approach The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R2). Findings The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable. Originality/value Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.
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Manickavasagam, Jeevananthan, and Visalakshmi S. "An investigational analysis on forecasting intraday values." Benchmarking: An International Journal 27, no. 2 (October 4, 2019): 592–605. http://dx.doi.org/10.1108/bij-11-2018-0361.
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Purpose The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results. Design/methodology/approach This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data. Findings MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models. Practical implications Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market. Originality/value This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.
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Volontyr, L., and L. Mykhalchyshyna. "Organizational and economic mechanism of grain sales: information component." Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies 21, no. 92 (May 11, 2019): 81–89. http://dx.doi.org/10.32718/nvlvet-e9213.
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A significant part of the output of the agro-industrial complex of Ukraine is exported. Therefore, it is desirable to determine the optimal volume of products to be implemented each month. Prices for grain are formed depending on demand and supply, costs for production and sale, market fees, etc. The analysis of the price situation on the Ukrainian cities shows a large variation. The average price of 1 kg of grain crops does not give a full opportunity to characterize the price situation of the Ukrainian grain market. There is seasonal price cyclicality: their growth with the decrease of stocks and the reduction after harvesting, when mass sales of grain are carried out by producers who are not able to store the grown crops, and consumers make grain crops. In the article the solution of the economic-mathematical model of optimization of the calendar plan for the sale of agricultural products is developed and found. The model is considered from the standpoint of deterministic product prices and under the probabilistic nature of future market prices. The system of restrictions consists of two constraints: to determine the optimal size of grain crop harvesting of each type and the capacity of the warehouse. If future market prices are considered not deterministic, then the commodity producer always has the risk of receiving in the future revenue from the sale of products smaller than expected. A risk-averse person will be guided by two criteria when deciding to: maximize the expected total net income and minimize the dispersion of total net income. In this case, the model will be two-criterial and nonlinear. The method of supporting the process of determining the predominance of multi-criteria optimization is that the owner first of all has received information about the limits of the variation of the expected total net income and the standard deviation of income on the set of effective options for the calendar plan. The peculiarities of the individual attitude to risk are calculated by drawing information on the permissible levels of the indicated criterion. Further among all effective variants of the calendar plan of realization is calculated precisely the one that best reflects the individual predominance of the owner of the product. The following information is needed to construct a numerical model for grain sales: sales prices and the cost of storing 1 ton of grain crops to a certain month. The predicted values are based on a simple linear econometric model based on statistical sampling. The reliability of the econometric model is determined by the determination coefficient or on the basis of Fisher's F-criterion according to the theory of statistical hypotheses. Econometric models have weak extropolitic properties, so the forecast can be formed only short-term. The solution of the model showed: all kinds of grain crops, except for barley, are economically unprofitable to be implemented in such months as January, May, June, July and August. Wheat grades 3 and 6, corn is also unprofitable to be sold in September. Unlike other crops, barley is beneficial throughout the year. In February, the maximum sales of wheat is 2, 3 and 6 classes, in March the maximum sale of barley, and the minimum is in May. Maize has the maximum sales in May, and the minimum in September. The minimum sale of wheat depends on its class – September, April and December respectively 2, 3 and 6 classes. With such incomplete loading of warehouses, the profit from storage of grain crops will be 743 thousand. UAH. Thus, PJSC “Gnivan Grain Reciprocal Enterprise” is more likely to load its warehouses to improve its financial position. One of the ways of solving the problem of seasonal grain sales is to create a network of modern certified grain elevators, taking into account the logistically rational location, which will allow to keep enough grain in addition and of the proper quality. This will allow an increase in the efficiency of grain producers through the sale of grain at favorable market conditions in a wider range of time. Independent operators should also be encouraged to ensure that the quality of the grain is objectively measured. At present, the analysis of the work of the grain storage system shows that the high cost of services of active elevators is also a problem.
Dissertations / Theses on the topic "Stocks Prices Australia Mathematical models"
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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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Cheng, Lap-yan, and 鄭立仁. "Extension of price-trend models with applications in finance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B37428408.
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董森 and Sen Dong. "Two essays on idiosyncratic volatility of stock markets." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31225937.
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Wei, Yong, and 卫勇. "The real effects of S&P 500 Index additions: evidence from corporate investment." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B4490681X.
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Wang, Hanfeng, and 王漢鋒. "Essays on stock trading volume, volatility and information." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38826185.
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Wang, Yintian 1976. "Three essays on volatility long memory and European option valuation." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102851.
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This dissertation is in the form of three essays on the topic of component and long memory GARCH models. The unifying feature of the thesis is the focus on investigating European index option evaluation using these models.The first essay presents a new model for the valuation of European options. In this model, the volatility of returns consists of two components. One of these components is a long-run component that can be modeled as fully persistent. The other component is short-run and has zero mean. The model can be viewed as an affine version of Engle and Lee (1999), allowing for easy valuation of European options. The model substantially outperforms a benchmark single-component volatility model that is well established in the literature. It also fits options better than a model that combines conditional heteroskedasticity and Poisson normal jumps. While the improvement in the component model's performance is partly due to its improved ability to capture the structure of the smirk and the path of spot volatility, its most distinctive feature is its ability to model the term structure. This feature enables the component model to jointly model long-maturity and short-maturity options.
The second essay derives two new GARCH variance component models with non-normal innovations. One of these models has an affine structure and leads to a closed-form option valuation formula. The other model has a non-affine structure and hence, option valuation is carried out using Monte Carlo simulation. We provide an empirical comparison of these two new component models and the respective special cases with normal innovations. We also compare the four component models against GARCH(1,1) models which they nest. All eight models are estimated using MLE on S&P500 returns. The likelihood criterion strongly favors the component models as well as non-normal innovations. The properties of the non-affine models differ significantly from those of the affine models. Evaluating the performance of component variance specifications for option valuation using parameter estimates from returns data also provides strong support for component models. However, support for non-normal innovations and non-affine structure is less convincing for option valuation.
The third essay aims to investigate the impact of long memory in volatility on European option valuation. We mainly compare two groups of GARCH models that allow for long memory in volatility. They are the component Heston-Nandi GARCH model developed in the first essay, in which the volatility of returns consists of a long-run and a short-run component, and a fractionally integrated Heston-Nandi GARCH (FIHNGARCH) model based on Bollerslev and Mikkelsen (1999). We investigate the performance of the models using S&P500 index returns and cross-sections of European options data. The component GARCH model slightly outperforms the FIGARCH in fitting return data but significantly dominates the FIHNGARCH in capturing option prices. The findings are mainly due to the shorter memory of the FIHNGARCH model, which may be attributed to an artificially prolonged leverage effect that results from fractional integration and the limitations of the affine structure.
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Mazzotta, Stefano. "Three essays on volatility." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85189.
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This dissertation is in the form of one survey paper and three essays on the topic of volatility. The unifying feature that permeates the entire thesis is the focus on the measurement and use of conditional second moment of equities and currencies as a measure of risk for asset pricing and policy purposes in the context of international markets.The survey examines selected papers from the international finance literature and from the volatility literature with a focus on the theoretical and empirical relationship between first and second unconditional and conditional moments of domestic and international asset returns. It then specifically proposes several areas for investigation related to international finance topics. The first essay investigates the importance of asymmetric volatility when computing the risk premium of international assets. The results indicate that conditional second moment asymmetry is significant and time-varying. They also show that, if the price of risk is time-varying, the world market and foreign exchange risk premia estimated without allowing for time-varying asymmetry are less consistent with the data. Furthermore, they imply that asymmetry is more pronounced when the business condition is such that investors require higher compensation to bear risk.
In the second essay we start from the consideration that financial decision makers often consider the information in currency option valuations when making assessments about future exchange rates. The purpose of this essay is then to systematically assess the quality of option based volatility, interval and density forecasts. We use a unique dataset consisting of over 10 years of daily data on over-the-counter currency option prices. We find that the implied volatilities explain a large share of the variation in realized volatility. Finally, we find that wide-range interval and density forecasts are often misspecified whereas narrow-range interval forecasts are well specified.
In the third essay we examine whether the information contained in various measures of correlation among exchange rates can be used to assess future currency co-movement. We compare option-implied correlation forecasts from a dataset consisting of over 10 years of daily data on over-the-counter currency option prices to a set of return-based correlation measures and assess the relative quality of the correlation forecasts. We find that while the predictive power of implied correlation is not always superior to that of returns based correlations measures, it tends to provide the most consistent results across currencies. Predictions that use both implied and returns-based correlations generate the highest adjusted R2's, explaining up to 42 per cent of the realized correlations.
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關惠貞 and Wai-ching Josephine Kwan. "Trend models for price movements in financial markets." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31211513.
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Wong, Chun-mei May, and 王春美. "The statistical tests on mean reversion properties in financial markets." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31211975.
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Luo, Yan, and 罗妍. "Three essays on noise and institutional trading." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44549246.
Full textBooks on the topic "Stocks Prices Australia Mathematical models"
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E, Allen D. The relationship between stock prices and dividends: Evidence from the Australian stock market. Perth, W.A: Edith Cowan University, Faculty of Business, School of Economics and Finance, 1996.
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Allen, D. E. Excess volatility and the short run modelling of Australian stock prices. Perth, W.A: Edith Cowan University, Faculty of Business, School of Economics and Finance, 1996.
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Eckhold, Kelly R. Bank asset valuation and risk in Australasia: The market's evaluation. [Wellington]: Reserve Bank of New Zealand, 1994.
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The informational role of prices. Cambridge, Mass: MIT Press, 1989.
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Aktienkurse und dynamische Makroökonomik: Aktienkursentwicklungen in makroökonomischen Modellen geschlossener sowie offener Volkswirtschaften : eine dynamische kapitalmarkttheoretische Analyse. Frankfurt am Main: P. Lang, 1994.
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Pindyck, Robert S. Do stock prices move together too much? Cambridge, MA: National Bureau of Economic Research, 1990.
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P, Dwyer Gerald, and Hafer R. W, eds. The stock market: Bubbles, volatility, and chaos : proceedings of the Thirteenth Annual Economic Policy Conference of the Federal Reserve Bank of St. Louis. Boston: Kluwer Academic Publishers, 1990.
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Scott, Louis O. A little bit of evidence on the intertemporal dependence in the volatility of stock prices. [Urbana]: College of Commerce and Business Administration,University of Illinois at Urbana-Champaign, 1985.
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Dumas, Bernard. Equilibrium portfolio strategies in the presence of sentiment risk and excess volatility. Cambridge, MA: National Bureau of Economic Research, 2007.
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Scott, Louis O. The present value model of stock prices: Regression tests and Monte Carlo results. [Urbana]: College of Commerce and Business Administration,University of Illinois at Urbana-Champaign, 1985.
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