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An Empirical Comparison Of Multivariate Garch Models With A Portfolio Management Application For The A Share In Shanghai Stock Exchange
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Book Synopsis An Empirical Comparison of Multivariate GARCH Models with a Portfolio Management Application for the A-share in Shanghai Stock Exchange by :
Download or read book An Empirical Comparison of Multivariate GARCH Models with a Portfolio Management Application for the A-share in Shanghai Stock Exchange written by and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Flexible Multivariate GARCH Modeling with an Application to International Stock Markets by : Olivier Ledoit
Download or read book Flexible Multivariate GARCH Modeling with an Application to International Stock Markets written by Olivier Ledoit and published by . This book was released on 2008 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this paper is to estimate time-varying covariance matrices. Since the covariance matrix of financial returns is known to change through time and is an essential ingredient in risk measurement, portfolio selection, and tests of asset pricing models, this is a very important problem in practice. Our model of choice is the Diagonal-Vech version of the Multivariate GARCH(1,1) model. The problem is that the estimation of the general Diagonal-Vech model model is numerically infeasible in dimensions higher than 5. The common approach is to estimate more restrictive models which are tractable but may not conform to the data. Our contribution is to propose an alternative estimation method that is numerically feasible, produces positive semi-definite conditional covariance matrices, and does not impose unrealistic a priori restrictions. We provide an empirical application in the context of international stock markets, comparing the new estimator to a number of existing ones.
Book Synopsis Multivariate GARCH and Dynamic Copula Models for Financial Time Series by : Martin Grziska
Download or read book Multivariate GARCH and Dynamic Copula Models for Financial Time Series written by Martin Grziska and published by Pro BUSINESS. This book was released on 2015-02-05 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents several non-parametric and parametric models for estimating dynamic dependence between financial time series and evaluates their ability to precisely estimate risk measures. Furthermore, the different dependence models are used to analyze the integration of emerging markets into the world economy. In order to analyze numerous dependence structures and to discover possible asymmetries, two distinct model classes are investigated: the multivariate GARCH and Copula models. On the theoretical side a new dynamic dependence structure for multivariate Archimedean Copulas is introduced which lifts the prevailing restriction to two dimensions and extends the multivariate dynamic Archimedean Copulas to more than two dimensions. On this basis a new mixture copula is presented using the newly invented multivariate dynamic dependence structure for the Archimedean Copulas and mixing it with multivariate elliptical copulas. Simultaneously a new process for modeling the time-varying weights of the mixture copula is introduced: this specification makes it possible to estimate various dependence structures within a single model. The empirical analysis of different portfolios shows that all equity portfolios and the bond portfolios of the emerging markets exhibit negative asymmetries, i.e. increasing dependence during market downturns. However, the portfolio consisting of the developed market bonds does not show any negative asymmetries. Overall, the analysis of the risk measures reveals that parametric models display portfolio risk more precisely than non-parametric models. However, no single parametric model dominates all other models for all portfolios and risk measures. The investigation of dependence between equity and bond portfolios of developed countries, proprietary, and secondary emerging markets reveals that secondary emerging markets are less integrated into the world economy than proprietary. Thus, secondary emerging markets are moresuitable to diversify a portfolio consisting of developed equity or bond indices than proprietary.
Book Synopsis Multistep Predictions for Multivariate GARCH Models by : Jaroslava Hlouskova
Download or read book Multistep Predictions for Multivariate GARCH Models written by Jaroslava Hlouskova and published by . This book was released on 2004 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Application of Multivariate GARCH Models to Turbulent Financial Markets by : Edy Zahnd
Download or read book The Application of Multivariate GARCH Models to Turbulent Financial Markets written by Edy Zahnd and published by . This book was released on 2002 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Correlation and Portfolio Optimization by Multivariate Garch by : Cuicui Luo
Download or read book Stochastic Correlation and Portfolio Optimization by Multivariate Garch written by Cuicui Luo and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling time varying volatility and correlation in financial time series is an important element in derivative pricing, risk management and portfolio management. The main goal of this thesis is to investigate the performance of multivariate GARCH model in stochastic correlation forecast and apply theses techniques to develop a new model to enhance the dynamic portfolio performance in several context, including hedge fund portfolio construction.\\ First, we examine the performance of various univariate GARCH models and regime-switching stochastic volatility models in crude oil market. Then these univariate models discussed are extended to multivariate settings and the empirical evaluation provides evidence on the use of the orthogonal GARCH in correlation forecasting and risk management performance when an equally weighted portfolio is considered. \\ The recent financial turbulence exposed and raised serious concerns about the optimal portfolio selection problem in hedge funds. The dynamic portfolio construction performance of a broad set of multivariate stochastic volatility models is examined in a fund of hedge fund context. It provides further evidence on the use of the orthogonal GARCH in dynamic portfolio constructions and risk management. \\ Further in this work, a new portfolio optimization model is proposed in order to improve the dynamic portfolio performance. We enhance the safety-first model with standard deviation constraint and derive an analytic formula by filtering the returns with GH skewed t distribution and OGARCH. It is found that the proposed model outperforms the classical Mean-Variance model and Mean-CVAR model during financial crisis period for a fund of hedge fund.
Book Synopsis Forecasting Daily Stock Volatility Using GARCH Model by : Sasikanta Tripathy
Download or read book Forecasting Daily Stock Volatility Using GARCH Model written by Sasikanta Tripathy and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling and forecasting the volatility of stock markets has been one of the major topics in financial econometrics in recent years. Based on the daily closing value of 23 years data, an average of 5,605 observations, for both Sensex and Shanghai Stock Exchange Composite Index, this paper makes an attempt to fit appropriate GARCH model to estimate the conditional market volatility for both Bombay Stock Exchange (BSE) and Shanghai Stock Exchange (SSE), respectively. The empirical results demonstrate that there are significant ARCH effects in both the stock markets, and it is appropriate to use the GARCH model to estimate the process.
Book Synopsis Artificial Intelligence in Asset Management by : Söhnke M. Bartram
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Book Synopsis Multistep Predictions for Multivariate GARCH Models by : Jaroslava Hlouskova
Download or read book Multistep Predictions for Multivariate GARCH Models written by Jaroslava Hlouskova and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Comparison of the Performances of Linear and Nonlinear GARCH Models in Forecasting Volatility of Shanghai's A- & B-share Markets by : Hu Bo
Download or read book Comparison of the Performances of Linear and Nonlinear GARCH Models in Forecasting Volatility of Shanghai's A- & B-share Markets written by Hu Bo and published by . This book was released on 2003 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter Variability in the Single Factor Market Model by : Johan Knif
Download or read book Parameter Variability in the Single Factor Market Model written by Johan Knif and published by . This book was released on 1989 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multivariate GARCH Models and Black-litterman Approach for Tracking Error Constrained Portfolios: an Empirical Analysis by : Giulio Palomba
Download or read book Multivariate GARCH Models and Black-litterman Approach for Tracking Error Constrained Portfolios: an Empirical Analysis written by Giulio Palomba and published by . This book was released on 2006 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Flexible multivariate GARCH modeling with an application to international stock markets[ by : Olivier Ledoit
Download or read book Flexible multivariate GARCH modeling with an application to international stock markets[ written by Olivier Ledoit and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH by : Robert F. Engle
Download or read book Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH written by Robert F. Engle and published by . This book was released on 2001 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we develop the theoretical and empirical properties of a new class of multi-variate GARCH models capable of estimating large time-varying covariance matrices, Dynamic Conditional Correlation Multivariate GARCH. We show that the problem of multivariate conditional variance estimation can be simplified by estimating univariate GARCH models for each asset, and then, using transformed residuals resulting from the first stage, estimating a conditional correlation estimator. The standard errors for the first stage parameters remain consistent, and only the standard errors for the correlation parameters need be modified. We use the model to estimate the conditional covariance of up to 100 assets using S&P 500 Sector Indices and Dow Jones Industrial Average stocks, and conduct specification tests of the estimator using an industry standard benchmark for volatility models. This new estimator demonstrates very strong performance especially considering ease of implementation of the estimator
Book Synopsis Analysis of Value at Risk Models Based on the Shanghai Stock Index by :
Download or read book Analysis of Value at Risk Models Based on the Shanghai Stock Index written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last few years, Value at Risk has been universally accepted as a measure of market risk in financial institutions. A lot of research has been done in the field of Value at Risk leading to the development of differing approaches to estimate Value at Risk. However each method has its own set of assumptions and there is very little consensus on the preferred method to estimate Value at Risk. Since all existing methods involve some tradeoff and simplifications, determining the best methodology for estimating Value at Risk becomes an empirical question for implementing the most suitable model. The challenge of this work is to come up with the best and easily implementable approach suitable to Shanghai Stock index data and apply time series models for calculating Value at Risk and compare their performance with current models. Several sketches of current methods are introduced with open issues associated with each method. The study identifies the path for future research to improve the performance of models. The Value at Risk models are evaluated over the two sample periods. The two periods serve to validate the performance of models over time. The best models (EWMA and GARCH) models were reevaluated for the extended forecast sample period and it was found that GARCH models performed consistently over the time. The study makes use of both parametric and non parametric models and also proposes some of the models to estimate Value at Risk. Performance evaluation of the risk metrics, Garch models and historical simulation Value at Risk models are outlined and assumptions tested on Shanghai stock exchange index. The risk metrics and the Garch models incorporate volatility updating as well as clustering phenomenon. It does a poor job in capturing the extreme tail region as compared to historical simulation models. On the other hand historical simulation models capture the tail of the empirical distribution, but are practically insensitive to periods of sudden volatility. Time series models fail to reject the random walk hypothesis and perform poorly in comparison to the current model .Overall the risk metrics model with decay factor of 0.90 performs better than all other models when comparing the accuracy of Value at Risk estimates in first sample period. However over the both forecast sample periods the GARCH models perform consistently. The performance of EWMA marginally deteriorates for the second sample period. It is felt that the conditioning on the past movement of the stock or assert in the previous period will significantly improve the performance of current Value at Risk models. The movements on the positive side should produce less volatility than the movements of equal magnitude on the negative side. This can be taken care of by conditioning of variance of returns on the direction of movement of asset.
Book Synopsis Multistep Prediction for Multivariate GARCH Models by : Jaroslava Hlouskova
Download or read book Multistep Prediction for Multivariate GARCH Models written by Jaroslava Hlouskova and published by . This book was released on 2004 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Application of GARCH Models for Modeling Stock Market Volatility by : Shabarisha N.
Download or read book Application of GARCH Models for Modeling Stock Market Volatility written by Shabarisha N. and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Return is the major attribute of an investment asset which can be construed as a random variable, and the 'variability in return' can be interpreted as volatility. Forecasting volatility and modeling it are the most prolific areas for research. This paper empirically investigates the conditional variance (volatility) pattern in Indian stock market based on financial time series data that consists of daily closing prices of CNX Nifty 50 market index for 10 years from April 2006 to March 2016. For the purpose of estimating conditional variance (volatility) in the daily returns of the index, Autoregressive Conditional Heteroskedasticity (ARCH) models are employed. Both symmetric and asymmetric models are used to capture stylized facts about CNX Nifty 50 market index returns such as volatility clustering and leverage effect. The findings of the study show that the asymmetric models are a better fit than symmetric models, confirming the presence of volatility clustering and leverage effect.
