Download or read book Real Time Estimation of Multivariate Stochastic Volatility Models written by Jian Wang and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Stochastic Volatility written by Esfandiar Maasoumi and published by . This book was released on 2006 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Stochastic Volatility written by Jón Daníelsson and published by . This book was released on 1995 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Models and Priors for Multivariate Stochastic Volatility written by Eric Jacquier and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Stochastic Volatility Models written by Jón Daníelsson and published by . This book was released on 1996 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Stochastic Volatility written by and published by . This book was released on 2006 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Stochastic Volatility with Dynamic Cross Leverage written by Sebastian Trojan and published by . This book was released on 2014 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Special Issue Multivariate Stochastic Volatility written by Esfandiar Maasoumi and published by . This book was released on 2006 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Stochastic Volatility Models with Correlated Errors written by David X. Chan and published by . This book was released on 2008 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop a Bayesian approach for parsimoniously estimating the correlation structure of the errors in a multivariate stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors is potentially very large, we impose a prior that allows the off-diagonal elements of the inverse of the correlation matrix to be identically zero. The model is estimated using a Markov chain simulation method that samples from the posterior distribution of the volatilities and parameters. We illustrate the approach using both simulated and real examples. In the real examples, the method is applied to equities at three levels of aggregation: returns for firms within the same industry, returns for different industries and returns aggregated at the index level. We find pronounced correlation effects only at the highest level of aggregation.

Download or read book Models and Priors for Multivariate Stochastic Volatility written by Peter E. (Peter Eric) Rossi and published by Montréal : CIRANO. This book was released on 1995 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Stochastic Volatility Models Based on Generalized Fisher Transformation written by Han Chen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimating High Dimensional Multivariate Stochastic Volatility Models written by Matteo Pelagatti and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Inference for Multivariate Stochastic Volatility and Related Models written by Kiriaki Platanioti and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Univariate and Multivariate Stochastic Volatility Models written by Roman Liesenfeld and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A Maximum Likelihood (ML) approach based upon an Efficient Importance Sampling (EIS) procedure is used to estimate several extensions of the standard Stochastic Volatility (SV) model for daily financial return series. EIS provides a highly generic procedure for a very accurate Monte Carlo evaluation of the marginal likelihood which depends upon high-dimensional interdependent integrals. Extensions of the standard SV model being analyzed only require minor modifications in the ML-EIS procedure. Furthermore, EIS can also be applied for filtering which provides the basis for several diagnostic tests. Our empirical analysis indicates that extensions such as a semi-nonparametric specification of the error term distribution in the return equation dominate the standard SV model. Finally, we also apply the ML-EIS approach to a multivariate factor model with stochastic volatility.

Download or read book Multivariate Stochastic Volatility Via Wishart Random Processes written by Alexander Philipov and published by . This book was released on 2004 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial models for asset and derivatives pricing, risk management, portfolio optimization, and asset allocation rely on volatility forecasts. Time-varying volatility models, such as GARCH and Stochastic Volatility (SVOL), have been successful in improving forecasts over constant volatility models. We develop a new multivariate SVOL framework for modeling financial data that assumes covariance matrices stochastically varying through a Wishart process. In our formulation, scalar variances naturally extend to covariance matrices rather than vectors of variances as in traditional SVOL models. Model fitting is performed using Markov chain Monte Carlo simulation from the posterior distribution. Due to the complexity of the model, an efficiently designed Gibbs sampler is described that produces inferences with a manageable amount of computation. Our approach is illustrated on a multivariate time series of monthly industry portfolio returns. In a test of the economic value of our model, minimum-variance portfolios based on our SVOL covariance forecasts outperform out-of-sample portfolios based on alternative covariance models such as Dynamic Conditional Correlations and factor-based covariances.

Download or read book Multivariate Stochastic Volatility Models written by Jun Yu and published by . This book was released on 2004 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shows that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS.

Download or read book Multivariate Stochastic Volatility written by Jón Daníelson and published by . This book was released on 1991 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: