Author: Joshua ChanPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Multivariate Stochastic Volatility with Co-heteroscedasticity
Stochastic Volatility
Author: Neil ShephardPublisher: Oxford University Press, USA
ISBN: 0199257205
Category : Business & Economics
Languages : en
Pages : 534
Book Description
Stochastic volatility is the main concept used in the fields of financial economics and mathematical finance to deal with time-varying volatility in financial markets. This work brings together some of the main papers that have influenced this field, andshows that the development of this subject has been highly multidisciplinary.
Models and Priors for Multivariate Stochastic Volatility
Author: Eric JacquierEstimating High Dimensional Multivariate Stochastic Volatility Models
Author: Matteo PelagattiReal Time Estimation of Multivariate Stochastic Volatility Models
Author: Jian WangMultivariate Stochastic Volatility
Author: Esfandiar MaasoumiMultivariate Stochastic Volatility Models with Correlated Errors
Author: David X. ChanPublisher:
ISBN:
Category :
Languages : en
Pages : 31
Book Description
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.
Multivariate Stochastic Volatility Models
Author: Jón DaníelssonModeling Stochastic Volatility with Application to Stock Returns
Author: Mr.Noureddine KrichenePublisher: International Monetary Fund
ISBN: 1451854846
Category : Business & Economics
Languages : en
Pages : 30
Book Description
A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was consistent with both volatility clustering and mean reversion. Filtering showed highly volatile markets, reflecting frequent pertinent news. Diagnostics showed no model failure, although specification improvements were always possible. The model corroborated stylized findings in volatility modeling and has potential value for market participants in asset pricing and risk management, as well as for policymakers in the design of macroeconomic policies conducive to less volatile financial markets.
Multivariate Stochastic Volatility Via Wishart Random Processes
Author: Alexander PhilipovPublisher:
ISBN:
Category :
Languages : en
Pages : 57
Book Description
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.