A Bayesian analysis of market information linkages among NAFTA countries using a multivariate stochastic volatility model

2009 ◽  
Vol 35 (2) ◽  
pp. 123-148 ◽  
Author(s):  
Petra Fleischer ◽  
Ross Maller ◽  
Gernot Müller
Keyword(s):  
Bayesian Analysis ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Market Information ◽  
Volatility Model ◽  
Multivariate Stochastic Volatility ◽  
Information Linkages

scholarly journals Cross-Commodity Spot Price Modeling with Stochastic Volatility and Leverage For Energy Markets

2013 ◽  
Vol 45 (02) ◽  
pp. 545-571 ◽  
Author(s):  
F. E. Benth ◽  
L. Vos
Keyword(s):  
Stochastic Volatility ◽  
Simulation Method ◽  
Energy Markets ◽  
Stochastic Volatility Model ◽  
Spot Price ◽  
Leverage Effect ◽  
Order Structure ◽  
Spot Prices ◽  
Volatility Model ◽  
Multivariate Stochastic Volatility

Spot prices in energy markets exhibit special features, such as price spikes, mean reversion, stochastic volatility, inverse leverage effect, and dependencies between the commodities. In this paper a multivariate stochastic volatility model is introduced which captures these features. The second-order structure and stationarity of the model are analyzed in detail. A simulation method for Monte Carlo generation of price paths is introduced and a numerical example is presented.

scholarly journals Dynamic Correlation Multivariate Stochastic Volatility Black-Litterman With Latent Factors

2021 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Mihnea S. Andrei ◽  
Sujit K. Ghosh ◽  
Jian Zou
Keyword(s):  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Mcmc Algorithm ◽  
Latent Factors ◽  
Dynamic Correlation ◽  
Confidence Levels ◽  
Volatility Models ◽  
Volatility Model ◽  
Multivariate Stochastic Volatility ◽  
Modeling Volatility

In finance, it is often of interest to study market volatility for portfolios that may consist of a large number of assets using multivariate stochastic volatility models. However, such models, though useful, do not usually incorporate investor views that might be available. In this paper we introduce a novel hierarchical Bayesian methodology of modeling volatility for a large portfolio of assets that incorporates investor’s personal views of the market via the Black-Litterman (BL) model. We extend the scope and use of BL models by using it within a multivariate stochastic volatility model based on latent factors for dimensionality reduction but allows for time varying correlations. Detailed derivations of MCMC algorithm are provided with an illustration with S&P500 asset returns. Moreover, sensitivity analysis for the confidence levels that the investor has in their personal views is also explored. Numerical results show that the proposed method provides flexible interpretation based on the investor’s uncertainty in personal beliefs, and converges to the empirical sample estimate when their confidence level of the market becomes weak.

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