A Multivariate Stochastic Volatility Model Applied to a Panel of S&P500 Stocks in Different Industries
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Date
2017
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
We estimate a multivariate stochastic volatility model for a panel of stock returns for a number of S&P 500 firms from different industries. To directly compare our results with those from the univariate estimation literature on the same data, we use an efficient importance sampling (EIS) method to estimate the likelihood function of the given multivariate system that we analyze. As opposed to univariate methods where each return is estimated separately for each firm, our results are based on joint estimation that can account for potential common error term interactions based on industry characteristics that cannot be detected by univariate methods. Our results reveal that there are important differences in the industry effects, something that suggests that differential gains to portfolio allocations in the different industries that we examine. There are differences because of idiosyncratic factors and the common industry factors that suggest that each industry requires a separate treatment in arriving at portfolio allocations.
Description
Keywords
Dynamic Factor Models, Variance
Journal or Series
International Review of Finance
WoS Q Value
Q4
Scopus Q Value
Q2
Volume
17
Issue
3
