A Multivariate Stochastic Volatility Model Applied to a Panel of S&P500 Stocks in Different Industries

Date01 September 2017
AuthorThanasis Stengos,Serda S. Öztürk
Published date01 September 2017
A Multivariate Stochastic Volatility
Model Applied to a Panel of S&P500
Stocks in Different Industries
Istanbul Bilgi University, Istanbul, Turkey and
Economics and Finace, University of Guelph, Guelph, Canada
We estimate a multivariate stochastic volatility model for a panel of stock
returns for a number of S&P 500 rms from different industries. To directly
compare our results with those from the univariate estimation literature on
the same data, we use an efcient importance sampling (EIS) method to esti-
mate the likelihood function of the given multivariate system that we analyze.
As opposed to univariate methods where each return is estimated separately
for each rm, 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.
JEL Codes: G170; C150
Asset volatility has been an area of intense research in nance and nancial
econometrics in the last three decades. The seminal work of Engle (1982) who
introduced the ARCH-type models has been one of the main vehicles to study
and model volatility as a conditional moment of interest that needs estimating
in the same way as the conditional mean that denes the typical regression func-
tion needs to be estimated. In their generalized (GARCH) form, these models
assume that conditional variances are functions of past variances and/or past
squared returns. At the same time, stochastic volatility (SV) models, introduced
by Taylor (1982, 1994), offer an important alternative to ARCH-type models
and are the focus of our paper. Under SV, volatility follows a stochastic process
generally independent of the conditional mean process. In GARCH-type models,
the conditional variance of returns is assumed to be a deterministic function of
past returns, whereas in SV models the volatility process is random. The
© 2017 International Review of Finance Ltd. 2017
International Review of Finance, 17:3, 2017: pp. 479490
DOI: 10.1111/ir.12111

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT