Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture

FRB Atlanta Working Paper Series No. 2012-6

39 Pages Posted: 16 May 2012

See all articles by Mark J. Jensen

Mark J. Jensen

Federal Reserve Bank of Atlanta

John M. Maheu

McMaster University - Michael G. DeGroote School of Business; RCEA

Date Written: April 2012

Abstract

In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional, return-volatility distribution with an infinite mixture of bivariate Normal distributions with mean zero vectors, but having unknown mixture weights and covariance matrices. This semiparametric ASV model nests stochastic volatility models whose innovations are distributed as either Normal or Student-t distributions, plus the response in volatility to unexpected return shocks is more general than the fixed asymmetric response with the ASV model. The unknown mixture parameters are modeled with a Dirichlet process prior. This prior ensures a parsimonious, finite, posterior mixture that best represents the distribution of the innovations and a straightforward sampler of the conditional posteriors. We develop a Bayesian Markov chain Monte Carlo sampler to fully characterize the parametric and distributional uncertainty. Nested model comparisons and out-of-sample predictions with the cumulative marginal-likelihoods, and one-day-ahead, predictive log-Bayes factors between the semiparametric and parametric versions of the ASV model shows the semiparametric model projecting more accurate empirical market returns. A major reason is how volatility responds to an unexpected market movement. When the market is tranquil, expected volatility reacts to a negative (positive) price shock by rising (initially declining, but then rising when the positive shock is large). However, when the market is volatile, the degree of asymmetry and the size of the response in expected volatility is muted. In other words, when times are good, no news is good news, but when times are bad, neither good nor bad news matters with regards to volatility.

Keywords: Bayesian nonparametrics, cumulative Bayes factor, Dirichlet process mixture, infinite mixture model, leverage effect, marginal likelihood, MCMC, non-normal, stochastic volatility, volatility-return relationship

JEL Classification: C11, C14, C53, C58

Suggested Citation

Jensen, Mark J. and Maheu, John M., Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture (April 2012). FRB Atlanta Working Paper Series No. 2012-6, Available at SSRN: https://ssrn.com/abstract=2060671 or http://dx.doi.org/10.2139/ssrn.2060671

Mark J. Jensen (Contact Author)

Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States

John M. Maheu

McMaster University - Michael G. DeGroote School of Business ( email )

1280 Main Street West
Hamilton, Ontario L8S 4M4
Canada

HOME PAGE: http://profs.degroote.mcmaster.ca/ads/maheujm/

RCEA

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Rimini (RN), RN 47900
Italy

HOME PAGE: http://www.rcfea.org/

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