Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture
AbstractIn 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 a 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 bests 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 forecasting 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 45_12.
Date of creation: Jun 2012
Date of revision:
Bayesian nonparametrics; cumulative Bayes factor; Dirichlet process mixture; infinite mixture model; leverage effect; marginal likelihood; MCMC; non-normal; stochastic volatility; volatility-return relationship;
Other versions of this item:
- Mark J. Jensen & John M. Maheu, 2012. "Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture," Working Paper 2012-06, Federal Reserve Bank of Atlanta.
- Mark J Jensen & John M Maheu, 2012. "Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture," Working Papers tecipa-453, University of Toronto, Department of Economics.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-07-08 (All new papers)
- NEP-ETS-2012-07-08 (Econometric Time Series)
- NEP-FOR-2012-07-08 (Forecasting)
- NEP-ORE-2012-07-08 (Operations Research)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
- Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
- Geert Bekaert & Guojun Wu, 1997.
"Asymmetric Volatility and Risk in Equity Markets,"
NBER Working Papers
6022, National Bureau of Economic Research, Inc.
- Sanjiv R. Das & Rangarajan K. Sundaram, 1998.
"Of Smiles and Smirks: A Term-Structure Perspective,"
New York University, Leonard N. Stern School Finance Department Working Paper Seires
98-024, New York University, Leonard N. Stern School of Business-.
- Das, Sanjiv Ranjan & Sundaram, Rangarajan K., 1999. "Of Smiles and Smirks: A Term Structure Perspective," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(02), pages 211-239, June.
- Hentschel, Ludger & Campbell, John, 1992.
"No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns,"
3220232, Harvard University Department of Economics.
- Campbell, John Y. & Hentschel, Ludger, 1992. "No news is good news *1: An asymmetric model of changing volatility in stock returns," Journal of Financial Economics, Elsevier, vol. 31(3), pages 281-318, June.
- John Y. Campbell & Ludger Hentschel, 1991. "No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns," NBER Working Papers 3742, National Bureau of Economic Research, Inc.
- Manabu Asai & Michael McAleer, 2009. "Multivariate stochastic volatility, leverage and news impact surfaces," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 292-309, 07.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Roberto Patuelli).
If references are entirely missing, you can add them using this form.