Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture

Mark Jensen and John Maheu

Journal of Econometrics, 2014, vol. 178, issue P3, 523-538

Abstract: We extend the asymmetric, stochastic, volatility model by modeling the return-volatility distribution nonparametrically. The novelty is modeling this distribution with an infinite mixture of Normals, where the mixture unknowns have a Dirichlet process prior. Cumulative Bayes factors show our semiparametric model accurately forecasting market returns. During tranquil markets, expected volatility rises (declines, then rises as the shock increases) when the market shock is negative (positive). This asymmetry is muted when the market is volatile. In other words, when times are good, no news is good news, but during bad times, neither good nor bad news matters with regards to volatility.

Keywords: Bayesian nonparametrics; Dirichlet process mixture; Leverage effect; Stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C14 C53 C58 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Related works:
Working Paper: Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture (2012)
Working Paper: Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:178:y:2014:i:p3:p:523-538

DOI: 10.1016/j.jeconom.2013.08.018

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