Stochastic Volatility Models for Asset Returns with Leverage, Skewness and Heavy-Tails via Scale Mixture

Jingzhi Huang and Li Xu ()
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Li Xu: Management Science and Engineering, Stanford University, Stanford, CA 94305, USA

Quarterly Journal of Finance (QJF), 2014, vol. 04, issue 03, 1-31

Abstract: We propose and estimate a new class of equity return models that incorporate scale mixtures of the skew-normal distribution for the error distribution into the standard stochastic volatility framework. The main advantage of our models is that they can simultaneously accommodate the skewness, heavy-tailedness, and leverage effect of equity index returns observed in the data. The proposed models are flexible and parsimonious, and include many asymmetrically heavy-tailed error distributions — such as skew-tand skew-slash distributions — as special cases. We estimate a variety of specifications of our models using the Bayesian Markov Chain Monte Carlo method, with data on daily returns of the S&P 500 index over 1987–2009. We find that the proposed models outperform existing ones of index returns.

Keywords: Stochastic volatility models; skewness; heavy-tailedness; Monte Carlo Markov chain; Bayesian analysis; model selection; JEL Classifications: C15; JEL Classifications: C11; JEL Classifications: G12 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S2010139214500116

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