A Stochastic Volatility Model with Conditional Skewness

36 Pages Posted: 19 Mar 2008 Last revised: 29 Oct 2012

Date Written: July 1, 2012


We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows current asset returns to be asymmetric conditional on current factors and past information, what we term contemporaneous asymmetry. Conditional skewness is an explicit combination of the conditional leverage effect and contemporaneous asymmetry. We derive analytical formulas for various return moments that are used for generalized method of moments (GMM) estimation. Applying our approach to S\&P500 index daily returns and option data, we show that one- and two-factor SVS models provide a better fit for both the historical and the risk-neutral distribution of returns, compared to existing affine generalized autoregressive conditional heteroskedasticity (GARCH), and stochastic volatility with jumps (SVJ) models. Our results are not due to an overparameterization of the model: the one-factor SVS models have the same number of parameters as their one-factor GARCH competitors and less than the SVJ benchmark.

Keywords: Discrete Time, Affine Model, Conditional Skewness, GMM, Option Pricing

JEL Classification: C1, C5, G1, G12

Suggested Citation

Feunou, Bruno and Tédongap, Roméo, A Stochastic Volatility Model with Conditional Skewness (July 1, 2012). Journal of Business & Economic Statistics, 30:4, 576-591., Available at SSRN: https://ssrn.com/abstract=1109096 or http://dx.doi.org/10.2139/ssrn.1109096

Bruno Feunou (Contact Author)

Bank of Canada ( email )

234 Wellington Street
Ottawa, Ontario K1A 0G9
613-782-8302 (Phone)

HOME PAGE: http://kamkui.net/

Roméo Tédongap

ESSEC Business School ( email )

Avenue Bernard Hirsch
BP 105 Cergy Cedex, 95021
+33134439734 (Phone)
+33134439734 (Fax)

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