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A Stochastic Volatility Model with Conditional Skewness

Bruno Feunou () and Roméo Tedongap

Staff Working Papers from Bank of Canada

Abstract: 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 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) 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.

Keywords: Asset Pricing; Econometric and statistical methods (search for similar items in EconPapers)
JEL-codes: C1 C5 G1 G12 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2011
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:11-20

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