Bayesian Learning of Impacts of Self-Exciting Jumps in Returns and Volatility

Andras Fulop Author Email:fulop@essec.fr, Junye Li Author Email:li@essec.fr and Jun Yu
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Andras Fulop Author Email:fulop@essec.fr: Finance Department, ESSEC Business School
Junye Li Author Email:li@essec.fr: Finance Department, ESSEC Business School

Working Papers from Singapore Management University, Sim Kee Boon Institute for Financial Economics

Abstract: The paper proposes a new class of continuous-time asset pricing models where negative jumps play a crucial role. Whenever there is a negative jump in asset re- turns, it is simultaneously passed on to diffusion variance and the jump intensity, generating self-exciting co-jumps of prices and volatility and jump clustering. To properly deal with parameter uncertainty and in-sample over-fitting, a Bayesian learning approach combined with an efficient particle filter is employed. It not only allows for comparison of both nested and non-nested models, but also gener- ates all quantities necessary for sequential model analysis. Empirical investigation using S&P 500 index returns shows that volatility jumps at the same time as neg- ative jumps in asset returns mainly through jumps in diffusion volatility. We find substantial evidence for jump clustering, in particular, after the recent financial crisis in 2008, even though parameters driving dynamics of the jump intensity remain difficult to identify.

Keywords: Self-Excitation; Volatility Jump; Jump Clustering; Extreme Events; Parameter Learning; Particle Filters; Sequential Bayes Factor; Risk Man- agement (search for similar items in EconPapers)
JEL-codes: C11 C13 C32 G12 (search for similar items in EconPapers)
Pages: 43 Pages
Date: 2011-12
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Published in SMU-SKBI CoFie Working Paper

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Related works:
Working Paper: Bayesian Learning of Impacts of Self-Exciting Jumps in Returns and Volatility (2012)
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