Publication Type

Working Paper

Version

publishedVersion

Publication Date

1-2012

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 returns, 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 generates all quantities necessary for sequential model analysis. Empirical investigation using S&P 500 index returns shows that volatility jumps at the same time as negative 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 Management

Discipline

Econometrics

Research Areas

Econometrics

First Page

1

Last Page

43

Publisher

SMU Economics and Statistics Working Paper Series, No. 03-2012

City or Country

Singapore

Copyright Owner and License

Authors

Included in

Econometrics Commons

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