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
Citation
FULOP, Andras; LI, Junye; and YU, Jun.
Bayesian Learning of Impacts of Self-Exciting Jumps in Returns and Volatility. (2012). 1-43.
Available at: https://ink.library.smu.edu.sg/soe_research/1325
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.