Publication Type
Working Paper
Version
publishedVersion
Publication Date
6-2014
Abstract
The paper proposes a self-exciting asset pricing model that takes into account cojumps between prices and volatility and self-exciting jump clustering. We employ a dence of self-exciting jump clustering since the 1987 market crash, and its importance Bayesian learning approach to implement real time sequential analysis. We find evidence of self-exciting jump clustering since the 1987 market crash, and its importance becomes more obvious at the onset of the 2008 global financial crisis. It is found that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting and option pricing.
Keywords
Self-Excitation, Jump Clustering, Tail behaviors, Parameter Learning, Sequential Bayes Factor, Excess Volatility, Volatility Forecasting, Option Pricing
Discipline
Econometrics | Finance and Financial Management
Research Areas
Econometrics
First Page
1
Last Page
52
Publisher
SMU Economics and Statistics Working Paper Series, No. 02-2014
City or Country
Singapore
Citation
FULOP, Andras; LI, Junye; and YU, Jun.
Self-Exciting Jumps, Learning, and Asset Pricing Implications. (2014). 1-52.
Available at: https://ink.library.smu.edu.sg/soe_research/1587
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.
Comments
Published in Review of Financial Studies https://doi.org/10.1093/rfs/hhu078