Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2015
Abstract
The paper proposes a self-exciting asset pricing model that takes into account co-jumps between prices and volatility and self-exciting jump clustering. We employ a 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. We also find that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting, and option pricing.
Keywords
stock-prices, excess volatility, financial-markets, levy processes, predictability, returns, simulation, finite, risk
Discipline
Finance | Finance and Financial Management
Research Areas
Econometrics
Publication
Review of Financial Studies
Volume
28
Issue
3
First Page
876
Last Page
912
ISSN
0893-9454
Identifier
10.1093/rfs/hhu078
Publisher
Oxford University Press
Embargo Period
2-23-2020
Citation
FULOP, Andras; LI, Junye; and YU, Jun.
Self-exciting jumps, learning, and asset pricing implications. (2015). Review of Financial Studies. 28, (3), 876-912.
Available at: https://ink.library.smu.edu.sg/soe_research/2356
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.
Additional URL
https://doi.org/10.1093/rfs/hhu078