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

Copyright Owner and License

Authors

Comments

Published in Review of Financial Studies https://doi.org/10.1093/rfs/hhu078

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