Self-Exciting Jumps, Learning, and Asset Pricing Implications

Self-Exciting Jumps, Learning, and Asset Pricing Implications

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...

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Bibliographic Details
Main Authors: FULOP, Andras, LI, Junye, YU, Jun
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
Subjects:
Self-Excitation
Jump Clustering
Tail behaviors
Parameter Learning
Sequential Bayes Factor
Excess Volatility
Volatility Forecasting
Option Pricing
Econometrics
Finance and Financial Management
Online Access:https://ink.library.smu.edu.sg/soe_research/1587
https://ink.library.smu.edu.sg/context/soe_research/article/2586/viewcontent/02_2014.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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