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.
Self-Excitation, Jump Clustering, Tail behaviors, Parameter Learning, Sequential Bayes Factor, Excess Volatility, Volatility Forecasting, Option Pricing
Fulop, Andras; Li, Junye; and YU, Jun.
Self-Exciting Jumps, Learning, and Asset Pricing Implications. (2014). Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1587
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