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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2014
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sg-smu-ink.soe_research-25862019-04-19T08:49:29Z Self-Exciting Jumps, Learning, and Asset Pricing Implications FULOP, Andras LI, Junye YU, Jun 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. 2014-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1587 https://ink.library.smu.edu.sg/context/soe_research/article/2586/viewcontent/02_2014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Self-Excitation Jump Clustering Tail behaviors Parameter Learning Sequential Bayes Factor Excess Volatility Volatility Forecasting Option Pricing Econometrics Finance and Financial Management |
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Self-Excitation Jump Clustering Tail behaviors Parameter Learning Sequential Bayes Factor Excess Volatility Volatility Forecasting Option Pricing Econometrics Finance and Financial Management |
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Self-Excitation Jump Clustering Tail behaviors Parameter Learning Sequential Bayes Factor Excess Volatility Volatility Forecasting Option Pricing Econometrics Finance and Financial Management FULOP, Andras LI, Junye YU, Jun Self-Exciting Jumps, Learning, and Asset Pricing Implications |
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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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FULOP, Andras LI, Junye YU, Jun |
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FULOP, Andras LI, Junye YU, Jun |
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FULOP, Andras |
title |
Self-Exciting Jumps, Learning, and Asset Pricing Implications |
title_short |
Self-Exciting Jumps, Learning, and Asset Pricing Implications |
title_full |
Self-Exciting Jumps, Learning, and Asset Pricing Implications |
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Self-Exciting Jumps, Learning, and Asset Pricing Implications |
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Self-Exciting Jumps, Learning, and Asset Pricing Implications |
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self-exciting jumps, learning, and asset pricing implications |
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Institutional Knowledge at Singapore Management University |
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2014 |
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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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