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Activity Number:
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100
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 7, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #306404 |
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Title:
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Bayesian Inference for Derivative Prices
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Author(s):
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Jonathan Stroud*+ and Nicholas Polson
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Companies:
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University of Pennsylvania and The University of Chicago
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Address:
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The Wharton School, Philadelphia, PA, 19104,
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Keywords:
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stochastic volatility ; jump diffusion ; optimal filtering ; particle filtering ; option pricing ; stock returns
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Abstract:
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This paper develops a methodology for parameter and state variable inference using both asset and derivative price information. We combine theoretical pricing models and asset dynamics to generate a joint posterior for parameters and state variables and provide an MCMC strategy for inference. There are several advantages of our inferential approach. First, more precise parameter estimates are obtained when both asset and derivative price information are used. Second, we provide a diagnostic tool for model misspecification based on agreement of the state and parameter estimates with and without derivative price information. Furthermore, the time series properties of the state variables also can be used to evaluate model fit. We illustrate our methodology using daily equity index options on the S&P 500 index from 1998--2002.
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