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The Maximum Entropy Distribution of an Asset Inferred from Option Prices

Published online by Cambridge University Press:  06 April 2009

Peter W. Buchen
Affiliation:
School of Mathematics and Statistics, University of Sydney, Australia
Michael Kelly
Affiliation:
Department of Mathematical Sciences, University of Western Sydney, Macarthur, Australia

Abstract

This paper describes the application of the Principle of Maximum Entropy to the estimation of the distribution of an underlying asset from a set of option prices. The resulting distribution is least committal with respect to unknown or missing information and is, hence, the least prejudiced. The maximum entropy distribution is the only information about the asset that can be inferred from the price data alone. An extension to the Principle of Minimum Cross-Entropy allows the inclusion of prior knowledge of the asset distribution. We show that the maximum entropy distribution is able to accurately fit a known density, given simulated option prices at different strikes.

Type
Research Article
Copyright
Copyright © School of Business Administration, University of Washington 1996

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References

Agmon, N.; Alhassid, Y.; and Levine, R. D.. “An Algorithm for Determining the Lagrange Parameters in the Maximal Entropy Formalism.” In The Maximum Entropy Formalism, Levine, R. D. and Tribus, M., eds. Cambridge, MA: MIT Press (1981), 207209.Google Scholar
Amin, K. I., and Ng, V. K.. “Option Valuation with Systematic Stochastic Volatility.” Journal of Finance, 48 (1993), 881910.CrossRefGoogle Scholar
Breeden, D. T., and Litzenberger, R. H.. “Price of State-Contingent Claims Implicit in Option Prices.” Journal of Business, 51 (1978), 621651.CrossRefGoogle Scholar
Brennan, M.The Pricing of Contingent Claims in Discrete-Time Models.” Journal of Finance, 34 (1979), 5368.CrossRefGoogle Scholar
Cover, T. M., and Joy, T. A.. Elements of Information Theory. New York, NY: John Wiley and Sons (1991).Google Scholar
Derman, E., and Kani, I.. “Riding on a Smile.” Risk, 7 (02 1994), 3239.Google Scholar
Dupire, B.Pricing and Hedging with Smiles.” Risk, 7 (01 1994), 1820.Google Scholar
Derman, E., and Kani, I.. “Arbitrage Pricing with Stochastic Volatility.” Working Paper, Proceedings of AFFI Conference, Paris (06 1992).Google Scholar
Jarrow, R., and Rudd, A.. “Approximate Option Valuation for Arbitrary Stochastic Processes.” Journal of Financial Economics, 10 (1982), 347369.CrossRefGoogle Scholar
Jaynes, E. T.Information Theory and Statistical Mechanics.” Physics Reviews, 106 (1957), 620630.CrossRefGoogle Scholar
Jaynes, E. T. “Where do We Stand on Maximum Entropy?” In The Maximum Entropy Formalism, Levine, R. D. and Tribus, M., eds. Cambridge, MA: MIT Press (1979), 115118.Google Scholar
Jaynes, E. T.On the Rationale of Maximum-Entropy Methods.” Proceedings of the IEEE, 70 (1982), 939952.CrossRefGoogle Scholar
Longstaff, F. A. “Option Pricing and the Martingale Restriction.” Finance Working Paper No. 8–94, Univ. of California (05 1994).Google Scholar
Rubinstein, M. “Implied Binomial Tree.” Finance Working Paper No. 232, Univ. of California (01 1994).Google Scholar
Shannon, C. E.The Mathematical Theory of Communication.” Bell Systems Technical Journal, 27 (1948), 379423.CrossRefGoogle Scholar
Shimko, D.Bounds of Probability.” Risk, 6 (1990), 3337.Google Scholar