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A Jump Diffusion Model For Option Pricing


Steven G. Kou


Columbia University - Department of Industrial Engineering and Operations Research (IEOR)

August 2001

AFA 2001 New Orleans Meetings

Abstract:     
Abstract_Content: Brownian motion and normal distribution have been widely used in the Black-Scholes option pricing framework to model the return of assets. However, two puzzles emerge from many empirical investigations: the leptokurtic feature that the return distribution of assets may have a higher peak and two (asymmetric) heavier tails than those of the normal distribution, and an empirical abnormity called "volatility smile'' in option pricing. To incorporate both of them, this paper proposes, for the purpose of option pricing, a double exponential jump diffusion model. The main attraction of the model is its simplicity. In particular, it is simple enough to derive analytical solutions for a variety of option pricing problems, including call and put options, interest rate derivatives and path-dependent options; it seems impossible for many other alternative models to do this. Equilibrium analysis and a psychological interpretation of the model are also presented.

Note: Previously titled: A Jump Diffusion Model for Option Pricing with Three Properties: Leptokurtic Feature, Volatility Smile and Analytical Tractability

Number of Pages in PDF File: 36

Keywords: Contingent claims, high peak, heavy tails, interest rate models, rational expectation, overreaction and underreaction

JEL Classification: G12, G13, C68

working papers series


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Date posted: September 16, 2000  

Suggested Citation

Kou, Steven G., A Jump Diffusion Model For Option Pricing (August 2001). AFA 2001 New Orleans Meetings. Available at SSRN: http://ssrn.com/abstract=242367 or http://dx.doi.org/10.2139/ssrn.242367

Contact Information

Steven G. Kou (Contact Author)
Columbia University - Department of Industrial Engineering and Operations Research (IEOR) ( email )
331 S.W. Mudd Building
500 West 120th Street
New York, NY 10027
United States
212-854-4334 (Phone)
Feedback to SSRN (Beta)


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