OPTION PRICING WITH HEAVY-TAILED DISTRIBUTIONS OF LOGARITHMIC RETURNS

Lasko Basnarkov, Viktor Stojkoski, Zoran Utkovski () and Ljupco Kocarev
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Lasko Basnarkov: Faculty of Computer Science and Engineering, SS. Cyril and Methodius University, Skopje, Macedonia2Macedonian Academy of Sciences and Arts, Skopje, Macedonia
Zoran Utkovski: Fraunhofer Heinrich Hertz Institute, Berlin, Germany
Ljupco Kocarev: Faculty of Computer Science and Engineering, SS. Cyril and Methodius University, Skopje, Macedonia2Macedonian Academy of Sciences and Arts, Skopje, Macedonia

International Journal of Theoretical and Applied Finance (IJTAF), 2019, vol. 22, issue 07, 1-35

Abstract: A growing body of literature suggests that heavy tailed distributions represent an adequate model for the observations of log returns of stocks. Motivated by these findings, here, we develop a discrete time framework for pricing of European options. Probability density functions of log returns for different periods are conveniently taken to be convolutions of the Student’s t-distribution with three degrees of freedom. The supports of these distributions are truncated in order to obtain finite values for the options. Within this framework, options with different strikes and maturities for one stock rely on a single parameter — the standard deviation of the Student’s t-distribution for unit period. We provide a study which shows that the distribution support width has weak influence on the option prices for certain range of values of the width. It is furthermore shown that such family of truncated distributions approximately satisfies the no-arbitrage principle and the put-call parity. The relevance of the pricing procedure is empirically verified by obtaining remarkably good match of the numerically computed values by our scheme to real market data.

Keywords: Asset pricing; option pricing; heavy-tailed distributions; truncated distributions (search for similar items in EconPapers)
Date: 2019
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Working Paper: Option Pricing with Heavy-Tailed Distributions of Logarithmic Returns (2019)
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DOI: 10.1142/S0219024919500419

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