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Department of Finance and Tax

Gram-Charlier expansions and option pricing

Abstract

dc:description.abstract

Gram-Charlier expansions provide a tractable way of fitting risk-neutral distributions to asset prices. This allows the model to capture skewness, excess kurtosis and higher moments in observed asset returns. Schlogl (2013) proposes a calibration method to ensure the fitted densities are valid and arbitrage free. This method is implemented with standard foreign exchange options and gives an exact fit when enough moments are included in the calibration process. GramCharlier expansions also result in analytic solutions for many exotic option prices through an extremely general framework. This relies on representing an option as a portfolio of the M-binaries defined by Skipper and Buchen (2003). Geometric Asian options are priced using this approach and compared to the corresponding Black-Scholes prices. Numerical examples highlight the effect skewness and excess kurtosis can have on these option prices, particularly for options that are out-the-money. Gram-Charlier distributions are also combined with Monte Carlo simulations to estimate option prices for calls and geometric Asian options. The results show convergence to the analytical solutions for all cases. Additionally, Gram-Charlier estimates for arithmetic Asian options are calculated and compared to Black-Scholes estimates.

Degree

thesis:*
Grantor
Department of Finance and Tax
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Knipe, Joshua
Advisors dc:contributor.advisor
  • Ouwehand, Peter
  • Mc Walter, Thomas

Subjects

dc:subject × 1

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/11427/36472
OAI identifier oai:identifier
oai:open.uct.ac.za:11427/36472

Chain of custody

source
Harvested from
University of Cape Town
Base URL
open.uct.ac.za/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Knipe, Joshua. Gram-Charlier expansions and option pricing. Department of Finance and Tax, 2022. http://hdl.handle.net/11427/36472