Swing Option Pricing by Dynamic Programming with B-spline Density Projection

International Journal of Theoretical and Applied Finance, 2019

36 Pages Posted: 16 Oct 2019 Last revised: 1 Feb 2020

See all articles by Justin Kirkby

Justin Kirkby

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE)

Shijie Deng

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE)

Date Written: Seb 6, 2017

Abstract

Swing options are a type of exotic financial derivative which generalize American options to allow for multiple early-exercise actions during the contract period. These contracts are widely traded in commodity and energy markets, but are often difficult to value using standard techniques due to their complexity and strong path-dependency. There are numerous interesting varieties of swing options, which differ in terms of their intermediate cash flows, and the constraints (both local and global) which they impose on early-exercise (swing) decisions.
We introduce an efficient and general purpose transform-based method for pricing discrete and continuously monitored swing options under exponential L\'evy models, which applies to contracts with fixed rights clauses, as well as recovery time delays between exercise. The approach combines dynamic programming with an efficient method for calculating the continuation value between monitoring dates, and applies generally to multiple early-exercise contracts, providing a unified framework for pricing a large class of exotic derivatives. Efficiency and accuracy of the method is supported by a series of numerical experiments which further provide benchmark prices for future research.

Keywords: Swing Option, early-exercise, option pricing, Levy process, American Option, Fourier

Suggested Citation

Kirkby, Justin and Deng, Shijie, Swing Option Pricing by Dynamic Programming with B-spline Density Projection (Seb 6, 2017). International Journal of Theoretical and Applied Finance, 2019. Available at SSRN: https://ssrn.com/abstract=3464984 or http://dx.doi.org/10.2139/ssrn.3464984

Justin Kirkby (Contact Author)

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE) ( email )

765 Ferst Drive
Atlanta, GA 30332-0205
United States

Shijie Deng

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE) ( email )

765 Ferst Drive
Atlanta, GA 30332-0205
United States

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