Pricing of Swing Options: A Monte Carlo Simulation Approach

We study the problem of pricing swing options, a class of multiple early exercise options that are traded in energy market, particularly in the electricity and natural gas markets. These contracts permit the option holder to periodically exercise the right to trade a variable amount of energy with a counterparty, subject to local volumetric constraints. In addition, the total amount of energy traded from settlement to expiration with the counterparty is restricted by a global volumetric constraint. Violation of this global volumetric constraint is allowed but would lead to penalty settled at expiration.The pricing problem is formulated as a stochastic optimal control problem in discrete time and state space. We present a stochastic dynamic programming algorithm which is based on piecewise linear concave approximation of value functions. This algorithm yields the value of the swing option under the assumption that the optimal exercise policy is applied by the option holder. We present a proof of an almost sure convergence that the algorithm generates the optimal exercise strategy as the number of iterations approaches to infinity. Finally, we provide a numerical example for pricing a natural gas swing call option.

Name thesis:degree_name

PHD

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

College of Arts and Sciences / Department of Mathematical Sciences

Grantor dc:publisher

Kent State University

Year dc:date

2013

Author dc:creator

• Leow, Kai-Siong

Contributors dc:contributor

• Mocioalca, Oana

• Applied Mathematics
• swing options
• energy derivatives
• stochastic dynamic programming
• simulation

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=kent1365439051

OAI identifier oai:identifier

oai:etd.ohiolink.edu:kent1365439051