{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:kent1365439051"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:kent1365439051","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"Pricing of Swing Options: A Monte Carlo Simulation Approach","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Leow, Kai-Siong"],"institution":"Kent State University","degree_name":"PHD","degree_level":"doctoral","degree_discipline":"College of Arts and Sciences / Department of Mathematical Sciences","degree_department":null,"school":null,"contributors":["Mocioalca, Oana"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-04-16","date_published":"2013-04-16","updated_at":"2026-07-24T03:37:31Z","subjects":["Applied Mathematics","swing options","energy derivatives","stochastic dynamic programming","simulation"],"languages":["English"],"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."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=kent1365439051","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mocioalca, Oana"]},{"key":"dc:creator","label":"Author","values":["Leow, Kai-Siong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-04-16"]},{"key":"dc:publisher","label":"Institution","values":["Kent State University / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["College of Arts and Sciences / Department of Mathematical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["PHD"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Kent State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Applied Mathematics","swing options","energy derivatives","stochastic dynamic programming","simulation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. 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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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","p.110","1.37 MB"]},{"key":"dc:title","label":"Title","values":["Pricing of Swing Options: A Monte Carlo Simulation Approach"]}]}],"canonical_facts":{"dc:contributor":["Mocioalca, Oana"],"dc:creator":["Leow, Kai-Siong"],"dc:date":["2013-04-16"],"dc:description":["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."],"dc:format":["application/pdf","p.110","1.37 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=kent1365439051"],"dc:language":["English"],"dc:publisher":["Kent State University / OhioLINK"],"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."],"dc:subject":["Applied Mathematics","swing options","energy derivatives","stochastic dynamic programming","simulation"],"dc:title":["Pricing of Swing Options: A Monte Carlo Simulation Approach"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["College of Arts and Sciences / Department of Mathematical Sciences"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["PHD"],"thesis:institution_name":["Kent State University"]},"updated_at":"2026-07-24T03:37:31Z"}