{"id":{"repo_id":"toronto-retro","oai_identifier":"oai:utoronto.scholaris.ca:1807/109011"},"canonical_url":"https://search.dev.ndltd.org/etd/toronto-retro/oai:utoronto.scholaris.ca:1807/109011","repository":{"repo_id":"toronto-retro","name":"University of Toronto","base_url":"https://utoronto.scholaris.ca/server/oai/request"},"display":{"title":"Ambiguity Aversion in Commodity Markets","abstract":"This thesis explores the impact of model uncertainty on commodity market models. The commodity markets include various idiosyncratic uncertainties that are not present in other markets, and this thesis is the first to provide a thorough and detailed analysis of their effects. We assume that agents acknowledge the possibility of model misspecification, adopt the framework of ambiguity aversion to protect themselves, and utilize a robust indifference pricing framework for valuation and hedging. Most exotic financial options written on commodities are valued by discretizing continuous-time and state models. Thus, in the first part of this thesis, we study the impact of ambiguity aversion in discrete-time and state using trinomial trees. We develop a general mathematical framework for incorporating model uncertainty, compare it with existing methodologies, and look at practical examples, including the valuation of swing options with model uncertainty. Next, we consider the continuous-time formulation of model uncertainty by investigating option pricing with both stochastic volatility and Levy jumps. We provide analytical characterization of ambiguity, robust prices, and resulting hedging strategies and provide perturbative approximations when analytical methods fail. A key insight is that the effect of ambiguity aversion is the strongest at-the-money and the weakest deep-in- or deep out-of-the-money. Finally, we study electricity interconnectors from the perspective of statistical arbitrage with model uncertainty. We provide closed-form optimal strategies when there is no ambiguity and obtain perturbative approximations of the robust optimal strategies. We illustrate the efficacy of the resulting strategies on simulated data. We propose a calibration algorithm on jump-diffusion data to bridge the abstract mathematical theory and real-world dynamics.","abstract_html":"This thesis explores the impact of model uncertainty on commodity market models. The commodity markets include various idiosyncratic uncertainties that are not present in other markets, and this thesis is the first to provide a thorough and detailed analysis of their effects. We assume that agents acknowledge the possibility of model misspecification, adopt the framework of ambiguity aversion to protect themselves, and utilize a robust indifference pricing framework for valuation and hedging. Most exotic financial options written on commodities are valued by discretizing continuous-time and state models. Thus, in the first part of this thesis, we study the impact of ambiguity aversion in discrete-time and state using trinomial trees. We develop a general mathematical framework for incorporating model uncertainty, compare it with existing methodologies, and look at practical examples, including the valuation of swing options with model uncertainty. Next, we consider the continuous-time formulation of model uncertainty by investigating option pricing with both stochastic volatility and Levy jumps. We provide analytical characterization of ambiguity, robust prices, and resulting hedging strategies and provide perturbative approximations when analytical methods fail. A key insight is that the effect of ambiguity aversion is the strongest at-the-money and the weakest deep-in- or deep out-of-the-money. Finally, we study electricity interconnectors from the perspective of statistical arbitrage with model uncertainty. We provide closed-form optimal strategies when there is no ambiguity and obtain perturbative approximations of the robust optimal strategies. We illustrate the efficacy of the resulting strategies on simulated data. We propose a calibration algorithm on jump-diffusion data to bridge the abstract mathematical theory and real-world dynamics.","abstract_has_math":false,"creators":["qin, zhen"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Statistics","school":null,"contributors":[],"advisors":["Jaimungal, Sebastian SJ"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-11","date_published":"2021-11","updated_at":"2026-07-27T21:28:01Z","subjects":["Ambiguity Aversion","Commodity Markets","Model Uncertainty","Stochastic Control"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1807/109011","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Jaimungal, Sebastian SJ"]},{"key":"dc:contributor.department","label":"Department","values":["Statistics"]},{"key":"dc:creator","label":"Author","values":["qin, zhen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-11"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-11-30T18:15:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-11-30T18:15:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-11"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ambiguity Aversion","Commodity Markets","Model Uncertainty","Stochastic Control"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1807/109011"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis explores the impact of model uncertainty on commodity market models. The commodity markets include various idiosyncratic uncertainties that are not present in other markets, and this thesis is the first to provide a thorough and detailed analysis of their effects. We assume that agents acknowledge the possibility of model misspecification, adopt the framework of ambiguity aversion to protect themselves, and utilize a robust indifference pricing framework for valuation and hedging. Most exotic financial options written on commodities are valued by discretizing continuous-time and state models. Thus, in the first part of this thesis, we study the impact of ambiguity aversion in discrete-time and state using trinomial trees. We develop a general mathematical framework for incorporating model uncertainty, compare it with existing methodologies, and look at practical examples, including the valuation of swing options with model uncertainty. Next, we consider the continuous-time formulation of model uncertainty by investigating option pricing with both stochastic volatility and Levy jumps. We provide analytical characterization of ambiguity, robust prices, and resulting hedging strategies and provide perturbative approximations when analytical methods fail. A key insight is that the effect of ambiguity aversion is the strongest at-the-money and the weakest deep-in- or deep out-of-the-money. Finally, we study electricity interconnectors from the perspective of statistical arbitrage with model uncertainty. We provide closed-form optimal strategies when there is no ambiguity and obtain perturbative approximations of the robust optimal strategies. We illustrate the efficacy of the resulting strategies on simulated data. We propose a calibration algorithm on jump-diffusion data to bridge the abstract mathematical theory and real-world dynamics."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Ambiguity Aversion in Commodity Markets"]}]}],"canonical_facts":{"dc:contributor.advisor":["Jaimungal, Sebastian SJ"],"dc:contributor.department":["Statistics"],"dc:creator":["qin, zhen"],"dc:date":["2021-11"],"dc:date.accessioned":["2021-11-30T18:15:21Z"],"dc:date.available":["2021-11-30T18:15:21Z"],"dc:date.issued":["2021-11"],"dc:description.abstract":["This thesis explores the impact of model uncertainty on commodity market models. The commodity markets include various idiosyncratic uncertainties that are not present in other markets, and this thesis is the first to provide a thorough and detailed analysis of their effects. We assume that agents acknowledge the possibility of model misspecification, adopt the framework of ambiguity aversion to protect themselves, and utilize a robust indifference pricing framework for valuation and hedging. Most exotic financial options written on commodities are valued by discretizing continuous-time and state models. Thus, in the first part of this thesis, we study the impact of ambiguity aversion in discrete-time and state using trinomial trees. We develop a general mathematical framework for incorporating model uncertainty, compare it with existing methodologies, and look at practical examples, including the valuation of swing options with model uncertainty. Next, we consider the continuous-time formulation of model uncertainty by investigating option pricing with both stochastic volatility and Levy jumps. We provide analytical characterization of ambiguity, robust prices, and resulting hedging strategies and provide perturbative approximations when analytical methods fail. A key insight is that the effect of ambiguity aversion is the strongest at-the-money and the weakest deep-in- or deep out-of-the-money. Finally, we study electricity interconnectors from the perspective of statistical arbitrage with model uncertainty. We provide closed-form optimal strategies when there is no ambiguity and obtain perturbative approximations of the robust optimal strategies. We illustrate the efficacy of the resulting strategies on simulated data. We propose a calibration algorithm on jump-diffusion data to bridge the abstract mathematical theory and real-world dynamics."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["http://hdl.handle.net/1807/109011"],"dc:subject":["Ambiguity Aversion","Commodity Markets","Model Uncertainty","Stochastic Control"],"dc:title":["Ambiguity Aversion in Commodity Markets"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T21:28:01Z"}