{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110614"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110614","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Long-memory stochastic volatility model calibration using deep neural nets","abstract":"Widespread use of stochastic volatility models in the financial industry is bottlenecked by the complexity and intractability they present. Since the seminal work in quantitative finance by Black et al. and Merton, the infamous Black-Scholes model has been extensively used in the industry for vanilla and exotic option pricing. Although the model assumes constant volatility which is not observed in the market, the widespread use is sustained due to its closed-form solution for European vanilla option. However, with the advent of neural networks, stochastic volatility models are becoming increasing tractable. The use of neural networks to learn the expectation function of the underlying stochastic volatility processes for calibration makes application of these more involved stochastic volatility models in the industrial settings practical. This thesis extends this application of neural networks to the calibration of long-memory stochastic volatility (LMSV) models, a class of stochastic volatility models characterized by fractional Brownian motion. The specific challenge with these long-memory models is that they are non-Markovian in nature and simulation can be time-consuming and costly. We show that by using neural networks we can capture these non-Markovian characteristics and quickly calibrate them to ever-evolving market conditions despite their high computational cost.","abstract_html":"Widespread use of stochastic volatility models in the financial industry is bottlenecked by the complexity and intractability they present. Since the seminal work in quantitative finance by Black et al. and Merton, the infamous Black-Scholes model has been extensively used in the industry for vanilla and exotic option pricing. Although the model assumes constant volatility which is not observed in the market, the widespread use is sustained due to its closed-form solution for European vanilla option. However, with the advent of neural networks, stochastic volatility models are becoming increasing tractable. The use of neural networks to learn the expectation function of the underlying stochastic volatility processes for calibration makes application of these more involved stochastic volatility models in the industrial settings practical. This thesis extends this application of neural networks to the calibration of long-memory stochastic volatility (LMSV) models, a class of stochastic volatility models characterized by fractional Brownian motion. The specific challenge with these long-memory models is that they are non-Markovian in nature and simulation can be time-consuming and costly. We show that by using neural networks we can capture these non-Markovian characteristics and quickly calibrate them to ever-evolving market conditions despite their high computational cost.","abstract_has_math":false,"creators":["Masroor, Ahnaf"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Chronopoulou, Alexandra","Milenkovic, Olgica"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T02:34:09Z","date_published":"2021-09-17T02:34:09Z","updated_at":"2026-07-22T22:24:52Z","subjects":["stochastic volatility models","neural network applications","fast calibration","long-memory models","Heston model","European option pricing","volatility model calibration"],"languages":["en"],"rights":["Copyright 2021 Ahnaf Masroor"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110614","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chronopoulou, Alexandra","Milenkovic, Olgica"]},{"key":"dc:creator","label":"Author","values":["Masroor, Ahnaf"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:09Z","2023-09-17T02:34:57Z","2021-04-29","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["stochastic volatility models","neural network applications","fast calibration","long-memory models","Heston model","European option pricing","volatility model calibration"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Ahnaf Masroor"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110614"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Widespread use of stochastic volatility models in the financial industry is bottlenecked by the complexity and intractability they present. Since the seminal work in quantitative finance by Black et al. and Merton, the infamous Black-Scholes model has been extensively used in the industry for vanilla and exotic option pricing. Although the model assumes constant volatility which is not observed in the market, the widespread use is sustained due to its closed-form solution for European vanilla option. However, with the advent of neural networks, stochastic volatility models are becoming increasing tractable. The use of neural networks to learn the expectation function of the underlying stochastic volatility processes for calibration makes application of these more involved stochastic volatility models in the industrial settings practical. This thesis extends this application of neural networks to the calibration of long-memory stochastic volatility (LMSV) models, a class of stochastic volatility models characterized by fractional Brownian motion. The specific challenge with these long-memory models is that they are non-Markovian in nature and simulation can be time-consuming and costly. We show that by using neural networks we can capture these non-Markovian characteristics and quickly calibrate them to ever-evolving market conditions despite their high computational cost.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Ahnaf Masroor, accepted the attached license on 2021-04-29 at 13:27.","The student, Ahnaf Masroor, submitted this Thesis for approval on 2021-04-29 at 13:40.","This Thesis was approved for publication on 2021-04-29 at 15:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15881 on 2021-09-16 at 17:01:25","Made available in DSpace on 2021-09-17T02:34:09Z (GMT). No. of bitstreams: 2 MASROOR-THESIS-2021.pdf: 3577000 bytes, checksum: 14241bc1471fd2a019d68c446f680fe1 (MD5) LICENSE.txt: 4210 bytes, checksum: ad94865f0f2b7b7e10461e446d411ce0 (MD5) Previous issue date: 2021-04-29","Embargo set by: Seth Robbins for item 118457 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Long-memory stochastic volatility model calibration using deep neural nets"]}]}],"canonical_facts":{"dc:contributor":["Chronopoulou, Alexandra","Milenkovic, Olgica"],"dc:creator":["Masroor, Ahnaf"],"dc:date":["2021-09-17T02:34:09Z","2023-09-17T02:34:57Z","2021-04-29","2021-05"],"dc:description":["Widespread use of stochastic volatility models in the financial industry is bottlenecked by the complexity and intractability they present. Since the seminal work in quantitative finance by Black et al. and Merton, the infamous Black-Scholes model has been extensively used in the industry for vanilla and exotic option pricing. Although the model assumes constant volatility which is not observed in the market, the widespread use is sustained due to its closed-form solution for European vanilla option. However, with the advent of neural networks, stochastic volatility models are becoming increasing tractable. The use of neural networks to learn the expectation function of the underlying stochastic volatility processes for calibration makes application of these more involved stochastic volatility models in the industrial settings practical. This thesis extends this application of neural networks to the calibration of long-memory stochastic volatility (LMSV) models, a class of stochastic volatility models characterized by fractional Brownian motion. The specific challenge with these long-memory models is that they are non-Markovian in nature and simulation can be time-consuming and costly. We show that by using neural networks we can capture these non-Markovian characteristics and quickly calibrate them to ever-evolving market conditions despite their high computational cost.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Ahnaf Masroor, accepted the attached license on 2021-04-29 at 13:27.","The student, Ahnaf Masroor, submitted this Thesis for approval on 2021-04-29 at 13:40.","This Thesis was approved for publication on 2021-04-29 at 15:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15881 on 2021-09-16 at 17:01:25","Made available in DSpace on 2021-09-17T02:34:09Z (GMT). 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