Quantitative Finance

Quantitative Finance
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Published by: Taylor & Francis

Editor(s): Jim Gatheral - Baruch College, The City University of New York, USA and Michael Dempster - Centre for Mathematical Sciences, University of Cambridge

The frontiers of finance are shifting rapidly, driven in part by the increasing use of quantitative methods in the field. Quantitative Finance welcomes original research articles that reflect the dynamism of this area. The journal provides an interdisciplinary forum for presenting both theoretical and empirical approaches and offers rapid publication of original new work with high standards of quality. The readership is broad, embracing researchers and practitioners across a range of specialisms and within a variety of organizations. All articles should aim to be of interest to this broad readership.

  • Optimal operation of a hydropower plant in a stochastic environment
    By Isabel Figuerola-FerrettiEduardo SchwartzIgnacio Segarra† ICADE and Center for Low Carbon Hydrogen Studies, Universidad Pontificia Comillas, Madrid, Spain‡ Beedie School of Business, Simon Fraser University, Vancouver, Canada§ Instituto de Investigación Tecnológica (IIT), Universidad Pontificia Comillas, Madrid, Spain
  • Tail risk aversion and backwardation of index futures
    By Jufang LiangDan YangQian Han† School of Finance and Statistics, Hunan University, 109 Shijiachong Road, Yuelu District, Changsha, Hunan 410079, People's Republic of China‡ Lingnan College, Sun Yat-Sen University, 135 Xingang Xi Road, Haizhu District, Guangzhou, Guangdong 510275, People's Republic of China
  • Narrative triggers of information sensitivity
    By Kim RistolainenDepartment of Economics, Turku School of Economics, University of Turku, FI-20014, Turku, Finland
  • Deep calibration with random grids
    By Fabio BaschettiGiacomo BormettiPietro Rossi† Scuola Normale Superiore, Pisa, Italy‡ Department of Mathematics, University of Bologna, Bologna, Italy§ Prometeia S.p.A., Bologna, Italy¶ Department of Statistical Sciences, University of Bologna, Bologna, Italy
  • A modified CTGAN-plus-features-based method for optimal asset allocation
    By José-Manuel Pe naFernando SuárezOmar LarréDomingo RamírezArturo Cifuentes† Fintual Administradora General de Fondos S.A. Santiago, Chile, Fintual, Inc.‡ Clapes UC, Pontificia Universidad Católica de Chile, Santiago, Chile
  • Interactions between monetary and macroprudential policies
    By Gustavo Libório Rocha LimaRegis Augusto ElyDaniel Oliveira Cajueiro† Department of Economics, University of Brasilia, Brasilia, Brazil‡ Department of Economics, Federal University of Pelotas, Pelotas, Brazil§ National Institute of Science and Technology for Complex Systems (INCT-SC), Rio de Janeiro, Brazil¶ Machine Learning Laboratory in Finance and Organizations (LAMFO), Brasilia, Brazil
  • Interactions between monetary and macroprudential policies
    By Gustavo Libório Rocha LimaRegis Augusto ElyDaniel Oliveira Cajueiro† Department of Economics, University of Brasilia, Brasilia, Brazil‡ Department of Economics, Federal University of Pelotas, Pelotas, Brazil§ National Institute of Science and Technology for Complex Systems (INCT-SC), Rio de Janeiro, Brazil¶ Machine Learning Laboratory in Finance and Organizations (LAMFO), Brasilia, Brazil
  • 15 years of Adjoint Algorithmic Differentiation (AAD) in finance
    By Luca CapriottiMike Giles† Department of Mathematics & Department of Industrial Engineering and Operations Research, Columbia University, New York, NY, 10027, USA‡ Mathematical Institute, Oxford University Mathematical Institute, Woodstock Road, Oxford, OX2 6GG, UK
  • 15 years of Adjoint Algorithmic Differentiation (AAD) in finance
    By Luca CapriottiMike Giles† Department of Mathematics & Department of Industrial Engineering and Operations Research, Columbia University, New York, NY, 10027, USA‡ Mathematical Institute, Oxford University Mathematical Institute, Woodstock Road, Oxford, OX2 6GG, UK
  • Asymptotics for short maturity Asian options in jump-diffusion models with local volatility
    By Dan PirjolLingjiong Zhu† School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA‡ Department of Mathematics, Florida State University, 1017 Academic Way, Tallahassee, FL 32306, USA
  • Asymptotics for short maturity Asian options in jump-diffusion models with local volatility
    By Dan PirjolLingjiong Zhu† School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA‡ Department of Mathematics, Florida State University, 1017 Academic Way, Tallahassee, FL 32306, USA
  • GPT's idea of stock factors
    By Yuhan ChengKe Tang† School of Management, Shandong University, Jinan, China‡ Institute of Economics, Tsinghua University, Beijing, China
  • GPT's idea of stock factors
    By Yuhan ChengKe Tang† School of Management, Shandong University, Jinan, China‡ Institute of Economics, Tsinghua University, Beijing, China
  • A static replication approach for callable interest rate derivatives: mathematical foundations and efficient estimation of SIMM–MVA
    By J. H. HoencampS. JainB. D. Kandhai† Computational Science Lab, University of Amsterdam, Science Park 904, Amsterdam 1098XH, Netherlands‡ Department of Management Studies, Indian Institute of Science, Bangalore, India§ Quantitative Analytics, ING Bank, Foppingadreef 7, Amsterdam, 1102 BD, Netherlands
  • A static replication approach for callable interest rate derivatives: mathematical foundations and efficient estimation of SIMM–MVA
    By J. H. HoencampS. JainB. D. Kandhai† Computational Science Lab, University of Amsterdam, Science Park 904, Amsterdam 1098XH, Netherlands‡ Department of Management Studies, Indian Institute of Science, Bangalore, India§ Quantitative Analytics, ING Bank, Foppingadreef 7, Amsterdam, 1102 BD, Netherlands
  • Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear
    By Aurélien AlfonsiStefano De Marcoa CERMICS, Ecole des Ponts ParisTechb CMAP, Ecole Polytechnique, Institut Polytechnique de ParisAurélien Alfonsi is professor at Ecole des Ponts ParisTech, part-time professor at Ecole Polytechnique, and deputy director of the CERMICS. He is the head of the master of financial mathematics at Ecole des Ponts. He is member of the Inria MathRisk project-team and involved in the chairs “Financial Risks” (Ecole des Ponts, Ecole Polytechnique, Sorbonne Université and Société Générale) and “Future of Quantitative Finance” (Ecole des Ponts, Université Paris Cité and BNP Paribas). His research brings on mathematical finance, insurance and numerical methods in probability.Stefano De Marco is Associate Professor in Probability and Mathematical Finance at Ecole Polytechnique, Paris. He owns a PhD in applied mathematics from Scuola Normale Superiore di Pisa and Université Paris-Est. He is a member of the steering committee of the Chaire Stress Test (Ecole Polytechnique and BNP Paribas) and he has taken part in the works of the Chaire Risques Financiers, a joint research project with Société Générale. He is the academic director of the 1st year of the Double degree Data and Finance offered jointly by Ecole Polytechnique and HEC Paris. His research focuses on risk management problems for options, volatility modeling and Monte Carlo methods.
  • Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear
    By Aurélien AlfonsiStefano De Marcoa CERMICS, Ecole des Ponts ParisTechb CMAP, Ecole Polytechnique, Institut Polytechnique de ParisAurélien Alfonsi is professor at Ecole des Ponts ParisTech, part-time professor at Ecole Polytechnique, and deputy director of the CERMICS. He is the head of the master of financial mathematics at Ecole des Ponts. He is member of the Inria MathRisk project-team and involved in the chairs “Financial Risks” (Ecole des Ponts, Ecole Polytechnique, Sorbonne Université and Société Générale) and “Future of Quantitative Finance” (Ecole des Ponts, Université Paris Cité and BNP Paribas). His research brings on mathematical finance, insurance and numerical methods in probability.Stefano De Marco is Associate Professor in Probability and Mathematical Finance at Ecole Polytechnique, Paris. He owns a PhD in applied mathematics from Scuola Normale Superiore di Pisa and Université Paris-Est. He is a member of the steering committee of the Chaire Stress Test (Ecole Polytechnique and BNP Paribas) and he has taken part in the works of the Chaire Risques Financiers, a joint research project with Société Générale. He is the academic director of the 1st year of the Double degree Data and Finance offered jointly by Ecole Polytechnique and HEC Paris. His research focuses on risk management problems for options, volatility modeling and Monte Carlo methods.
  • On the impact of feeding cost risk in aquaculture valuation and decision making
    By Christian Oliver EwaldKevin Kamm† Department of Mathematics and Statistics, Umeå University, Umeå, Sweden‡ Inland University of Applied Sciences, Lillehammer, Norway
  • On the impact of feeding cost risk in aquaculture valuation and decision making
    By Christian Oliver EwaldKevin Kamm† Department of Mathematics and Statistics, Umeå University, Umeå, Sweden‡ Inland University of Applied Sciences, Lillehammer, Norway
  • Risk sharing with deep neural networks
    By M. BurzoniA. DoldiE. Monzio Compagnoni† Dipartimento di Matematica, Università degli Studi di Milano, Milano, Italy‡ Department of Economics and Management, Università degli Studi di Firenze, Firenze, Italy§ Department of Mathematics & Computer Science, University of Basel, Basel, Switzerland
  • Risk sharing with deep neural networks
    By M. BurzoniA. DoldiE. Monzio Compagnoni† Dipartimento di Matematica, Università degli Studi di Milano, Milano, Italy‡ Department of Economics and Management, Università degli Studi di Firenze, Firenze, Italy§ Department of Mathematics & Computer Science, University of Basel, Basel, Switzerland
  • On the pricing of capped volatility swaps using machine learning techniques
    By Stephan HöchtWim SchoutensEva Verschueren† Assenagon Asset Management S.A., München, Germany‡ Department of Mathematics, KU Leuven, Leuven, Belgium
  • On the pricing of capped volatility swaps using machine learning techniques
    By Stephan HöchtWim SchoutensEva Verschueren† Assenagon Asset Management S.A., München, Germany‡ Department of Mathematics, KU Leuven, Leuven, Belgium
  • When is cross impact relevant?
    By Victor Le CozIacopo MastromatteoDamien ChalletMichael Benzaquen† Chair of Econophysics and Complex Systems, École polytechnique, 91128 Palaiseau Cedex, France‡ Quant AI lab, 29 Rue de Choiseul, 75002 Paris, France§ LadHyX UMR CNRS 7646, École polytechnique, 91128 Palaiseau Cedex, France¶ Capital Fund Management, 23 Rue de l'Université, 75007 Paris, France∥ Laboratoire de Mathématiques et Informatique pour la Complexité et les Systèmes, CentraleSupélec, Université Paris-Saclay, 91192 Gif-sur-Yvette Cedex, France
  • When is cross impact relevant?
    By Victor Le CozIacopo MastromatteoDamien ChalletMichael Benzaquen† Chair of Econophysics and Complex Systems, École polytechnique, 91128 Palaiseau Cedex, France‡ Quant AI lab, 29 Rue de Choiseul, 75002 Paris, France§ LadHyX UMR CNRS 7646, École polytechnique, 91128 Palaiseau Cedex, France¶ Capital Fund Management, 23 Rue de l'Université, 75007 Paris, France∥ Laboratoire de Mathématiques et Informatique pour la Complexité et les Systèmes, CentraleSupélec, Université Paris-Saclay, 91192 Gif-sur-Yvette Cedex, France
  • Deep impulse control: application to interest rate intervention
    By Bowen JiaHoi Ying WongDepartment of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
  • Deep impulse control: application to interest rate intervention
    By Bowen JiaHoi Ying WongDepartment of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
  • Optimal stop-loss rules in markets with long-range dependence
    By Yun XiangShijie Deng† School of Finance, Southwestern University of Finance and Economics, Chengdu, People’s Republic of China‡ School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
  • Optimal stop-loss rules in markets with long-range dependence
    By Yun XiangShijie Deng† School of Finance, Southwestern University of Finance and Economics, Chengdu, People’s Republic of China‡ School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
  • Handbook of Price Impact Modeling
    By Johannes Muhle-KarbeMathematics, Imperial College LondonJohannes Muhle-Karbe is the Head of the Mathematical Finance Section at Imperial College London.
  • Handbook of Price Impact Modeling
    By Johannes Muhle-KarbeMathematics, Imperial College LondonJohannes Muhle-Karbe is the Head of the Mathematical Finance Section at Imperial College London.
  • Fin-GAN: forecasting and classifying financial time series via generative adversarial networks
    By Milena VuletićFelix PrenzelMihai Cucuringu† Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Rd, Oxford, OX2 6GG, UK‡ Oxford-Man Institute of Quantitative Finance, University of Oxford, Eagle House, Walton Well Rd, Oxford, UK§ Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK¶ The Alan Turing Institute, 96 Euston Rd, London, NW1 2DB, UK
  • Fin-GAN: forecasting and classifying financial time series via generative adversarial networks
    By Milena VuletićFelix PrenzelMihai Cucuringu† Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Rd, Oxford, OX2 6GG, UK‡ Oxford-Man Institute of Quantitative Finance, University of Oxford, Eagle House, Walton Well Rd, Oxford, UK§ Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK¶ The Alan Turing Institute, 96 Euston Rd, London, NW1 2DB, UK
  • Dynamic currency hedging with non-Gaussianity and ambiguity
    By Paweł PolakUrban Ulrych† Department of Applied Mathematics and Statistics, Institute for Advanced Computational Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794-3600, USA‡ Department of Banking and Finance, University of Zurich & Swiss Finance Institute, Plattenstrasse 32, Zurich 8032, Switzerland
  • Dynamic currency hedging with non-Gaussianity and ambiguity
    By Paweł PolakUrban Ulrych† Department of Applied Mathematics and Statistics, Institute for Advanced Computational Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794-3600, USA‡ Department of Banking and Finance, University of Zurich & Swiss Finance Institute, Plattenstrasse 32, Zurich 8032, Switzerland
  • Implied roughness in the term structure of oil market volatility
    By Mesias AlfeusChristina S. NikitopoulosLudger Overbeck† Department of Statistics and Actuarial Science, Stellenbosch University, Stellenbosch, South Africa‡ National Institute for Theoretical and Computational Sciences, Stellenbosch, South Africa§ Finance Discipline Group, UTS Business School, University of Technology Sydney, Australia¶ Mathematisches Institut, Justus-Liebig-University, Gießen, Germany
  • Implied roughness in the term structure of oil market volatility
    By Mesias AlfeusChristina S. NikitopoulosLudger Overbeck† Department of Statistics and Actuarial Science, Stellenbosch University, Stellenbosch, South Africa‡ National Institute for Theoretical and Computational Sciences, Stellenbosch, South Africa§ Finance Discipline Group, UTS Business School, University of Technology Sydney, Australia¶ Mathematisches Institut, Justus-Liebig-University, Gießen, Germany
  • Book review
    By Mark PodolskijUniversity of Luxembourg
  • A generalization of the rational rough Heston approximation
    By Jim GatheralRadoš RadoičićBaruch College, CUNY, New York, USA
  • A generalization of the rational rough Heston approximation
    By Jim GatheralRadoš RadoičićBaruch College, CUNY, New York, USA
  • A unified formula of the optimal portfolio for piecewise hyperbolic absolute risk aversion utilities
    By Zongxia LiangYang LiuMing MaRahul Pothi Vinoth† Department of Mathematical Sciences, Tsinghua University, Beijing 100084, People's Republic of China‡ Division of Mathematics, School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, People's Republic of China§ Hangzhou Higgs Asset Management Co., Ltd., Hangzhou, Zhejiang 310000, People's Republic of China¶ Department of Economics, UC Berkeley, Berkeley, CA 94720, USA
  • A unified formula of the optimal portfolio for piecewise hyperbolic absolute risk aversion utilities
    By Zongxia LiangYang LiuMing MaRahul Pothi Vinoth† Department of Mathematical Sciences, Tsinghua University, Beijing 100084, People's Republic of China‡ Division of Mathematics, School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, People's Republic of China§ Hangzhou Higgs Asset Management Co., Ltd., Hangzhou, Zhejiang 310000, People's Republic of China¶ Department of Economics, UC Berkeley, Berkeley, CA 94720, USA
  • On the optimal forecast with the fractional Brownian motion
    By Xiaohu WangJun YuChen Zhang† School of Economics, Fudan University, Shanghai, People's Republic of China‡ Shanghai Institute of International Finance and Economics, Shanghai, People's Republic of China§ Department of Finance and Business Economics, Faculty of Business Administration, University of Macau, Avenida da Universidade, Taipa, Macau, People's Republic of China
  • On the optimal forecast with the fractional Brownian motion
    By Xiaohu WangJun YuChen Zhang† School of Economics, Fudan University, Shanghai, People's Republic of China‡ Shanghai Institute of International Finance and Economics, Shanghai, People's Republic of China§ Department of Finance and Business Economics, Faculty of Business Administration, University of Macau, Avenida da Universidade, Taipa, Macau, People's Republic of China
  • An early indicator for anomalous stock market performance
    By Marlon FritzThomas GriesLukas Wiechers† Fachbereich Gesellschaftswissenschaften, Hochschule Darmstadt, Schöfferstraße 3, 64295 Darmstadt, Germany‡ Department of Economics, Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany
  • Physics-informed convolutional transformer for predicting volatility surface
    By Soohan KimSeok-Bae YunHyeong-Ohk BaeMuhyun LeeYoungjoon Hong† Department of Mathematics, Sungkyunkwan University, Suwon, Republic of Korea‡ Department of Financial Engineering, Ajou University, Suwon, Republic of Korea§ Samsung Securities, 11 Seocho-daero 74-gil, Seocho-gu, Seoul, Republic of Korea¶ Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
  • Physics-informed convolutional transformer for predicting volatility surface
    By Soohan KimSeok-Bae YunHyeong-Ohk BaeMuhyun LeeYoungjoon Hong† Department of Mathematics, Sungkyunkwan University, Suwon, Republic of Korea‡ Department of Financial Engineering, Ajou University, Suwon, Republic of Korea§ Samsung Securities, 11 Seocho-daero 74-gil, Seocho-gu, Seoul, Republic of Korea¶ Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
  • Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers
    By Álvaro ArroyoÁlvaro CarteaFernando Moreno-PinoStefan Zohren† Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford, UK‡ Mathematical Institute, University of Oxford, Oxford, UK§ Signal Processing and Learning Group, Universidad Carlos III de Madrid, Madrid, Spain
  • Estimating correlations among elliptically distributed random variables under any form of heteroskedasticity
    By Matteo PelagattiGiacomo Sbrana† Department of Economics, Management and Statistics, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, Milan, 20126, Italy‡ Department of Information Systems, Supply Chain & Decision Making, NEOMA Business School, 1 Rue du Marechal Juin, Mont-Saint-Aignan, 76130, France
  • Estimating correlations among elliptically distributed random variables under any form of heteroskedasticity
    By Matteo PelagattiGiacomo Sbrana† Department of Economics, Management and Statistics, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, Milan, 20126, Italy‡ Department of Information Systems, Supply Chain & Decision Making, NEOMA Business School, 1 Rue du Marechal Juin, Mont-Saint-Aignan, 76130, France
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