Quantitative Finance


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.

  • Trade co-occurrence, trade flow decomposition and conditional order imbalance in equity markets
    By Yutong Lu Gesine Reinert Mihai Cucuringu † Department of Statistics, University of Oxford, 24–29 St Giles', Oxford OX1 3LB, UK‡ Mathematical Institute, University of Oxford, Woodstock Rd, Oxford OX2 6GG, UK§ Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford, UK¶ The Alan Turing Institute, 96 Euston Rd, London NW1 2DB, UK
  • Predicting forward default probabilities of firms: a discrete-time forward hazard model with firm-specific frailty
    By Ruey-Ching Hwang Yi-Chi Chen † Department of Finance, National Dong Hwa University, Hualien, Taiwan‡ Department of Economics, National Cheng Kung University, Tainan, Taiwan
  • Interest rate convexity in a Gaussian framework
    By Antoine Jacquier Mugad Oumgari † Department of Mathematics, Imperial College London, London, UK‡ The Alan Turing Institute, London, UK§ University College London, London, UK¶ Lloyds Banking, London, UK
  • Neural network approach to portfolio optimization with leverage constraints: a case study on high inflation investment
    By Chendi Ni Yuying Li Peter Forsyth † Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada‡ Flap Technologies, 307 W 38th St, Floor 16, PMB 468, New York, NY, 10018, USA
  • Cross-section without factors: a string model for expected returns
    By Walter Distaso Antonio Mele Grigory Vilkov † Imperial College, South Kensington Campus, London SW7 2AZ, United Kingdom‡ USI Lugano, Swiss Finance Institute and CEPR, Via Buffi 13, 6900 Lugano, Switzerland§ Frankfurt School of Finance & Management, Adickesallee 32-34, Frankfurt, Germany
  • Counting jumps: does the counting process count?
    By Laura Ballotta Gianluca Fusai Daniele Marazzina † Bayes Business School (formerly Cass), City University of London, London, UK‡ Dipartimento DiSei, Università degli Studi del Piemonte Orientale, Novara, Italy§ Department of Mathematics, Politecnico di Milano, Milano, Italy
  • Market consistent bid-ask option pricing under Dempster-Shafer uncertainty
    By A. Cinfrignini D. Petturiti B. Vantaggi † Department MEMOTEF, “La Sapienza” University of Rome, Rome, Italy‡ Department Economics, University of Perugia, Perugia, Italy
  • ESG risk exposure: a tale of two tails
    By Runfeng Yang Massimiliano Caporin Juan-Angel Jiménez-Martin † Department of Economics, Ca' Foscari University of Venice, Venice, Italy‡ Department of Statistical Sciences, University of Padova, Padova, Italy§ Instituto Complutense de Análisis Económico (ICAE), Facultad de Ciencias Económicas y Empresariales, Universidad Complutense de Madrid, Madrid, Spain
  • On the implied volatility skew outside the at-the-money point
    By Michele Azzone Lorenzo Torricelli † Department of Mathematics, Politecnico di Milano, Milano, Italy‡ Department of Statistical Sciences “P. Fortunati”, University of Bologna, Bologna, Italy
  • Deep learning for enhanced index tracking
    By Zhiwen Dai Lingfei Li Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China
  • Consistent curves in the -world: optimal bonds portfolio
    By Gaddiel Y. Ouaknin Engineering Department, Stanford University, Stanford, CA, USA
  • Optimal trading and competition with information in the price impact model
    By Longjie Xu Yufeng Shi † Institute for Financial Studies, Shandong University, Jinan 250100, People's Republic of China‡ School of Mathematics, Shandong University, Jinan 250100, People's Republic of China
  • Do price trajectory data increase the efficiency of market impact estimation?
    By Fengpei Li Vitalii Ihnatiuk Yu Chen Jiahe Lin Ryan J. Kinnear Anderson Schneider Yuriy Nevmyvaka Henry Lam † Machine Learning Research, Morgan Stanley, New York, NY, USA‡ Quantitative Research, Morgan Stanley, New York, NY, USA§ Department of Economics, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine¶ Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada‖ Department of IEOR, Columbia University, New York, NY, USA
  • Risk management under weighted limited expected loss
    By An Chen Thai Nguyen † Institute of Insurance Science, University of Ulm, Helmholtzstrasse 20, 89069 Ulm, Germany‡ École d'Actuariat, Université Laval, 2425, rue de l'Agriculture, Québec G1V 0A6, Canada§ School of Economic Mathematics – Statistics, University of Economics Ho Chi Minh City, 59C Nguyen Dinh Chieu Street, Ho Chi Minh City, Vietnam
  • Optimal reinsurance under a new design: two layers and multiple reinsurers
    By Dingjun Yao Jinxia Zhu † School of Finance, Nanjing University of Finance and Economics, Nanjing 210023, People's Republic of China‡ School of Risk and Actuarial Studies, Business School, The University of New South Wales, Sydney, NSW 2052, Australia
  • Mean-variance portfolio with wealth and volatility dependent risk aversion
    By Shican Liu School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China
  • A study on asset price bubble dynamics: explosive trend or quadratic variation?
    By Robert A. Jarrow Simon S. Kwok † Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, NY 14853, USA‡ Kamakura Corporation, Honolulu, HI 96815, USA§ School of Economics, The University of Sydney, Camperdown, NSW 2006, Australia
  • The contagion of extreme risks between fossil and green energy markets: evidence from China
    By Xiaohang RenYa XiaoFeng HeGiray Gozgor† School of Business, Central South University, Changsha, People’s Republic of China‡ School of Finance, Capital University of Economics and Business, Beijing, People’s Republic of China§ School of Management, University of Bradford, Bradford, UK
  • Dynamic partial (co)variance forecasting model
    By Zirong ChenYao Zhou† Department of Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, People's Republic of China‡ The People's Bank of China, Beijing, People's Republic of China
  • Online learning of order flow and market impact with Bayesian change-point detection methods
    By Ioanna-Yvonni TsaknakiFabrizio LilloPiero Mazzarisi† Scuola Normale Superiore, Piazza dei Cavalieri 7, Pisa, 56126, Italy‡ Department of Mathematics, University of Bologna, Piazza di Porta San Donato 5, Bologna, 40126, Italy§ Department of Economics and Statistics, University of Siena, Banchi di Sotto 55, Siena, 53100, Italy
  • Speed and duration of drawdown under general Markov models
    By Lingfei LiPingping ZengGongqiu Zhang† Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR‡ Department of Mathematics, Southern University of Science and Technology, Shenzhen, People's Republic of China§ School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, People's Republic of China
  • 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