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.

  • Merged LSTM-MLP for option valuation
    By Jacob Vinje Erlend Stegavik Rygg Cassandra Wu Morten Risstad Rita Pimentel Sjur Westgaard Christian O. Ewald Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway
  • Optimal reinsurance in a dynamic contagion model: comparing self-exciting and externally-exciting risks
    By C. Ceci A. Cretarola † Department MEMOTEF, University of Rome La Sapienza, Via del Castro Laurenziano, 9, Rome, I-00161, Italy‡ Department of Economic Studies, University ‘G. D’Annunzio’ of Chieti-Pescara, Viale Pindaro, 42, Pescara, I-65127, Italy
  • The non-linear ESG premium
    By Runfeng Yang Juan-Angel Jimenez-Martin Massimiliano Caporin † Business School, Central South University, Changsha, People's Republic of China‡ Instituto Complutense de Análisis Económico (ICAE), Universidad Complutense de Madrid, Madrid, Spain§ Department of Statistical Sciences, University of Padova, Padua, Italy
  • Enhanced indexation: can volatility timing improve portfolio performance?
    By Qi Jiang Chonghui Jiang Yunbi An † School of Finance, Jiangxi University of Finance and Economics, Nanchang, People’s Republic of China‡ Odette School of Business, University of Windsor, Windsor, Canada
  • A copula-based data augmentation strategy for the sensitivity analysis of extreme operational losses
    By A. Khorrami Chokami G. Rabitti † Department of Mathematics and Computer Science, Università di Cagliari, Cagliari, Italy‡ Department of Actuarial Mathematics and Statistics, Heriot-Watt University and Maxwell Institute for Mathematical Sciences, Edinburgh, UK
  • Hydrodynamics of Markets: Hidden Links between Physics and Finance
    By Mayukh Mukhopadhyay Department of Marketing, Indian Institute of Management IndoreMayukh Mukhopadhyay is a full-time technology consultant with more than a decade of experience in designing business continuity solutions in complex digital transformation programs. He received his M.Engg from Jadavpur University and MBA from IIT Kharagpur. He is currently pursuing PhD (Industry Track) from IIM Indore. He has authored the book Ethereum Smart Contract Development, Packt UK. He is an active Professional Member of ACM, Professional Scrum Master I and Azure Certified Solution Architect.
  • Computing the SSR
    By Peter K. Friz Jim Gatheral † TU and WIAS Berlin, Berlin, Gerrmany‡ Baruch College, CUNY, New York, USA
  • An early-warning risk signals framework to capture systematic risk in financial markets
    By Vito Ciciretti Monomita Nandy Alberto Pallotta Suman Lodh P. K. Senyo Jekaterina Kartasova † Independent Researcher, Berlin, Germany‡ Brunel Business School, Brunel University London, Middlesex, UK§ Accounting, Finance & Economics, Middlesex University, London, UK¶ Kingston Business School, Kingston University London, Surrey, UK‖ Accounting, Finance & Economics, University of Southampton, Southampton, UK†† Middlesex University Business School, Middlesex University, London, UK
  • Neural Hawkes: non-parametric estimation in high dimension and causality analysis in cryptocurrency markets
    By Timothee Fabre Ioane Muni Toke † Laboratoire MICS, CentraleSupélec, Université Paris-Saclay, Paris, France‡ SUN ZU Lab, Paris, France
  • Simultaneous upper and lower bounds of American-style option prices with hedging via neural networks
    By Ivan Guo Nicolas Langrené Jiahao Wu † School of Mathematical Sciences, Monash University, Melbourne, Australia‡ Centre for Quantitative Finance and Investment Strategies, Monash University, Melbourne, Australia§ Guangdong Provincial/Zhuhai Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, People's Republic of China
  • A model of financial bubbles and drawdowns with non-local behavioral self-referencing
    By Yannick Malevergne Didier Sornette Ran Wei † PRISM Sorbonne – EA 4101, Université Paris 1 Panthéon-Sorbonne, 17 rue de la Sorbonne, Paris, 75005, France‡ Institute of Risk Analysis, Prediction and Management (Risks-X), Southern University of Science and Technology (SUSTech), Shenzhen, 518055, People's Republic of China§ Department of Management, Technology and Economics, ETH Zurich, 7 Scheuchzerstrasse, Zürich, 8092, Switzerland
  • Provisions and economic capital for credit losses†
    By D. Bastide S. Crépey † RISK Stress Testing Methodologies & Models, BNP Paribas, Paris, France‡ Laboratoire de Mathématiques et Modélisation d'Evry (LaMME) CNRS UMR 8071, Université d'Evry, Université Paris-Saclay, Evry, France§ Laboratoire de Probabilités Statistique et Modélisation (LPSM) CNRS UMR 8001, Université Paris Cité, Paris, France
  • Multiperiod interval-based stochastic dominance with application to dynamic portfolios
    By Giorgio Consigli Brian Vasquez Campos Jia Liu † Dept of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, UAE‡ School of Mathematics and Statistics, Xi'an Jiaotong University, Shaanxi, People's Republic of China
  • Bid-ask bounds for option prices: the two-tail distortion model
    By Umberto Cherubini Sabrina Mulinacci † Department of Economics, University of Bologna, Bologna, Italy‡ Department of Statistical Sciences University of Bologna, Bologna, Italy
  • Local sensitivity analysis of heating degree day and cooling degree day temperature derivative prices
    By Sara Solanilla Blanco † Department of Economic, Financial and Actuarial Mathematics, University of Barcelona, Av. Diagonal 690, 08034 Barcelona, Spain‡ Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway
  • When order execution meets informed trading
    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§ National Center for Applied Mathematics of Shandong, Shandong University, Jinan 250100, People's Republic of China¶ State Key Laboratory of Cryptography and Digital Economy Security, Shandong University, Jinan 250100, People's Republic of China
  • Pricing and calibration in the 4-factor path-dependent volatility model
    By Guido Gazzani Julien Guyon † Department of Economics, University of Verona, Via Cantarane 24, 37129 Verona, Italy‡ CERMICS, ENPC, Institut polytechnique de Paris, Marne-la-Vallée, France§ Department of Finance and Risk Engineering, NYU Tandon School of Engineering, One MetroTech Center, Brooklyn, 11201 NY, USA
  • Ensemble learning for portfolio valuation and risk management
    By Lotfi Boudabsa Damir Filipović † Department of Mathematics, EPFL, Lausanne, Switzerland‡ Swiss Finance Institute @ EPFL, EPFL and Swiss Finance Institute, Lausanne, VD, Switzerland
  • Relative entropy-regularized robust optimal order execution
    By Meng Wang Tai-Ho Wang Baruch College, City University of New York, 1 Bernard Baruch Way, New York, NY 10010, USA
  • Beyond GMV: the relevance of covariance matrix estimation for risk-based portfolio construction
    By M. Sipke Dom Clint Howard Maarten Jansen Harald Lohre † Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands‡ UTS Business School, University of Technology Sydney, Sydney, Australia§ Robeco Quantitative Investments, Rotterdam, The Netherlands¶ Lancaster University Management School, Lancaster University, Lancaster, UK
  • A social media alert system for meme stocks
    By Ilaria Gianstefani Luigi Longo Massimo Riccaboni † DNB - De Nederlasche Bank, Amsterdam, Netherlands‡ JRC - European Commission, Ispra (VA), Italy§ IUSS, Palazzo del Broletto Piazza della Vittoria, n.15 27100, Pavia (PV), Italy¶ IMT School for Advanced Studies Lucca, Piazza S.Francesco, 19, 55100, Lucca (LU), Italy
  • Back-testing credit risk parameters on low default portfolios: a simple Bayesian transfer learning approach with an application to sovereign risk‖
    By Sergio Caprioli Raphael Cavallari Jacopo Foschi Riccardo Cogo † Internal Validation & Model Risk Management, Intesa Sanpaolo SpA, Turin, Italy‡ Colleges of Computing, Business, and Engineering, Georgia Institute of Technology, Atlanta, USA§ Data & Artificial Intelligence Office, Intesa Sanpaolo SpA, Turin, Italy¶ Department of Economics, Management and Statistics, University of Milano, Bicocca, Milan, Italy
  • Harnessing uncertainty: a new approach to real estate investment decision support
    By Arne Johan Pollestad Are Oust NTNU Business School, Norwegian University of Science and Technology, Trondheim, Norway
  • α-threshold networks in credit risk models
    By Eduard Baumöhl Štefan Lyócsa † Faculty of Commerce, University of Economics, Dolnozemska cesta 1, Bratislava, 852 35, Slovakia‡ Institute of Economic Research, Slovak Academy of Sciences, Sancova 56, Bratislava, 811 05, Slovakia§ Department of Finance, Faculty of Economics and Administration, Masaryk University, Lipova 41a, Brno, 602 00, Czech Republic¶ Faculty of Management and Business, University of Presov, Konstantinova 16, Presov, 080 01, Slovakia
  • Optimal liquidation under indirect price impact with propagator
    By Jean-Loup Dupret Donatien Hainaut † LIDAM-ISBA, Université Catholique de Louvain, Voie du Roman Pays 20, Louvain-La-Neuve, 1348, Belgium‡ Department of Mathematics and RiskLab, ETH Zurich, Rämistrasse 101, Zurich, 8092, Switzerland
  • Liquidity Coverage at Risk
    By Giacomo Morelli Virginia Pugliese Paolo Santucci de Magistris † Department of Statistical Sciences, Sapienza University of Rome, Rome, 00185, Italy‡ Bank of England, London, EC2R 8AH, UK§ Department of Economics and Social Sciences, Sapienza University of Rome, Rome, 00185, Italy¶ Department of Economics and Finance, Luiss University, Rome, 00197, Italy
  • Special Issue on XXIV Workshop on Quantitative Finance
    By Marina Di Giacinto Holger Kraft a Università degli studi di Cassino e del Lazio Meridionale, Italyb Goethe University Frankfurt, Germany
  • Randomized signature methods in optimal portfolio selection
    By Erdinç Akyildirim Matteo Gambara Josef Teichmann Syang Zhou † Department of Mathematics, ETH, Zurich, Switzerland‡ Nottingham University Business School, University of Nottingham, Nottingham, UK
  • Options-driven volatility forecasting
    By Nikolas Michael Mihai Cucuringu Sam Howison † Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK‡ Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Rd, Oxford OX2 6GG, UK§ The Alan Turing Institute, John Dodson House, 96 Euston Rd, London NW1 2DB, UK
  • A note on closed-form spread option valuation under log-normal models
    By Nuerxiati Abudurexiti Kai He Dongdong Hu Hasanjan Sayit † Department of Financial and Actuarial Mathematics, Xi'an Jiaotong Liverpool University, Suzhou, People's Republic of China‡ Mathematics Department, Yiwu Industrial & Commercial College, Yiwu, People's Republic of China
  • Lost in the LIBOR transition
    By Alex Backwell Andrea Macrina Erik Schlögl David Skovmand † African Institute for Financial Markets and Risk Management, University of Cape Town, Rondebosch, 7701, South Africa‡ Department of Mathematics, University College London, London, WC1E 6BT, UK§ School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW, 2007, Australia¶ Faculty of Science, Department of Statistics, University of Johannesburg, Auckland Park, Johannesburg, 2006, South Africa∥ Department of Mathematics, University of Copenhagen, Copenhagen, 2100, Denmark
  • Deep Learning: Foundations and Concepts
    By Blanka N. Horvath Anastasis Kratsios Raeid Saqur a University of Oxford, Oxford, UKb McMaster University, Hamilton, Canadac University of Toronto, Toronto, CanadaBlanka N. Horvath is an Associate Professor at the University of Oxford and researcher at the Oxford Man Institute, and a core member of the DataSig group affiliated with the Alan Turing Institute. With a career spanning across different countries, she has held tenure-track faculty positions at the Technical University of Munich and Kings College London, as well as postdoctoral appointments at Imperial College London and ETH Zurich. Blanka's research bridges foundational theory and industry application. Her expertise encompasses stochastic volatility modelling, with pioneering contributions to rough volatility models, and extends to cutting-edge developments in generative models, emphasizing the use of rough path signatures for advanced data representation. Blankas work is evidenced to have influenced the financial industry, with numerous contributions implemented by leading organizations. It has also been recognized with prestigious grants and honours, including being the inaugural recipient of Risk Magazines Rising Star Award in 2020 and the London Mathematical Society's Emmy Noether Fellowship 2024–2025.Anastasis Kratsios is an Assistant Professor at McMaster University, affiliated with the Vector Institute in Toronto. His research focuses on the mathematical foundations of geometric deep learning and the design of custom universal deep learning models optimized for problem-specific geometries. His work has appeared in numerous top machine learning venues, from JMLR to NeurIPS. Anastasis completed his postdocs at ETH Zrich in the mathematical finance group and in the computer science and probability groups at the Universitt Basel.Raeid Saqur is a final year PhD candidate in Computer Science and a lecturer for the Natural Language Computing (CS401/2511) course at the University of Toronto and Vector Institute for AI. He is cross-affiliated with Princeton University as a Fulbright Scholar, collaborating with the CS NLP group. Raeid's educational background spans engineering (BSc.), applied computing (MSc.), and an MBA from the Rotman School of Management. His research interests range from multi-modal, grounded representation learning, language compositionality to long-horizon action planning for AI agents by adaptive reinforcement learning, and human-like cognitive biases in large language models. Raeid made his mark at IBM as their youngest Enterprise Thought Leader, and in the entrepreneurial space. His industry innovations are recognized by the prestigious Peter F. Drucker Effective Executive Scholarship and numerous leadership awards. Beyond his scholarly work, Raeid has mentored over 300 students in AI and NLP.
  • Time-consistent investment strategy for a DC pension plan with the return of premiums clause and mispricing
    By Ling Zhang Pei Wang Yang Shen † School of National Finance, Guangdong University of Finance, Guangzhou, 510521, People's Republic of China‡ School of Risk and Actuarial Studies and CEPAR, University of New South Wales Sydney, Sydney, NSW, 2052, Australia
  • A semi-parametric dynamic conditional correlation framework for risk forecasting
    By Giuseppe Storti Chao Wang † Department of Economics and Statistics, University of Salerno, Salerno, Italy‡ Discipline of Business Analytics, The University of Sydney, Sydney, Australia
  • A new test of factor model for asset returns: based on pleiotropy model
    By Qing Jiang Xingwei Tong Peng Wu Xun Zhang † Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, People's Republic of China‡ School of Statistics, Beijing Normal University, Beijing 100875, People's Republic of China§ School of Mathematics and Statistics, Beijing Technology and Business University, Beijing 100048, People's Republic of China
  • NN de-Americanization: an efficient method to facilitate calibration of American-style options
    By Peter PommergÅrd Lind Jim Gatheral † Business School, Aalborg University, Aalborg, Denmark‡ Department of Mathematics, Baruch College, CUNY, New York, USA
  • Multiple equilibria in mean-field game models of firm competition with strategic complementarities
    By Jodi Dianetti Salvatore Federico Giorgio Ferrari Giuseppe Floccari † Department of Economics and Finance, University of Rome Tor Vergata, Rome, Italy‡ Department of Mathematics, University of Bologna, Bologna, Italy§ Center for Mathematical Economics (IMW), Bielefeld University, Bielefeld, Germany¶ Economic Outlook and Monetary Policy Directorate, Bank of Italy, Rome, Italy
  • Predicting VIX with adaptive machine learning
    By Yunfei Bai Charlie X. Cai † AI/ML and Big Data Consultant, Seattle, USA‡ Finance, Liverpool University School of Management, University of Liverpool, Liverpool, UK
  • A unifying approach for the pricing of debt securities
    By Marie-Claude Vachon Anne Mackay † Department of Mathematics, Université du Québec à Montréal, Montréal, Canada‡ Department of Mathematics and Department of Finance, Université de Sherbooke, Sherbrooke, Canada
  • Asset and Factor Risk Budgeting: a balanced approach
    By Adil Rengim Cetingoz Olivier Guéant Centre d'Economie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne, 106 Boulevard de l'Hôpital, Paris Cedex 13, 75642, France
  • An orthogonal expansion approach to joint SPX and VIX calibration in affine stochastic volatility models with jumps
    By Thomas K. Kloster Elisa Nicolato Department of Economics and Business Economics, Aarhus University, Fuglesangs Allé 4, Aarhus V, 8210, Denmark
  • Forecasting volatility in Chinese crude oil futures: insights from volatility-of-volatility and Markov regime-switching approaches
    By Gaoxiu Qiao Yijun Pan Chao Liang † School of Mathematics, Southwest Jiaotong University, Chengdu, People’s Republic of China‡ School of Economics and Management, Southwest Jiaotong University, Chengdu, People’s Republic of China
  • Equity protection swaps: investment insurance for superannuation accounts
    By Huansang Xu Ruyi Liu Marek Rutkowski † School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia‡ Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China§ Faculty of Mathematics and Information Science, Warsaw University of Technology, Warszawa 00-661, Poland
  • Price dynamics with circuit breakers
    By Sandro Claudio Lera Didier Sornette Florian Ulmann † Institute of Risk Analysis, Prediction and Management, Southern University of Science and Technology, Shenzhen, People's Republic of China‡ Business School, Southern University of Science and Technology, Shenzhen, People's Republic of China§ Connection Science, Massachusetts Institute of Technology, Cambridge, MA, USA¶ Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
  • On bid and ask pricing of European options via direct discretization of Choquet distorted expectations
    By Matteo Michielon Quantitative Analysis and Quantitative Development, ABN AMRO Bank N.V., Gustav Mahlerlaan 10, 1082 PP, Amsterdam, The Netherlands
  • Semi-parametric financial risk forecasting incorporating multiple realized measures
    By Rangika Peiris Chao Wang Richard Gerlach Minh-Ngoc Tran Discipline of Business Analytics, The University of Sydney, Sydney, Australia
  • On general semi-closed-form solutions for VIX derivative pricing
    By Étienne Bacon Jean-François Bégin Geneviève Gauthier † Department of Decision Sciences, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 2A7, Canada‡ Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
  • Algorithmic trading of real-time electricity with machine learning
    By Vighnesh Natarajan Ganesh Derek Bunn London Business School, The Regent's Park, London NW1 4SA, United Kingdom
  • Optimal attention allocation: picking alpha or betting on beta?
    By Zuyao Gu Yun Shi Tingjin Yan Yong Zhou Key Laboratory of Advanced Theory and Application in Statistics and Data Science, MOE, and School of Statistics and Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai, People's Republic of China
  • Risk-free rate caplets pricing by CTMC approximation
    By Fengming Liu Yingda Song Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
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