Ariel Neufeld

Ariel Neufeld
Assistant Professor


Nanyang Technological University (NTU)
Division of Mathematical Sciences
Office SPMS-MAS 05-02
21 Nanyang Link, Singapore 637371
Phone (65) 6592 1799
Email ariel.neufeld@ntu.edu.sg



I am a Nanyang Assistant Professor in mathematics at the Nanyang Technological University in Singapore.
I received my PhD from ETH Zurich in 2015 under the supervision of Prof. Marcel Nutz and Prof. Martin Schweizer.

My research focuses on:

• machine learning algorithms, their convergence rates, and their applications in finance and operations research
• model uncertainty in finance and operations research
• financial and insurance mathematics
• stochastic analysis and stochastic optimal control
• stochastic optimization and applied probability theory

Support by the following grants is gratefully acknowledged:

• Nanyang Assistant Professorship Grant (NAP Grant)   Machine Learning based Algorithms in Finance and Insurance
• MOE AcRF Tier 2 Grant   MOE-T2EP20222-0013
• MOE AcRF Tier 1 Grant   RG74/21
• NRF-QEP Grant   NRF2021-QEP2-02-P06

A detailed CV can be found here.

Open Positions

I am looking for Postdocs, PhD students, as well as Bachelor/Master students who would like to join my research group; see for example

Postdoc position

Postdoc position in Quantum Computing for Finance

PhD position

Research Internship position for PhD and master students

If you would like to join my research group (even if you do not see a suitable job announcement), please feel free to contact me anytime by email.

Members of the Research Group

Zeyi Chen   (Undergraduate Research Student, started 08.2022; jointly supervised also by Qikun Xiang)

Johannes Langner   (Visiting Research Associate, started 10.2023; jointly supervised also by Kyunghyun Park)

Thi Van Hang Nguyen   (Postdoctoral Research Fellow, SASEA Fellowship, started 10.2022)

Tuan Anh Nguyen   (Postdoctoral Research Fellow, started 01.2023)

Kyunghyun Park   (Gopalakrishnan - Presidential Postdoctoral Fellow, started 12.2022)

Philipp Schmocker   (PhD Student, started 08.2021)

Sizhou Wu   (Postdoctoral Research Fellow, started 08.2021)

Qikun Xiang   (PostDoc, started 08.2019 as PhD student and 01.2024 as PostDoc)


Former Members of the Research Group

Géraldine Bouveret   (Gopalakrishnan - Presidential Postdoctoral Fellow, 11.2019-01.2022; now Chief Research Officer at RIMM Sustainability)

Pushpendu Ghosh   (Research Internship, 08.2019-12.2019; now Applied Scientist at Amazon)

Yongming Li   (Project Officer, 05.2020-12.2020; now PhD student in mathematics at Texas A&M University)

Matthew Ng Cheng En   (Project Officer, 08.2021-07.2023; now Quant at BNP Paribas)

Julian Sester   (Postdoctoral Research Fellow, 04.2020-06.2022; now Peng Tsu Ann Assistant Professor in mathematics at NUS)

Shunan Sheng   (Undergraduate Research Student, 06.2020-07.2022; now PhD student in statistics at Columbia University)

Daiying Yin   (Undergraduate Research Student, 06.2020-08.2021; now PhD student in mathematics at NTU with J.-P. Ortega)

Ying Zhang   (Postdoctoral Research Fellow, 11.2020-06.2023; now Assistant Professor at HKUST-GZ)


Teaching

MH7004

Introduction
Chapter 1.1
Chapter 1.2
Tutorial about Chapter 1
Tutorial about Chapter 1--Solutions
Chapter 2
Tutorial about Chapter 2
Tutorial about Chapter 2--Solutions
Chapter 3.1
Chapter 3.2
Chapter 3.3
Chapter 3.4
Tutorial about Chapter 3
Tutorial about Chapter 3--Solutions
Chapter 4
Chapter 5
Notes on Uniform Integrability
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Statistical Tables

Publications and Preprints

A. Neufeld, P. Schmocker:
Universal Approximation Property of Random Neural Networks
Preprint (submitted), 2023 [PDF, arXiv, Code]

A. Neufeld, T. A. Nguyen:
Rectified deep neural networks overcome the curse of dimensionality when approximating solutions of McKean--Vlasov stochastic differential equations
Preprint (submitted), 2023 [PDF, arXiv]

A. Neufeld, T. A. Nguyen, S. Wu:
Multilevel Picard approximations overcome the curse of dimensionality in the numerical approximation of general semilinear PDEs with gradient-dependent nonlinearities
Preprint (submitted), 2023 [PDF, arXiv]

D. Bartl, A. Neufeld, K. Park:
Sensitivity of robust optimization problems under drift and volatility uncertainty
Preprint (submitted), 2023 [PDF, arXiv]

A. Neufeld, T. A. Nguyen, S. Wu:
Deep ReLU neural networks overcome the curse of dimensionality when approximating semilinear partial integro-differential equations
Preprint (submitted), 2023 [PDF, arXiv]

A. Neufeld, S. Wu:
Multilevel Picard algorithm for general semilinear parabolic PDEs with gradient-dependent nonlinearities
Preprint (submitted), 2023 [PDF, arXiv, Code]

A. Neufeld, J. Sester:
Bounding the Difference between the Values of Robust and Non-Robust Markov Decision Problems
Preprint (submitted), 2023 [PDF, arXiv]

A. Neufeld, Q. Xiang:
Feasible approximation of matching equilibria for large-scale matching for teams problems
Preprint (submitted), 2023 [PDF, arXiv, Code]

A. Neufeld, J. Sester:
Neural networks can detect model-free static arbitrage strategies
Preprint (submitted), 2023 [PDF, arXiv, Code]

Y. Li, A. Neufeld:
Quantum Monte Carlo algorithm for solving Black-Scholes PDEs for high-dimensional option pricing in finance and its proof of overcoming the curse of dimensionality
Preprint (submitted), 2023 [PDF, arXiv]

D.-Y. Lim, A. Neufeld, S. Sabanis, Y. Zhang:
Langevin dynamics based algorithm e-THεO POULA for stochastic optimization problems with discontinuous stochastic gradient
Preprint (submitted), 2022 [PDF, arXiv, Code]

A. Neufeld, J. Sester:
Robust Q-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty
Preprint (submitted), 2022 [PDF, arXiv, Code]

A. Neufeld, P. Schmocker:
Chaotic Hedging with Iterated Integrals and Neural Networks
Preprint (submitted), 2022 [PDF, arXiv, Code]

A. Neufeld, M. Ng, Y. Zhang:
Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting
Preprint (submitted), 2022 [PDF, arXiv, Code]

A. Neufeld, S. Wu:
Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis
Preprint (submitted), 2022 [PDF, arXiv, Code]

A. Neufeld, Q. Xiang:
Numerical method for approximately optimal solutions of two-stage distributionally robust optimization with marginal constraints
Preprint (submitted), 2022 [PDF, arXiv, Code]

A. Neufeld, Q. Xiang:
Numerical method for feasible and approximately optimal solutions of multi-marginal optimal transport beyond discrete measures
Preprint (submitted), 2022 [PDF, arXiv, Code]

C. Beck, S. Becker, P. Cheridito, A. Jentzen, A. Neufeld:
Deep learning based numerical approximation algorithms for stochastic partial differential equations and high-dimensional nonlinear filtering problems
Preprint (submitted), 2020 [PDF, arXiv, Code]

A. Neufeld, J. Sester, D. Yin:
Detecting data-driven robust statistical arbitrage strategies with deep neural networks
SIAM Journal on Financial Mathematics (SIFIN), accepted for publication, 2024 [PDF, arXiv, Code]

S. Sheng, Q. Xiang, I. Nevat, A. Neufeld:
Binary Spatial Random Field Reconstruction from Non-Gaussian Inhomogeneous Time-series Observations
Journal of the Franklin Institute, Vol. 361, No. 2, pp. 612-636, 2024 [PDF, arXiv, DOI, Code]

J. Ansari, E. Lütkebohmert, A. Neufeld, J. Sester:
Improved Robust Price Bounds for Multi-Asset Derivatives under Market-Implied Dependence Information
Finance and Stochastics, accepted for publication, 2023 [PDF, arXiv, Code]

C. Beck, S. Becker, P. Cheridito, A. Jentzen, A. Neufeld:
An efficient Monte Carlo scheme for Zakai equations
Communications in Nonlinear Science and Numerical Simulation, Vol. 126, 107438, 2023 [PDF, arXiv, DOI, Code]

A. Neufeld, J. Sester, M. Sikic:
Markov Decision Processes under Model Uncertainty
Mathematical Finance, Vol. 33, No. 3, pp. 618-665, 2023 [PDF, arXiv, DOI, Code]

D.-Y. Lim, A. Neufeld, S. Sabanis, Y. Zhang:
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function
IMA Journal of Numerical Analysis, 2023 [PDF, arXiv, DOI, Code]

A. Neufeld, J. Sester:
A deep learning approach to data-driven model-free pricing and to martingale optimal transport
IEEE Transactions on Information Theory, Vol. 69, No. 5, pp. 3172-3189, 2023 [PDF, arXiv, DOI, Code]

Q. Xiang, A. Neufeld, G. W. Peters, I. Nevat, A. Datta:
A Bonus-Malus Framework for Cyber Risk Insurance and Optimal Cybersecurity Provisioning
European Actuarial Journal, 2023 [PDF, arXiv, DOI, Code]

A. Neufeld, A. Papapantoleon, Q. Xiang:
Model-free bounds for multi-asset options using option-implied information and their exact computation
Management Science, Vol. 69, No. 4, pp. 2051-2068, 2023 [PDF, arXiv, DOI, Code]

M. Baes, C. Herrera, A. Neufeld, P. Ruyssen:
Low-Rank plus Sparse Decomposition of Covariance Matrices using Neural Network Parametrization
IEEE Transactions on Neural Networks and Learning Systems, Vol. 34, No. 1, pp. 171-185, 2023 [PDF, arXiv, DOI, Code]

P. Ghosh, A. Neufeld, J. K. Sahoo:
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests
Finance Research Letters, Vol. 46, Part A, 102280, 2022 [PDF, arXiv, DOI, Code]

A. Neufeld, J. Sester:
On the stability of the martingale optimal transport problem: A set-valued map approach
Statistics & Probability Letters, Vol. 176, pp. 109131-1-7, 2021 [PDF, arXiv, DOI]

A. Neufeld, J. Sester:
Model-free price bounds under dynamic option trading
SIAM Journal on Financial Mathematics (SIFIN), Vol. 12, No. 4, pp. 1307-1339, 2021 [PDF, arXiv, DOI, Code]

D. Bartl, M. Kupper, A. Neufeld:
Duality Theory for Robust Utility Maximisation
Finance and Stochastics, Vol. 25, No. 3, pp. 469-503, 2021 [PDF, arXiv, DOI]

P. J. Graber, V. Ignazio, A. Neufeld:
Nonlocal Bertrand and Cournot Mean Field Games with General Nonlinear Demand Schedule
Journal de Mathématiques Pures et Appliquées (JMPA), Vol. 148, pp. 150-198, 2021 [PDF, arXiv, DOI]

P. Harms, C. Liu, A. Neufeld:
Supermartingale Deflators in the Absence of a Numéraire
Mathematics and Financial Economics, Vol. 15, pp. 885-915, 2021 [PDF, arXiv, DOI]

C. Beck, S. Becker, P. Cheridito, A. Jentzen, A. Neufeld:
Deep splitting method for parabolic PDEs
SIAM Journal on Scientific Computing (SISC), Vol. 43, No. 5, pp. A3135-A3154, 2021 [PDF, arXiv, DOI, Code]

A. Jentzen, B. Kuckuck, A. Neufeld, P. von Wurstemberger:
Strong error analysis for stochastic gradient descent optimization algorithms
IMA Journal of Numerical Analysis, Vol. 41, No. 1, pp. 455-492, 2021 [PDF, arXiv, DOI]

D. Bartl, M. Kupper, A. Neufeld:
Pathwise superhedging on prediction sets
Finance and Stochastics, Vol. 24, No. 1, pp. 215-248, 2020 [PDF, arXiv, DOI]

T. Fadina, A. Neufeld, T. Schmidt:
Affine processes under parameter uncertainty
Probability, Uncertainty and Quantitative Risk, Vol. 4, No. 1, pp. 1-35, 2019 [PDF, arXiv, DOI]

D. Bartl, M. Kupper, A. Neufeld:
Stochastic integration and differential equations for typical paths
Electronic Journal of Probability, Vol. 24, No. 97, pp. 1-21, 2019 [PDF, arXiv, DOI]

A. Neufeld, M. Sikic:
Nonconcave Robust Optimization with Discrete Strategies under Knightian Uncertainty
Mathematical Methods of Operations Research, Vol. 90, No. 2, pp. 229-253, 2019 [PDF, arXiv, DOI]

C. Liu, A. Neufeld:
Compactness Criterion for Semimartingale Laws and Semimartingale Optimal Transport
Transactions of the American Mathematical Society, Vol. 372, No. 1, pp. 187-231, 2019 [PDF, arXiv, DOI]

A. Neufeld:
Buy-and-Hold Property for Fully Incomplete Markets when Super-replicating Markovian Claims
International Journal of Theoretical and Applied Finance, Vol. 21, No. 8, 1850051, 2018 [PDF, arXiv, DOI]

A. Neufeld, M. Sikic:
Robust Utility Maximization in Discrete-Time Markets with Friction
SIAM Journal on Control and Optimization (SICON), Vol. 56, No. 3, pp. 1912-1937, 2018 [PDF, arXiv, DOI]

Y. Dolinsky, A. Neufeld:
Super-replication in Fully Incomplete Markets
Mathematical Finance, Vol. 28, No. 2, pp. 483-515, 2018 [PDF, arXiv, DOI]

A. Neufeld, M. Nutz:
Robust Utility Maximization with Lévy Processes
Mathematical Finance, Vol. 28, No. 1, pp. 82-105, 2018 [PDF, arXiv, DOI]

A. Neufeld, M. Nutz:
Nonlinear Lévy Processes and their Characteristics
Transactions of the American Mathematical Society, Vol. 369, No. 1, pp. 69-95, 2017 [PDF, arXiv, DOI]

A. Neufeld, M. Nutz:
Measurability of Semimartingale Characteristics with Respect to the Probability Law
Stochastic Processes and their Applications, Vol. 124, No. 11, pp. 3819-3845, 2014 [PDF, arXiv, DOI]

A. Neufeld, M. Nutz:
Superreplication under Volatility Uncertainty for Measurable Claims
Electronic Journal of Probability, Vol. 18, No. 48, pp. 1-14, 2013 [PDF, arXiv, DOI]

K. Du, A. Neufeld:
A note on asymptotic exponential arbitrage with exponentially decaying failure probability
Journal of Applied Probability, Vol. 50, No. 3, pp. 801-809, 2013 [PDF, arXiv, DOI]


Theses

A. Neufeld:
Knightian Uncertainty in Mathematical Finance
PhD Thesis ETH Zurich, Diss. ETH No. 22605, 2015 [PDF, ETH e-collection]


Miscellaneous

The Oracle of DLPhi [PDF]

This manuscript has mostly humouristic value. However, while the main theorem is based on a dubious application of the axiom of choice, it is a correct mathematical statement.
Therefore, this manuscript at least highlights the dangers of applying mathematical theory to real-world applications blindly.


Last update: February 28, 2024