Search Results for author: Ariel Neufeld

Found 18 papers, 11 papers with code

Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty

1 code implementation30 Sep 2022 Ariel Neufeld, Julian Sester

We present a novel $Q$-learning algorithm to solve distributionally robust Markov decision problems, where the corresponding ambiguity set of transition probabilities for the underlying Markov decision process is a Wasserstein ball around a (possibly estimated) reference measure.

Chaotic Hedging with Iterated Integrals and Neural Networks

1 code implementation21 Sep 2022 Ariel Neufeld, Philipp Schmocker

In this paper, we extend the Wiener-Ito chaos decomposition to the class of diffusion processes, whose drift and diffusion coefficient are of linear growth.

Markov Decision Processes under Model Uncertainty

1 code implementation13 Jun 2022 Ariel Neufeld, Julian Sester, Mario Šikić

We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting.

Portfolio Optimization

Binary Spatial Random Field Reconstruction from Non-Gaussian Inhomogeneous Time-series Observations

1 code implementation7 Apr 2022 Shunan Sheng, Qikun Xiang, Ido Nevat, Ariel Neufeld

We develop a new model for binary spatial random field reconstruction of a physical phenomenon which is partially observed via inhomogeneous time-series data.

Time Series

Improved Robust Price Bounds for Multi-Asset Derivatives under Market-Implied Dependence Information

1 code implementation3 Apr 2022 Jonathan Ansari, Eva Lütkebohmert, Ariel Neufeld, Julian Sester

We show how inter-asset dependence information derived from observed market prices of liquidly traded options can lead to improved model-free price bounds for multi-asset derivatives.

Detecting data-driven robust statistical arbitrage strategies with deep neural networks

1 code implementation7 Mar 2022 Ariel Neufeld, Julian Sester, Daiying Yin

We present an approach, based on deep neural networks, that allows identifying robust statistical arbitrage strategies in financial markets.

Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function

1 code implementation19 Jul 2021 Dong-Young Lim, Ariel Neufeld, Sotirios Sabanis, Ying Zhang

We consider non-convex stochastic optimization problems where the objective functions have super-linearly growing and discontinuous stochastic gradients.

Stochastic Optimization Transfer Learning

A deep learning approach to data-driven model-free pricing and to martingale optimal transport

1 code implementation21 Mar 2021 Ariel Neufeld, Julian Sester

We introduce a novel and highly tractable supervised learning approach based on neural networks that can be applied for the computation of model-free price bounds of, potentially high-dimensional, financial derivatives and for the determination of optimal hedging strategies attaining these bounds.

On the stability of the martingale optimal transport problem: A set-valued map approach

no code implementations4 Feb 2021 Ariel Neufeld, Julian Sester

its marginals was recently established in Backhoff-Veraguas and Pammer [2] and Wiesel [21].

Probability Optimization and Control Mathematical Finance

Model-free price bounds under dynamic option trading

1 code implementation4 Jan 2021 Ariel Neufeld, Julian Sester

In this paper we extend discrete time semi-static trading strategies by also allowing for dynamic trading in a finite amount of options, and we study the consequences for the model-independent super-replication prices of exotic derivatives.

Deep learning based numerical approximation algorithms for stochastic partial differential equations and high-dimensional nonlinear filtering problems

no code implementations2 Dec 2020 Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld

In this article we introduce and study a deep learning based approximation algorithm for solutions of stochastic partial differential equations (SPDEs).

Duality Theory for Robust Utility Maximisation

no code implementations16 Jul 2020 Daniel Bartl, Michael Kupper, Ariel Neufeld

In this paper we present a duality theory for the robust utility maximisation problem in continuous time for utility functions defined on the positive real axis.

Model-free bounds for multi-asset options using option-implied information and their exact computation

1 code implementation25 Jun 2020 Ariel Neufeld, Antonis Papapantoleon, Qikun Xiang

We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested in the computation of model-free upper and lower bounds for their arbitrage-free prices.

Optimization and Control Probability Computational Finance Mathematical Finance Pricing of Securities

Forecasting directional movements of stock prices for intraday trading using LSTM and random forests

3 code implementations21 Apr 2020 Pushpendu Ghosh, Ariel Neufeld, Jajati Keshari Sahoo

Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0. 41% and of 0. 39% with respect to LSTM and random forests, respectively.

Stock Market Prediction

Supermartingale deflators in the absence of a numéraire

no code implementations16 Jan 2020 Philipp Harms, Chong Liu, Ariel Neufeld

In this paper we study arbitrage theory of financial markets in the absence of a num\'eraire both in discrete and continuous time.

Deep splitting method for parabolic PDEs

no code implementations8 Jul 2019 Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld

In this paper we introduce a numerical method for nonlinear parabolic PDEs that combines operator splitting with deep learning.

Strong error analysis for stochastic gradient descent optimization algorithms

no code implementations29 Jan 2018 Arnulf Jentzen, Benno Kuckuck, Ariel Neufeld, Philippe von Wurstemberger

Stochastic gradient descent (SGD) optimization algorithms are key ingredients in a series of machine learning applications.

Numerical Analysis Probability

Cannot find the paper you are looking for? You can Submit a new open access paper.