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Found 12 papers, 8 papers with code
Measuring Name Concentrations through Deep Learning
We propose a new deep learning approach for the quantification of name concentration risk in loan portfolios.
On the Relevance and Appropriateness of Name Concentration Risk Adjustments for Portfolios of Multilateral Development Banks
Sovereign loan portfolios of Multilateral Development Banks (MDBs) typically consist of only a small number of borrowers and hence are heavily exposed to single name concentration risk.
On intermediate Marginals in Martingale Optimal Transportation
We study the influence of additional intermediate marginal distributions on the value of the martingale optimal transport problem.
Neural networks can detect model-free static arbitrage strategies
In this paper we demonstrate both theoretically as well as numerically that neural networks can detect model-free static arbitrage opportunities whenever the market admits some.
Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty
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.
Markov Decision Processes under Model Uncertainty
We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting.
Improved Robust Price Bounds for Multi-Asset Derivatives under Market-Implied Dependence Information
We show how inter-asset dependence information derived from market prices of options can lead to improved model-free price bounds for multi-asset derivatives.
Detecting data-driven robust statistical arbitrage strategies with deep neural networks
We present an approach, based on deep neural networks, that allows identifying robust statistical arbitrage strategies in financial markets.
Robust deep hedging
We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black-Scholes model as well as the constant elasticity of variance (CEV) model as special cases.
A deep learning approach to data-driven model-free pricing and to martingale optimal transport
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
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
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.
