arXiv subjects

Jean-Loup Dupret

Publications and source records attributed to Jean-Loup Dupret.

5 recordsLinked to original sources

arXiv2026

Deep kernel hedging

We introduce a deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods. The hedging functional is restricted to a reproducing kernel Hilbert space whose kernel is parameterized through a neural network embedding of the input features. The framework minimizes a regularized empirical risk under convex loss functions and can accommodate path-dependent information through truncated time-augmented signature features. We derive a generalized representer theorem for the joint hedging problem, reducing the empirical optimization to a finite-dimensional problem. To further reduce the computational cost associated with large kernel matrices, we develop a scalable random Fourier feature approximation and establish convergence guarantees. The random Fourier parameters are sampled once and remain fixed throughout training, while the deep kernel adapts to market data through the learned neural representation. We evaluate the performance of the proposed deep kernel approach on both synthetic and real data and compare it with standard kernel methods and classical deep hedging architectures. Numerical results indicate competitive and robust hedging performance, particularly in low-data regimes, which highlights the benefits of combining expressive neural representations with the inductive bias of kernel methods.

cs.LG↗

arXiv2026

ABIDES-MARL: A Multi-Agent Reinforcement Learning Environment for Optimal Execution with Endogenous Liquidity

Classical optimal execution models treat market impact as a pre-specified, exogenous process. However, when market makers adapt strategically, this assumption becomes a structural misspecification: execution dynamics depend on the policies and actions of other agents. The problem therefore ceases to be a single-agent control problem and instead becomes a finite-horizon stochastic game, in which liquidity emerges endogenously from the interactions among heterogeneous market players. We hence introduce ABIDES-MARL, a multi-agent reinforcement learning framework for studying optimal execution under endogenous liquidity in a realistic limit order book setting. The framework extends ABIDES-Gym to support multiple learning agents with synchronized decision periods that preserve proper information filtration and key market microstructure features. We validate the framework in an extended Kyle setting, where multiple learning agents recovers gradual price discovery. Numerical results then show that execution strategies learned under endogenous, adaptive liquidity differ fundamentally from those implied by fixed exogenous price-impact benchmarks. In particular, strategies optimized under exogenous impact perform poorly once market makers adapt strategically: when information is not balanced across traders and market makers, market makers exploit predictable execution and the market dynamics may degenerate. The framework therefore provides a reproducible foundation for studying strategic adaptation in realistic markets and contributes to the development of economically interpretable agentic AI systems in finance.

q-fin.TR↗

arXiv2026

INEUS: Iterative Neural Solver for High-Dimensional PIDEs

In this paper, we introduce INEUS, a meshfree iterative neural solver for partial integro-differential equations (PIDEs). The method replaces the explicit evaluation of nonlocal jump integrals with single-jump sampling and reformulates PIDE solving as a sequence of recursive regression problems. Like Physics-Informed Neural Networks (PINNs), INEUS learns global solutions over the entire space-time domain, yet it offers a more efficient treatment of nonlocal terms and avoids the computationally expensive differentiation of full PIDE residuals. These features make INEUS particularly well suited for high-dimensional PDEs and PIDEs. Supported by a contraction-based convergence proof for linear PIDEs, our numerical experiments show that INEUS delivers accurate and scalable solutions for various high-dimensional linear and nonlinear examples.

cs.LG↗

arXiv2026

General bounds on functionals of the lifetime under life table constraints in a joint actuarial-financial framework

In life insurance, life tables are used to estimate the survival distribution of individuals from a given population. However, these tables only provide survival probabilities at integer ages but no information about the distribution of deaths between two consecutive integer values. This incompleteness is particularly relevant for modern insurance products such as variable annuities, whose payoffs depend jointly on lifetime uncertainty and financial market performance. The valuation of such contracts must therefore be carried out in a joint actuarial-financial framework, as their values depend not only on the full information about mortality rates but also on the interaction between mortality risk, asset dynamics, and embedded guarantees. One frequent solution to this incompleteness is to postulate fractional age assumptions or mortality rate models, but it turns out that the results of the computations strongly depend on these restrictive assumptions. We hence derive upper and lower bounds of hybrid functionals of the lifetime with respect to mortality rates, which are compatible with the observed life table at integer ages and the given financial market. We derive two sets of results under distinct assumptions. In the first, we assume that each mortality trajectory is almost surely consistent with all the given one-year survival probabilities from the table. In the second, we consider a relaxed formulation that allows for deviations of the mortality rates while still being consistent in expectation with the given one-year reference survival probabilities. These distinct yet complementary approaches provide a new robust joint actuarial-financial framework for managing mortality risk in life insurance. They characterize the worst- and best-case contract values over all mortality processes that remain compatible with the observed life-table information and the financial market.

q-fin.RM↗

arXiv2026

Deep Learning for Continuous-Time Stochastic Control with Jumps

In this paper, we introduce a model-based deep-learning approach to solve finite-horizon continuous-time stochastic control problems with jumps. We iteratively train two neural networks: one to represent the optimal policy and the other to approximate the value function. Leveraging a continuous-time version of the dynamic programming principle, we derive two different training objectives based on the Hamilton-Jacobi-Bellman equation, ensuring that the networks capture the underlying stochastic dynamics. Empirical evaluations on different problems illustrate the accuracy and scalability of our approach, demonstrating its effectiveness in solving complex high-dimensional stochastic control tasks.

cs.LG↗