arXiv research

This paper improves market making strategies by incorporating non-Markovian features in order book models.

Paper tackles robust offline RL for non-Markovian processes, improving efficiency and applicability.

Path signatures improve hedging of exotic derivatives in non-Markovian models.

HONEM learns embeddings for higher-order networks, improving performance in various tasks.

DeepSynth synthesizes automata to guide deep RL agents through sparse, non-Markovian rewards.

New algorithm prices Bermudan options using Wiener chaos expansion for non-Markovian processes.

Signature portfolios approximate optimal wealth in non-Markovian markets.

Analyzes non-Markovian environments in stochastic approximation.

A new method predicts non-Markovian closure terms for complex systems.

Non-Markovian point process shows power-law scaling, similar to nonlinear Markovian process.

Paper introduces PRMs to learn non-Markovian stochastic rewards for reinforcement learning.

Deep learning solves non-Markovian FBSDEs for utility maximization.

Paper solves non-Markovian optimal stopping problems using discrete approximations.

ARL bridges non-Markovian decision processes with reinforcement learning, improving foresight and stability.

The paper values variable annuities using complex stochastic models and deep learning.

Data-driven model reduction captures non-Markovian dynamics using Koopman and Mori-Zwanzig formalisms.

Unified analytical tool for non-Markovian jump processes.

Investigates optimal consumption and investment strategies in non-Markovian markets with unbounded parameters.

Two signature-based methods solve optimal stopping in non-Markovian frameworks.

New algorithm for non-Markovian optimal stopping problems using Brownian motion.

The paper develops a deep signature approach for option pricing under non-Markovian stochastic volatility models.

Introduces alternators for modeling sequences, outperforming baselines.

Study optimal portfolios in a non-Markovian regime-switching model with random time horizon.

Study develops numerical schemes for non-Markovian volatility models with memory.

Study analyzes non-Markovian effects in financial markets over multiple years.

Paper improves neural ODEs for forecasting non-Markovian processes.

Paper tackles non-Markovian control problems with new learning methods.

We establish existence, uniqueness and regularity of solution results for a class of backward stochastic partial differential equations with singular terminal condition. The equation describes the value function of non-Markovian stochastic optimal control problem in which the terminal state of the controlled process is…

This paper studies a class of non$-$Markovian singular stochastic control problems, for which we provide a novel probabilistic representation. The solution of such control problem is proved to identify with the solution of a $Z-$constrained BSDE, with dynamics associated to a non singular underlying forward process. Du…

Paper introduces MVS to detect non-Markovian observations in reinforcement learning.

Extends pricing methods for index options under rough volatility.

The paper develops methods to price options under rough volatility models using BSPDEs.

This work analyzes $Q$-learning with adaptive stepsizes for finite-time convergence.

Partially observable environments present an important open challenge in the domain of sequential control learning with delayed rewards. Despite numerous attempts during the two last decades, the majority of reinforcement learning algorithms and associated approximate models, applied to this context, still assume Marko…

HS-FNO models non-Markovian PDEs by learning history and future states.

New model controls memory in seq2seq tasks, revealing learning regimes.

Efficient RL in PRMs with improved regret bound.

Develops RL algorithm for non-Markovian, non-stationary reward streams.

Describes state variables in sequential decision problems, linking them to Markovian and non-Markovian models.

Fractional processes have gained popularity in financial modeling due to the dependence structure of their increments and the roughness of their sample paths. The non-Markovianity of these processes gives, however, rise to conceptual and practical difficulties in computation and calibration. To address these issues, we…

By appealing to renewal theory we determine the equations that the mean exit time of a continuous-time random walk with drift satisfies both when the present coincides with a jump instant or when it does not. Particular attention is paid to the corrections ensuing from the non-Markovian nature of the process. We show t…

Paper solves Merton's portfolio problem in a non-Markovian, non-semimartingale model.

This work expands state-action aggregation methods for non-Markovian environments.

We study stochastic differential equations (SDEs) whose drift and diffusion coefficients are path-dependent and controlled. We construct a value process on the canonical path space, considered simultaneously under a family of singular measures, rather than the usual family of processes indexed by the controls. This val…

Study on kinetic Langevin diffusions and their couplings, showing subtle TV bounds and new non-Markovian couplings.

New approach tackles non-Markovian behavior in maternal health programs.

Two efficient ML techniques compute American option prices in high-dimensional models, including non-Markovian ones.

FLDD improves discrete diffusion models by learning a non-Markovian noising process.