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Found 17 papers, 8 papers with code
$K$-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic Control
In this paper, we propose a novel $K$-nearest neighbor resampling procedure for estimating the performance of a policy from historical data containing realized episodes of a decision process generated under a different policy.
Convergence of the Euler--Maruyama particle scheme for a regularised McKean--Vlasov equation arising from the calibration of local-stochastic volatility models
Using this result, we prove the strong convergence of the Euler--Maruyama scheme to the particle system with rate 1/2 in the step-size and obtain an explicit dependence of the error on the regularisation parameters.
Convergence of policy gradient methods for finite-horizon exploratory linear-quadratic control problems
We study the global linear convergence of policy gradient (PG) methods for finite-horizon continuous-time exploratory linear-quadratic control (LQC) problems.
Hedging option books using neural-SDE market models
We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for hedging options.
Linear convergence of a policy gradient method for some finite horizon continuous time control problems
Despite its popularity in the reinforcement learning community, a provably convergent policy gradient method for continuous space-time control problems with nonlinear state dynamics has been elusive.
Estimating risks of option books using neural-SDE market models
In this paper, we examine the capacity of an arbitrage-free neural-SDE market model to produce realistic scenarios for the joint dynamics of multiple European options on a single underlying.
Arbitrage-free neural-SDE market models
Modelling joint dynamics of liquid vanilla options is crucial for arbitrage-free pricing of illiquid derivatives and managing risks of option trade books.
Simulation of conditional expectations under fast mean-reverting stochastic volatility models
In this short paper, we study the simulation of a large system of stochastic processes subject to a common driving noise and fast mean-reverting stochastic volatilities.
Numerical Analysis Numerical Analysis Computational Finance
Understanding Deep Architecture with Reasoning Layer
Recently, there is a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.
Detecting and repairing arbitrage in traded option prices
In addition, we show that removing arbitrage from prices data by our repair method can improve model calibration with enhanced robustness and reduced calibration error.
Understanding Deep Architectures with Reasoning Layer
Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.
Deep xVA solver -- A neural network based counterparty credit risk management framework
In this paper, we present a novel computational framework for portfolio-wide risk management problems, where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective.
Regularity and stability of feedback relaxed controls
This paper proposes a relaxed control regularization with general exploration rewards to design robust feedback controls for multi-dimensional continuous-time stochastic exit time problems.
A neural network based policy iteration algorithm with global $H^2$-superlinear convergence for stochastic games on domains
In this work, we propose a class of numerical schemes for solving semilinear Hamilton-Jacobi-Bellman-Isaacs (HJBI) boundary value problems which arise naturally from exit time problems of diffusion processes with controlled drift.
Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
In this paper, we establish that for a wide class of controlled stochastic differential equations (SDEs) with stiff coefficients, the value functions of corresponding zero-sum games can be represented by a deep artificial neural network (DNN), whose complexity grows at most polynomially in both the dimension of the state equation and the reciprocal of the required accuracy.
Calibration of a Hybrid Local-Stochastic Volatility Stochastic Rates Model with a Control Variate Particle Method
We propose a novel and generic calibration technique for four-factor foreign-exchange hybrid local-stochastic volatility models with stochastic short rates.
Numerical analysis of an extended structural default model with mutual liabilities and jump risk
We consider a structural default model in an interconnected banking network as in Lipton [International Journal of Theoretical and Applied Finance, 19(6), 2016], with mutual obligations between each pair of banks.
