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Found 10 papers, 2 papers with code
Reinforcement Learning and Deep Stochastic Optimal Control for Final Quadratic Hedging
We consider two data driven approaches, Reinforcement Learning (RL) and Deep Trajectory-based Stochastic Optimal Control (DTSOC) for hedging a European call option without and with transaction cost according to a quadratic hedging P&L objective at maturity ("variance-optimal hedging" or "final quadratic hedging").
A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging
We consider two data-driven approaches to hedging, Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control, under a stepwise mean-variance objective.
Parametric Differential Machine Learning for Pricing and Calibration
To allow convenient and efficient simulation of processes and functionals and in particular the corresponding computation of samplewise derivatives, we propose to specify the processes and functionals in a low-code way close to mathematical notation which is then used to generate efficient computation of the functionals and derivatives in TensorFlow.
A Flexible Commodity Skew Model with Maturity Effects
We propose a non-parametric extension with leverage functions to the Andersen commodity curve model.
Estimating Future VaR from Value Samples and Applications to Future Initial Margin
Predicting future values at risk (fVaR) is an important problem in finance.
Calibrating Local Volatility Models with Stochastic Drift and Diffusion
We give conditions under which a local volatility can exist given European option prices, stochastic interest rate model parameters, and correlations.
Backward Deep BSDE Methods and Applications to Nonlinear Problems
To time-step the BSDE backward, one needs to solve a nonlinear problem.
Pricing Barrier Options with DeepBSDEs
In the PDE formulation, this corresponds to adding boundary conditions to the final value problem.
Extensions of Dupire Formula: Stochastic Interest Rates and Stochastic Local Volatility
We provide derivations for the case where both short rates are given as single factor processes and present the limits for a single stochastic rate or all deterministic short rates.
Introduction to Solving Quant Finance Problems with Time-Stepped FBSDE and Deep Learning
In this introductory paper, we discuss how quantitative finance problems under some common risk factor dynamics for some common instruments and approaches can be formulated as time-continuous or time-discrete forward-backward stochastic differential equations (FBSDE) final-value or control problems, how these final value problems can be turned into control problems, how time-continuous problems can be turned into time-discrete problems, and how the forward and backward stochastic differential equations (SDE) can be time-stepped.
