5 citations · 14 across the 12 of their papers we have counts for
8 papers · 1 filter
A case study on different one-factor Cheyette models for short maturity caplet calibration
Arun Kumar Polala, Bernhard Hientzsch
In [1], we calibrated a one-factor Cheyette SLV model with a local volatility that is linear in the benchmark forward rate and an uncorrelated CIR stochastic variance to 3M caplets…
Reinforcement Learning and Deep Stochastic Optimal Control for Final Quadratic Hedging
Bernhard Hientzsch
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…
Parametric Differential Machine Learning for Pricing and Calibration
Arun Kumar Polala, Bernhard Hientzsch
Differential machine learning (DML) is a recently proposed technique that uses samplewise state derivatives to regularize least square fits to learn conditional expectations of fun…
A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging
Ali Fathi, Bernhard Hientzsch
We consider two data-driven approaches to hedging, Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control, under a stepwise mean-variance objective. We compare…
Estimating Future VaR from Value Samples and Applications to Future Initial Margin
Narayan Ganesan, Bernhard Hientzsch
Predicting future values at risk (fVaR) is an important problem in finance. They arise in the modelling of future initial margin requirements for counterparty credit risk and futur…
Backward Deep BSDE Methods and Applications to Nonlinear Problems
Yajie Yu, Bernhard Hientzsch, Narayan Ganesan
In this paper, we present a backward deep BSDE method applied to Forward Backward Stochastic Differential Equations (FBSDE) with given terminal condition at maturity that time-step…
Pricing Barrier Options with DeepBSDEs
Narayan Ganesan, Yajie Yu, Bernhard Hientzsch
This paper presents a novel and direct approach to price boundary and final-value problems, corresponding to barrier options, using forward deep learning to solve forward-backward…
Introduction to Solving Quant Finance Problems with Time-Stepped FBSDE and Deep Learning
Bernhard Hientzsch
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…