Paper: arXiv 2502.11706
Abstract
A deep BSDE approach is presented for the pricing and delta-gamma hedging of high-dimensional Bermudan options, with applications in portfolio risk management. Large portfolios of a mixture of multi-asset European and Bermudan derivatives are cast into the framework of discretely reflected BSDEs. This system is discretized by the One Step Malliavin scheme (Negyesi et al. [“2024, 2025”]) of discretely reflected Markovian BSDEs, which involves a $Γ$ process, corresponding to second-order sensitivities of the associated option prices. The discretized system is solved by a neural network regression Monte Carlo method, efficiently for a large number of underlyings. The resulting option Deltas and Gammas are used to discretely rebalance the corresponding replicating strategies. Numerical experiments are presented on both high-dimensional basket options and large portfolios consisting of multiple options with varying early exercise rights, moneyness and volatility. These examples demonstrate the robustness and accuracy of the method up to $100$ risk factors. The resulting hedging strategies significantly outperform benchmark methods both in the case of standard delta- and delta-gamma hedging.
Complexity vs Empirical Score
- Math Complexity: 9.2/10
- Empirical Rigor: 8.5/10
- Quadrant: Holy Grail — high math complexity, high empirical rigor
Why this score: The paper employs advanced stochastic calculus including BSDEs, Malliavin calculus, and high-dimensional neural network approximations, representing a very high math complexity. The method is demonstrated with numerical experiments on portfolios up to 100 risk factors, showing backtest-ready implementation with empirical validation of hedging performance.
Research Flowchart
flowchart TD A["Research Goal"] --> B["Problem Formulation"] B --> C["Discretization"] C --> D["Computational Engine"] D --> E["Outcomes & Validation"] A["Research Goal<br/>Simultaneous Pricing & Delta-Gamma Hedging<br/>High-Dim Bermudan Portfolios"] B["Problem Formulation<br/>Large portfolios of multi-asset options<br/>Cast as Discretely Reflected BSDEs"] C["Discretization<br/>One Step Malliavin Scheme<br/>(Includes Γ-process for Gamma)"] D["Computational Engine<br/>Neural Network Regression<br/>Monte Carlo Simulation"] E["Outcomes & Validation<br/>Accurate Deltas/Gammas up to 100 dimensions<br/>Superior hedging performance<br/>vs. benchmarks"]
Cited References (50)
- A comparison of biased simulation schemes for stochastic volatility models (2010) — link
- Two algorithms for the discrete time approximation of Markovian backward stochastic differential equations under local conditions (2015) — link
- Convergence of Monte Carlo simulations involving the mean-reverting square root process (2005) — link
- Representations and regularities for solutions to BSDEs with reflections (2005) — link
- Discrete-time approximation for continuously and discretely reflected BSDEs (2008) — link
- Reflected solutions of backward SDE ’s, and related obstacle problems for PDE ’s (1997) — link
- DGM : A deep learning algorithm for solving partial differential equations (2018) — link
- The Tracking Error Rate of the Delta - Gamma Hedging Strategy (2012) — link
and 42 more
- An Introduction to Computational Finance Without Agonizing Pain
- Backward Stochastic Differential Equations (2017) — link
- Deep Runge - Kutta schemes for BSDEs — → 2212.14372
- The One Step Malliavin scheme: new discretization of BSDEs implemented with deep learning regressions (2021) — link
- Deep learning for CVA computations of large portfolios of financial derivatives (2021) — link
- Brownian Motion and Stochastic Calculus (1998) — link
- The Malliavin Calculus and Related Topics (2006) — link
- Deep backward multistep schemes for nonlinear PDEs and approximation error analysis (2020)
- Path regularity and explicit convergence rate for BSDE with truncated quadratic growth (2010) — link
- Numerical simulation of quadratic BSDEs (2016) — link
- Efficient numerical Fourier methods for coupled forward–backward SDEs (2016) — link
- Deep neural network framework based on backward stochastic differential equations for pricing and hedging American options in high dimensions (2021) — link
- Deep backward schemes for high-dimensional nonlinear PDEs (2020) — link
- Backward stochastic differential equations and quasilinear parabolic partial differential equations (1992) — link
- A regression-based Monte Carlo method to solve backward stochastic differential equations (2005) — link
- A quantization algorithm for solving multidimensional discrete-time optimal stopping problems (2003) — link
- A Fourier Cosine Method for an Efficient Computation of Solutions to BSDEs (2015) — link
- A forward scheme for backward SDEs (2007) — link
- Computation of Greeks for Barrier and Lookback Options Using Malliavin Calculus (2003) — link
- Hedging of the European Option of the Exotic Type with a Nonsmooth Payoff Function (2017)
- Applications of Malliavin calculus to Monte Carlo methods in finance (1999) — link
- Discrete-time approximation and Monte - Carlo simulation of backward stochastic differential equations (2004) — link
- Pricing and Hedging American - Style Options with Deep Learning (2020) — link
- Two- Dimensional Fourier Cosine Series Expansion Method for Pricing Financial Options (2012) — link
- Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks (1990) — link
- Solving high-dimensional partial differential equations using deep learning (2018) — link
- Solving forward-backward stochastic differential equations explicitly — a four step scheme (1994) — link
- Simulation of BSDEs by Wiener chaos expansion (2014) — link
- Reflected Backward SDEs (2017) — link
- Convergence of the deep BSDE method for coupled FBSDEs (2020) — link
- Backward Stochastic Differential Equations in Finance (1997) — link
- Malliavin calculus for backward stochastic differential equations and application to numerical solutions (2011) — link
- Pricing early-exercise and discrete barrier options by fourier-cosine series expansions (2009) — link
- Reflected extension to the One Step Malliavin scheme and its corresponding deep BSDE approximations (working paper) (2024)
- The One Step Malliavin scheme: new discretization of BSDEs implemented with deep learning regressions (2024) — link
- The Value of an Option to Exchange One Asset for Another (1978) — link
- LSQR : An Algorithm for Sparse Linear Equations and Sparse Least Squares (1982) — link
- Algorithm 583: LSQR : Sparse Linear Equations and Least Squares Problems (1982) — link
- Continuous Time Wishart Process for Stochastic Risk (2006) — link
- Efficient Simulation of the Wishart Model (2009) — link
- Approximation Error Analysis of Some Deep Backward Schemes for Nonlinear PDEs (2022) — link
- Deep Quadratic Hedging — → 2212.12725
2 of 50 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.