The stochastic grid bundling method: efficient pricing of Bermudan options and their Greeks

DOI10.1016/j.amc.2015.07.085zbMath1410.91486OpenAlexW3003239501MaRDI QIDQ668683

Shashi Jain, Cornelis W. Oosterlee

Publication date: 19 March 2019

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://ir.cwi.nl/pub/23551

zbMATH Keywords

Bermudan optionsGreeks for American optionsMonte Carlo methods for American optionspricing American optionsstochastic grid bundling method

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (31)

Numerical valuation of Bermudan basket options via partial differential equations ⋮ Stochastic grid bundling method for backward stochastic differential equations ⋮ BENCHOP – SLV: the BENCHmarking project in Option Pricing – Stochastic and Local Volatility problems ⋮ Quasi-Monte Carlo simulation for American option sensitivities ⋮ A novel Monte Carlo approach to hybrid local volatility models ⋮ On pre-commitment aspects of a time-consistent strategy for a mean-variance investor ⋮ Multi-period mean-variance portfolio optimization based on Monte-Carlo simulation ⋮ A general continuous time Markov chain approximation for multi-asset option pricing with systems of correlated diffusions ⋮ Bermudan options pricing formulas in uncertain financial markets ⋮ KrigHedge: Gaussian Process Surrogates for Delta Hedging ⋮ Counterparty Credit Exposures for Interest Rate Derivatives using the Stochastic Grid Bundling Method ⋮ Simulated Greeks for American options ⋮ Mixing LSMC and PDE Methods to Price Bermudan Options ⋮ COMPUTING CREDIT VALUATION ADJUSTMENT FOR BERMUDAN OPTIONS WITH WRONG WAY RISK ⋮ Calculation of Exposure Profiles and Sensitivities of Options under the Heston and the Heston Hull-White Models ⋮ On the modelling of nested risk-neutral stochastic processes with applications in insurance ⋮ Computing credit valuation adjustment solving coupled PIDEs in the Bates model ⋮ Modern Monte Carlo Methods and GPU Computing ⋮ A New Approach for American Option Pricing: The Dynamic Chebyshev Method ⋮ Deep learning for ranking response surfaces with applications to optimal stopping problems ⋮ Efficient Computation of Various Valuation Adjustments Under Local Lévy Models ⋮ An SGBM-XVA demonstrator: a scalable Python tool for pricing XVA ⋮ ``Regression anytime with brute-force SVD truncation ⋮ A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options ⋮ Deep learning for CVA computations of large portfolios of financial derivatives ⋮ Deep optimal stopping ⋮ Explainable neural network for pricing and universal static hedging of contingent claims ⋮ Dynamic portfolio optimization with liquidity cost and market impact: a simulation-and-regression approach ⋮ Polynomial affine approach to HARA utility maximization with applications to OrnsteinUhlenbeck \(4/2\) models. ⋮ Pricing high-dimensional American options by kernel ridge regression ⋮ Speed-up credit exposure calculations for pricing and risk management

Cites Work

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