Penalty and penalty-like methods for nonlinear HJB PDEs
DOI10.1016/j.amc.2022.127015OpenAlexW4226269424WikidataQ114210911 ScholiaQ114210911MaRDI QIDQ2139765
Christina C. Christara, Ruining Wu
Publication date: 19 May 2022
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2022.127015
finite differencespartial differential equationstransaction costscontrol problempenalty methodsBlack-ScholesCrank-Nicolsonnonlinear iterationHamilton-Jacobi-Bellman (HJB) equationstock borrowing fees
Numerical methods (including Monte Carlo methods) (91G60) Newton-type methods (49M15) Finite difference methods for initial value and initial-boundary value problems involving PDEs (65M06) Stability and convergence of numerical methods for initial value and initial-boundary value problems involving PDEs (65M12) Derivative securities (option pricing, hedging, etc.) (91G20)
Related Items (1)
Cites Work
- Unnamed Item
- The Pricing of Options and Corporate Liabilities
- Finite element solution of diffusion problems with irregular data
- On Leland's strategy of option pricing with transactions costs
- A penalty scheme and policy iteration for nonlocal HJB variational inequalities with monotone nonlinearities
- Quadratic Convergence for Valuing American Options Using a Penalty Method
- The Effect of Nonsmooth Payoffs on the Penalty Approximation of American Options
- Numerical Methods for Nonlinear PDEs in Finance
- Maximal Use of Central Differencing for Hamilton–Jacobi–Bellman PDEs in Finance
- High-order compact scheme for solving nonlinear Black–Scholes equation with transaction cost
- Multigrid for American option pricing with stochastic volatility
- Convergence Properties of Policy Iteration
- A New Convergent Explicit Tree-Grid Method for HJB Equations in One Space Dimension
- Numerical convergence properties of option pricing PDEs with uncertain volatility
- The numerical approximation of nonlinear Black–Scholes model for exotic path-dependent American options with transaction cost
- Solving high-dimensional partial differential equations using deep learning
- Implicit solution of uncertain volatility/transaction cost option pricing models with discretely observed barriers.
This page was built for publication: Penalty and penalty-like methods for nonlinear HJB PDEs