The Journal of Computational Finance is an international peer-reviewed journal dedicated to advancing knowledge in the area of financial mathematics. The journal is focussed on the measurement, management and analysis of financial risk, and provides detailed insight into numerical and computational techniques in the pricing, hedging and risk management of financial instruments.
The journal welcomes papers dealing with innovative computational techniques in the following areas:
- Numerical solutions of pricing equations: finite differences, finite elements, and spectral techniques in one and multiple dimensions.
- Simulation approaches in pricing and risk management: advances in Monte Carlo and quasi-Monte Carlo methodologies; new strategies for market factors simulation.
- Optimization techniques in hedging and risk management.
- Fundamental numerical analysis relevant to finance: effect of boundary treatments on accuracy; new discretization of time-series analysis.
- Developments in free-boundary problems in finance: alternative ways and numerical implications in American option pricing.
A reduced basis method for parabolic partial differential equations with parameter functions and application to option pricing
The authors introduce an RB space–time variational approach for parametric PPDEs with coefficient parameters and a variable initial condition.
The authors propose a general framework to assess the probability of backtest overfitting (PBO).
Efficient estimation of sensitivities for counterparty credit risk with the finite difference Monte Carlo method
This paper applies a variety of second-order finite difference schemes to the SABR arbitrage-free density problem and explores alternative formulations.
A mixed Monte Carlo and partial differential equation variance reduction method for foreign exchange options under the Heston–Cox–Ingersoll–Ross model
The paper concerns a hybrid pricing method build upon a combination of Monte Carlo and PDE approach for FX options under the four-factor Heston-CIR model.
The Authors introduce a closed-form approximation for the forward implied volatilities.
Numerical solution of the Hamilton–Jacobi–Bellman formulation for continuous-time mean–variance asset allocation under stochastic volatility
The paper deals with robust and accurate numerical solution methods for the nonlinear Hamilton–Jacobi–Bellman partial differential equation (PDE), which describes the dynamic optimal portfolio selection problem.
This paper presents a high-performance spectral collocation method for the computation of American put and call option prices.
Adjusting exponential Lévy models toward the simultaneous calibration of market prices for crash cliquets
The authors propose so-called tail thinning strategies that may be employed to better connect the calibrated models to the crash cliquets prices.
An exact and efficient method for computing cross-Gammas of Bermudan swaptions and cancelable swaps under the Libor market model
A new simulation algorithm for computing the Hessians of Bermudan swaptions and cancelable swaps is presented.
Pricing swing options in electricity markets with two stochastic factors using a partial differential equation approach
This paper considers the numerical valuation of swing options in electricity markets based on a two-factor model.
Efficient computation of exposure profiles on real-world and risk-neutral scenarios for Bermudan swaptions
In the paper, real-world and risk-neutral scenarios are combined for the valuation of the exposure values of Bermudan swaptions on real-world Monte Carlo paths.
The authors propose a method for determining an arbitrage-free density implied by the Hagan formula.
This paper reviews and extends the saddlepoint methods currently available to measure credit risk.
The authors develop a technique, based on numerical inversion, to compute the prices and Greeks of lookback options driven by Lévy processes.
In this paper the authors provide a comprehensive treatment of the discretization effect under general stochastic volatility dynamics.
The authors propose a novel method for efficiently comparing the performance of different stopping times.
The authors propose an efficient, novel numerical scheme for solving the stochastic Heath–Jarrow–Morton interest rate model.
Accelerated trinomial trees applied to American basket options and American options under the Bates model
This paper introduces accelerated trinomial trees, a novel efficient lattice method for the numerical pricing of derivative securities.
This paper develops a new scheme for improving an approximation method of a probability density function.
The authors propose stratified approximations of option prices using the gamma and lognormal distributions, with an application to bond pricing in the Dothan model.
Efficient solution of backward jump-diffusion partial integro-differential equations with splitting and matrix exponentials
A unified approach for solving jump-diffusion partial integro differential equations is proposed.
In this paper the use of B-splines is advocated for volatility modeling within the calibration of stochastic local volatility (SLV) models and for the parameterization of an arbitrage-free implied volatility surface calibrated to sparse option data.
The efficient application of automatic differentiation for computing gradients in financial applications
Automatic differentiation is the theme of this paper. The authors show that many functions in calibration and inverse problems, exhibit a natural substitution structure. A significant speedup is achieved compared with common reverse-mode AD.