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.
This paper presents a natural extension of the LGM that keeps the affine structure and generates an implied volatility smile.
The authors provide a bound for the error committed when using a Fourier method to price European options, when the underlying follows an exponential Lévy dynamic.
The authors build a whole family of local correlation models by combining the particle method with a new, simple idea.
In this paper the authors present an efficient convergent lattice method for Asian option pricing with superlinear complexity.
The authors present Sequential Monte Carlo (SMC) method for pricing barrier options.
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.
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.