Journal of Computational Finance
ISSN:
1460-1559 (print)
1755-2850 (online)
Editor-in-chief: Christoph Reisinger
About this journal
The Journal of Computational Finance is an international peer-reviewed journal dedicated to advancing knowledge in the area of financial mathematics. The journal is focused 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.
Abstracting and Indexing: Scopus; Web of Science - Social Science Index; MathSciNet; EconLit; Econbiz; and Cabell’s Directory
Journal Metrics:
Journal Impact Factor: 1.417
5-Year Impact Factor: 1.222
CiteScore: 1.4
Latest papers
A chaos expansion approach for the pricing of contingent claims
Efficient variations of the Fourier transform in applications to option pricing
Fourier transform algorithms for pricing and hedging discretely sampled exotic variance products and volatility derivatives under additive processes
Application of the improved fast Gauss transform to option pricing under jump-diffusion processes
Option calibration of exponential Lévy models: confidence intervals and empirical results
Pricing American-style options by Monte Carlo simulation: alternatives to ordinary least squares
The authors investigate the performance of the ordinary least squares (OLS) regression method in Monte Carlo simulation algorithms for pricing American options.
Value function approximation or stopping time approximation: a comparison of two recent numerical methods for American option pricing using simulation and regression
Counterparty credit risk pricing and measurement of swaption portfolios
This paper introduces a technique for pricing and risk measurement of portfolios containing swaption contracts in the presence of counterparty credit risk, under general market model and volatility assumptions.
Numerical algorithms for research and development stochastic control models
The authors consider the optimal strategy of research and development (R&D) expenditure adopted by a firm that engages in R&D to develop an innovative product to be launched in the market.
Optimizing the Omega ratio using linear programming
Adjoint algorithmic differentiation: calibration and implicit function theorem
Credit risk contributions under the Vasicek one-factor model: a fast wavelet expansion approximation
Robust calibration of financial models using Bayesian estimators
Quadratic finite element and preconditioning methods for options pricing in the SVCJ model
Monte Carlo pricing in the Schöbel–Zhu model and its extensions
TR-BDF2 for fast stable American option pricing
Robust and accurate Monte Carlo simulation of (cross-) Gammas for Bermudan swaptions in the LIBOR market model
Simulation of Lévy processes and option pricing
Exact simulation pricing with Gamma processes and their extensions
Variance–optimal hedging for discrete-time processes with independent increments: application to electricity markets