Journal of Computational Finance

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	0.5
2024	0.80

SJR (SCImago Journal Rank)

Year	Value
2024	0.198

Quartile

Year	Value
2024	Q4

h-index

Year	Value
2024	17

Journal Rank

Year	Value
2024	21310

Journal Citation Indicator

Year	Value
2024	19

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Economics, Econometrics and Finance and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Option valuation using the fast Fourier transform

  Citation: 1607

  Authors: Peter, Dilip

• Valuation of mortgage-backed securities using Brownian bridges to reduce effective dimension

  Citation: 367

  Authors: Russel, William, Art

• Option pricing by transform methods: extensions, unification and error control

  Citation: 248

  Authors: Roger

• Simple and efficient simulation of the Heston stochastic volatility model

  Citation: 222

  Authors: Leif

• A new PDE approach for pricing arithmetic average Asian options

  Citation: 209

  Authors: Jan

• A stochastic mesh method for pricing high-dimensional American options

  Citation: 208

  Authors: Mark, Paul

• Non-parametric calibration of jump–diffusion option pricing models

  Citation: 158

  Authors: Rama, Peter

• Pricing options on realized variance in the Heston model with jumps in returns and volatility

  Citation: 137

  Authors: Artur

• Robust numerical methods for PDE models of Asian options

  Citation: 129

  Authors: R, Peter, K

• Convergence of Monte Carlo simulations involving the mean-reverting square root process

  Citation: 125

  Authors: Desmond, Xuerong

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