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
Automatic adjoint differentiation for special functions involving expectations
The authors put forward AAD algorithms for functions involving expectations and use their technique to calibrate European options.
Neural stochastic differential equations for conditional time series generation using the Signature-Wasserstein-1 metric
Using conditional neural stochastic differential equations, the authors propose a means to improve the efficiency of generative adversarial networks and test their model against other classical approaches.
Toward a unified implementation of regression Monte Carlo algorithms
The authors put forward a publicly available computational template for machine learning, named mlOSP, which presents a unified numerical implementation of RMC approaches for optimal stopping.
A general control variate method for time-changed Lévy processes: an application to options pricing
The authors put forward a novel control variate method for time-changed Lévy models and demonstrate an efficient reduction of the variance of Monte Carlo in numerical experiments.
Modeling the bid and ask prices of options
The authors investigate and partially solve theoretical and empirical problems for the joint modelling of bid and ask prices.
Efficient numerical valuation of European options under the two-asset Kou jump-diffusion model
The authors extend a technique proposed by Toivanen (2008), arriving at an algorithm evaluating the nonlocal double integral appearing in the two-dimensional Kou PIDE and perform several numerical experiments to demonstrate actual convergence behavior…
Sharp L¹-approximation of the log-Heston stochastic differential equation by Euler-type methods
The authors employ Euler-type methods to study the L¹ approximation of the log-Heston stochastic differential equation at equidistant time points.
An optimal control strategy for execution of large stock orders using long short-term memory networks
Using a general power law in the Almgren and Chriss model and real data, the authors simulate the execution of a large stock order with an appropriately trained LSTM network.
Estimating risks of European option books using neural stochastic differential equation market models
The authors investigate how arbitrage-free neural stochastic differential equation market models can produce realistic scenarios for the joint dynamics of multiple European options on a single underlying and demonstrate how they can be used as a risk…
Robust pricing and hedging via neural stochastic differential equations
The authors propose a model called neural SDE and demonstrate how this model can make it possible to find robust bounds for the prices of derivatives and the corresponding hedging strategies.
Least squares Monte Carlo methods in stochastic Volterra rough volatility models
The authors offer a VIX pricing algorithm for stochastic Volterra rough volatility models where the volatility is dependent of the vol-of-vol which reproduces key features of real-world data.
Analytical conversion between implied volatilities based on different dividend models
The authors propose an explicit formula for the conversion of implied volatilities corresponding to dividend modelling assumptions which covers a wide range of strikes and maturities.
Adjoint differentiation for generic matrix functions
The authors develop and apply a formula to derive closed-form expressions in particular quantitative finance cases.
Simulating the Cox–Ingersoll–Ross and Heston processes: matching the first four moments
This paper investigates various techniques for the CIR and Heston models.
Multilevel Monte Carlo simulation for VIX options in the rough Bergomi model
The authors consider the pricing of the Chicago Board options Exchange VIX, demonstrating experiments highlighting the efficiency of a multilevel approach in pricing of VIX options.
Pricing the correlation skew with normal mean–variance mixture copulas
The author puts forward a pricing methodology for European multi-asset derivatives that consists of a flexible copula-based method that can reproduce the correlation skew and is efficient enough for use with large baskets.
Optimal trade execution with uncertain volume target
This paper demonstrates that risk-averse traders can benefit from delaying trades using a model that accounts for volume uncertainty.
A general firm value model under partial information
The authors propose a general structural default model combining enhanced economic relevance and affordable computational complexity.
Deep learning for efficient frontier calculation in finance
The author puts forward a means to calculate the efficient frontier in the Mean-Variance and Mean-CVaR portfolio optimization problems using deep neural network algorithms.
Subsampling and other considerations for efficient risk estimation in large portfolios
The authors apply multilevel Monte Carlo simulation to the problems inherent in computing risk measures of a financial portfolio with large numbers of derivatives.
