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.
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Journal Impact Factor: 1.417
5-Year Impact Factor: 1.222
The authors propose a method for credit valuation adjustment evaluation that avoids the need for simulation while maintaining efficiency and accuracy.
Optimal damping with a hierarchical adaptive quadrature for efficient Fourier pricing of multi-asset options in Lévy models
The authors put forward a a method for pricing European multi-asset options intended to address challenges related to the choice of damping parameters and the treatment of high dimensionality when designing methods for Fourier pricing options.
The author investigates quantiles, expectiles and extremiles in tail estimators for linear regression.
This paper proposes the use of neural stochastic differential equations as a means to learn approximately optimal control variates, reducing variance as trajectories of the SDEs are simulated.
This paper applies the Monte Carlo tree search as a method for replication in the presence of risk and market friction
The authors investigate the surface SVI model with three with three parameters, applying the SVI results to give the nobutterfly- arbitrage domain
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.
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.
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.
The authors investigate and partially solve theoretical and empirical problems for the joint modelling of bid and ask prices.
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…
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…
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.
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.
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.
The authors develop and apply a formula to derive closed-form expressions in particular quantitative finance cases.
This paper investigates various techniques for the CIR and Heston models.