Article ID: 1d02a7fe5c219a542033663142756deb2c36e853cc69fc23be44585d120b688b
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URL: https://www.risk.net/journal-of-computational-finance
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Journal of Computational Finance ISSN: 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: 0.5 5-Year Impact Factor: 0.7 CiteScore: 0.9 Latest papers High-performance applications of the nonuniform fast Fourier transform to option pricing The authors propose a framework for Fourier-based option pricing overcoming key limitations of standard fast Fourier transform methods. Optimality, sparsity and regularization parameter analysis for a risk diversification portfolio selection model This paper introduce the risk diversification mean–variance model: a convergent numerical algorithm for portfolio optimization in a model that mitigates against the risk of drastic market downturns while controlling transaction costs. The log-signature-based time series Wasserstein generative adversarial network This paper puts forward a method using neural networks to minimize the Wasserstein distance between log signatures to reduce the target dimension of the generator and increase performance An efficient algorithm to compute correlation Greeks This paper proposes a new algorithm that allows us to compute pairwise-correlation sensitivities in a Monte Carlo framework by modifying only one trajectory at a time Fast calculation of cheapest-to-deliver curves This paper puts forward an analytical, faster and accurate approximation to compute cheapest-to-deliver discount curves for multi-currency collateral. Strong order-one-half convergence of the projected Euler–Maruyama method for the Cox–Ingersoll–Ross model The authors investigate the projected Euler–Maruyama method for solving the Cox–Ingersoll–Ross model. Stochastic path-dependent volatility models for price–storage dynamics in natural gas markets and discrete-time swing option pricing With a focus on price–storage dynamics in natural gas markets, the authors propose a stochastic path-dependent volatility model with path dependence in both price volatility and storage increments Policy gradient methods for optimal trade execution in limit order books The authors investigate applications of policy gradient methods for the optimal execution of an asset position via limit orders. An efficient numerical method for pricing American options and their Greeks under the two-asset Kou jump-diffusion model This paper presents an efficient numerical method for solving a two-dimensional time-dependent PIDCP for American-style options under the two-asset Kou jump-diffusion model. Robust financial calibration: a Bayesian approach for neural stochastic differential equations This paper offers a Bayesian framework for the calibration of financial models using neural stochastic differential equations. Deep self-consistent learning of local volatility This paper offers an algorithm for calibrating local volatility from market option prices using deep self-consistent learning, by approximating both market option prices and local volatility using deep neural networks. Finite-difference solution ansatz approach in least-squares Monte Carlo This paper presents a novel technique, which is simple yet effective, to improve the accuracy and stability of the least-squares Monte Carlo method. Machine learning and a Hamilton–Jacobi–Bellman equation for optimal decumulation: a comparison study This paper ascertains a decumulation strategy for the holder of a defined contribution pension plan with an approach based on neural network optimization. Total value adjustment in a multicurrency framework with stochastic exchange rates and mean-reversion spreads The authors employ portfolio replication and dynamic hedging techniques to derive models for pricing financial derivatives in multicurrency markets and in the presence of counterparty credit risk. On deep portfolio optimization with stocks, bonds and options The authors put forward a neural-network machine learning algorithm for time-inconsistent portfolio optimization. Convexity adjustments à la Malliavin This paper puts forward a novel means to approximate convexity adjustments in a general interest rate model using Malliavin calculcus. An explicit scheme for pathwise cross valuation adjustment computations The authors put forward a simulation/regression scheme for a class of anticipated backward stochastic differential equations, where the coefficient entails a conditional expected shortfall of the martingale part of the solution. A flexible commodity skew model with maturity effects The authors propose an extension to the Andersen commodity curve and calibrate the model to market data for West Texas Intermediate crude oil and for natural gas. Pricing time-capped American options using a least squares Monte Carlo method This paper uses a modified least squares Monte Carlo method to price time-capped American options. Pricing American options under irrational behavior in a Markov regime-switching model with a finite-element method The authors investigate the problem of pricing American options under an irrational strategy, putting forward a method to negate this problem and demonstrate the performance of this model against alternatives.
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