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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5-Year Impact Factor: 0.831
In this paper the authors show how the techniques introduced by Hurd and Zhou in 2010 can be used to derive a pricing framework for rainbow options by using the joint characteristic function of the logarithm of the underlying assets.
This paper extends the branching diffusion Monte Carlo method of Henry-Labordère et al to the case of parabolic partial differential equations with mixed local–nonlocal analytic nonlinearities.
This paper looks at ways of solving (decoupled) forward–backward stochastic differential equations numerically using regression trees.
In this paper the universal approximation theorem of artificial neural networks (ANNs) is applied to the stochastic alpha beta rho (SABR) stochastic volatility model in order to construct highly efficient representations.
This paper proposes two numerical solutions based on product optimal quantization for the pricing of Bermudan options on foreign exchange rates.
This paper presents several algorithms based on machine learning to solve hedging problems in incomplete markets.
This paper derives a new integral equation for American options under negative rates and shows how to solve this new equation through modifications to the modern and efficient algorithm of Andersen and Lake.
This research develops a new fast and accurate approximation method, inspired by the quadratic approximation, to get rid of the time steps required in finite-difference and simulation methods, while reducing error by making use of a machine learning…
Expansion method for pricing foreign exchange options under stochastic volatility and interest rates
This paper applies the smart expansion method to the Heston–Hull–White model, which admits stochastic interest rates to enhance the model, and obtains the expansion formula for pricing options in the model up to second order.
This paper proposes a simple and robust expected shortfall estimation method based on the tail-based normal approximation.
This paper employs a PDE approach to price several volatility derivatives under different transaction costs and illiquidity models.
This work presents an efficient computational framework for pricing a general class of exotic and vanilla options under a versatile stochastic volatility model.
Calibration of local-stochastic and path-dependent volatility models to vanilla and no-touch options
In this paper, the authors consider a large class of continuous semi-martingale models and propose a generic framework for their simultaneous calibration to vanilla and no-touch options.
Penalty methods for bilateral XVA pricing in European and American contingent claims by a partial differential equation model
Under some assumptions, the valuation of financial derivatives, including a value adjustment to account for default risk (the so-called XVA), gives rise to a nonlinear partial differential equation (PDE). The authors propose numerical methods for…
In this paper, the authors discuss how tree-based machine learning techniques can be used in the context of derivatives pricing.
The authors devise a neural network-based compression/completion methodology for financial nowcasting.
In this paper, the authors discuss all aspects of derivative pricing under the Heston–CLV model: calibration with an efficient Fourier method; a Monte Carlo simulation with second-order convergence; and accurate partial differential equation pricing…
In this paper, the authors introduce two mixing fractions that can be controlled separately to apply impact to the volatility-of-volatility and the correlation in a lognormal LSV model.
The authors present a methodology to generate future scenarios of interest rates for different credit ratings under a real-world probability measure.
The authors consider the problem of finding a valid covariance matrix in the foreign exchange market given an initial nonpositively semidefinite (non-PSD) estimate of such a matrix.