The Journal of Computational Finance is an international peer-reviewed journal dedicated to advancing knowledge in the area of financial mathematics. The journal is focussed 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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This paper considers the problem of European option pricing in the presence of a proportional transaction cost when the price of the underlying follows a jump–diffusion process.
This paper extends and refines the method of option pricing by frame projection of risk-neutral densities to incorporate B-splines.
This paper proposes a generalized risk budgeting approach to portfolio construction.
This paper proposes an efficient algorithm to value two popular crediting formulas found in equity-indexed annuities – APP and MPP – under general Lévy-process-based index returns.
In this paper, the authors propose a new method of constructing volatility surfaces for foreign exchange options.
In this paper, the author considers a special type of nonlinear PDE that arises by applying optimization to some financial problems.
This paper proposes a nonparametric local volatility Cheyette model and applies it to pricing interest rate swaptions.
This paper introduces a local volatility model for the valuation of options on commodity futures by using European vanilla option prices.
Investment opportunities forecasting: a genetic programming-based dynamic portfolio trading system under a directional-change framework
This paper presents an autonomous effective trading system devoted to the support of decision-making processes in the financial market domain.
This paper proposes a method for overhedging weighted variance using only a finite number of maturities.
This paper presents a natural extension of the LGM that keeps the affine structure and generates an implied volatility smile.
The authors provide a bound for the error committed when using a Fourier method to price European options, when the underlying follows an exponential Lévy dynamic.
The authors build a whole family of local correlation models by combining the particle method with a new, simple idea.
In this paper the authors present an efficient convergent lattice method for Asian option pricing with superlinear complexity.
The authors present Sequential Monte Carlo (SMC) method for pricing barrier options.
A reduced basis method for parabolic partial differential equations with parameter functions and application to option pricing
The authors introduce an RB space–time variational approach for parametric PPDEs with coefficient parameters and a variable initial condition.
The authors propose a general framework to assess the probability of backtest overfitting (PBO).
Efficient estimation of sensitivities for counterparty credit risk with the finite difference Monte Carlo method
This paper applies a variety of second-order finite difference schemes to the SABR arbitrage-free density problem and explores alternative formulations.
A mixed Monte Carlo and partial differential equation variance reduction method for foreign exchange options under the Heston–Cox–Ingersoll–Ross model
The paper concerns a hybrid pricing method build upon a combination of Monte Carlo and PDE approach for FX options under the four-factor Heston-CIR model.