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
This paper provides a comprehensive review of the field of neural networks, comparing articles in terms of input features, output variables, benchmark models, performance measures, data partition methods and underlying assets. Related work and…
The author considers a classical term structure model framework, ie, a Heath–Jarrow–Morton framework, on a time-discrete tenor, such as the London Interbank Offered Rate market model, using a sequence of tenor discretizations, where the tenors are valid…
Gaussian process regression for derivative portfolio modeling and application to credit valuation adjustment computations
The authors present a multi-Gaussian process regression approach, which is well suited for the over-the-counter derivative portfolio valuation involved in credit valuation adjustment (CVA) computation.
In the present paper, a decomposition formula for the call price due to Alòs is transformed into a Taylor-type formula containing an infinite series with stochastic terms. The new decomposition may be considered as an alternative to the decomposition of…
This paper develops two local mesh-free methods for designing stencil weights and spatial discretization, respectively, for parabolic partial differential equations (PDEs) of convection–diffusion–reaction type.
In this work, we present a new Monte Carlo algorithm that is able to calculate the pathwise sensitivities for discontinuous payoff functions.
We present an approach for pricing European call options in the presence of proportional transaction costs, when the stock price follows a general exponential Lévy process.
This paper proposes a new, flexible framework using Monte Carlo methods to price Parisian options not only with constant boundaries but also with general curved boundaries.
This paper considers the problem of efficiently computing the full matrix of second-order sensitivities of a Monte Carlo price when the number of inputs is large.
In this paper, we refer to the axiomatic theory of risk and investigate the problem of formal verification of the expected shortfall (ES) model based on a sample ES. Recognizing the infeasibility of parametric methods, they explore the bootstrap…
In this paper, a novel quasi-multiperiod model for optimal position liquidation in the presence of both temporary and permanent market impact is proposed. Two main features distinguish the proposed approach from its alternatives.
In this paper, the authors investigate a nonlinear generalization of the Black–Scholes equation for pricing American-style call options, where the volatility term may depend on both the underlying asset price and the Gamma of the option.
In this paper, the authors propose a bivariate interpolation of the implied volatility surface based on Chebyshev polynomials. This yields a closed-form approximation of the implied volatility, which is easy to implement and to maintain.
The aim of this paper is to move away from a Gaussian assumption and to provide new algorithms that can be used to implement a Markov-functional model driven by a more general class of one-dimensional diffusion processes.
In this paper, the author uses the mean–variance hedging criterion to value contracts in incomplete markets.
In this paper, the authors analyse the convergence of tree methods for pricing barrier and lookback options.
This paper derives an alternative fast Fourier transform-based computational approach for calculating the target capital of the SST that is more than 600 times faster than a Monte Carlo simulation.
In this paper, the authors introduce a novel, explicit, wide-stencil, two-dimensional (2D) tree–grid method for solving stochastic control problems (SCPs) with two space dimensions and one time dimension, or, equivalently, the corresponding Hamilton…
In this paper, the authors investigate a path-dependent American option problem and provide an efficient and implementable numerical scheme for the solution of its associated path-dependent variational inequality.
In this paper, the authors propose and investigate a new method for the calibration to American option price data.