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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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.
Skewed target range strategy for multiperiod portfolio optimization using a two-stage least squares Monte Carlo method
In this paper, the authors propose a novel investment strategy for portfolio optimization problems.
In this paper, the authors construct a Heath-Platen-type Monte Carlo estimator that performs extraordinarily well compared with the crude Monte Carlo estimation.
This paper's aim is twofold: to introduce a mathematical framework that is sufficiently general and sound to cover the main areas of model risk, and to illustrate how a practitioner can identify the relevant abstract concepts and put them to work.
In this paper the authors evaluate the performance of different approaches for estimating quantiles of compound distributions, which are widely used for risk quantification in the banking and insurance industries.
In this paper, the authors propose improvements to the approach of Ramírez-Espinoza and Ehrhardt (2013) for option-pricing PDEs formulated in the conservative form.
This paper extends Gatheral and Jacquier’s surface stochastic volatility-inspired (SSVI) parameterization by making the correlation maturity dependent and obtaining the necessary and sufficient conditions for no calendar-spread arbitrage.
Yield curve fitting with artificial intelligence: a comparison of standard fitting methods with artificial intelligence algorithms
In this paper, the author expands standard yield curve fitting techniques to artificial intelligence methods.
In this paper, the authors study an evolutionary framework for the optimization of various types of neural network structures and parameters.
Fast stochastic forward sensitivities in Monte Carlo simulations using stochastic automatic differentiation (with applications to initial margin valuation adjustments)
In this paper, the author applies stochastic (backward) automatic differentiation to calculate stochastic forward sensitivities.
In this paper, the authors give a preprocessing step for Fourier methods that involves projecting the Green’s function onto the set of linear basis functions.
In this paper, the authors develop a new local correlation model that uses a generic function 'g' to describe the correlation between all asset–asset pairs for a basket of underlyings.
In this paper, the author describes a simple adaptive Filon method that performs better and more accurately than various popular alternatives for pricing options under the Heston model.
This paper develops a general methodology for pricing early exercise and exotic financial options by extending the recently developed PROJ method.
In this paper, the authors construct strategies for an American option portfolio by exercising options at optimal timings with optimal weights determined concurrently.
This paper analyzes the efficiency of hedging strategies for stock options in the presence of jump clustering.
In this paper, the authors present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture.
Vibrato and automatic differentiation for high-order derivatives and sensitivities of financial options
This paper deals with the computation of second-order or higher Greeks of financial securities. It combines two methods, vibrato and automatic differentiation (AD), and compares these with other methods.
In this paper, the authors present a new approach to bounding financial derivative prices in regime-switching market models from both above and below.