I am delighted to introduce the April 2019 issue of The Journal of Computational Finance.
As ever, the papers included in this issue provide a broad mix of topics, from derivative pricing – including a new parameterization of volatility surfaces and some novel finite-difference techniques for pricing equations – via risk management through the efficient computation of quantiles, to the use of artificial intelligence for yield curve modeling.
In our first paper, “Yield curve fitting with artificial intelligence: a comparison of standard fitting methods with artificial intelligence algorithms”, Achim Posthaus tests the applicability of various data science methodologies – in particular, neural networks and support vector machines – to yield curve fitting, as an alternative to more traditionally used approaches such as parametric curves and interpolation. The author offers a critical assessment of their performance and parsimony.
In “The extended SSVI volatility surface”, the issue’s second paper, Sebas Hendriks and Claude Martini provide us with an extension to surface stochastic volatility-inspired (SSVI) parameterization by allowing a term structure of the correlation parameter. The authors also derive conditions for the absence of calendar arbitrage.
Azamat Abdymomunov, Filippo Curti and Hayden Kane conduct an empirical comparison of the accuracy and efficiency of different methods in approximating tail quantiles of compound distributions in “Calculate tail quantiles of compound distributions”, this issue’s third paper. By doing so, they provide the reader with useful practical guidance on which of the methods to use in different applications.
In our last paper, “Efficient conservative second-order central-upwind schemes for option-pricing problems”, Omishwary Bhatoo, Arshad Ahmud Iqbal Peer, Eitan Tadmor, De´sire´ Yannick Tangman and Aslam Aly El Faidal Saib introduce a second- order flux limiter scheme combined with explicit second-order Runge–Kutta time stepping. The authors use a conservative form of the Black–Scholes partial differential equation as a model problem to assess the performance of the new scheme compared with other published methods for several different option types.
I hope you will find this issue interesting and useful.
University of Oxford
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.
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.
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.