B-Spline Techniques for Volatility Modeling
This paper is devoted to the application of B-splines to volatility modeling, specifically the calibration of the leverage function in stochastic local volatility (SLV) models and the parameterization of an arbitrage-free implied volatility surface calibrated to sparse option data. We use an extension of classical B-splines obtained by including basis functions with infinite support. We first discuss the application of shape-constrained B-splines to the estimation of conditional expectations, not merely from a scatter plot but also from the given marginal distributions. One application is the Monte Carlo calibration of SLV models by Markov projection. We then present a new technique for the calibration of an implied volatility surface to sparse option data. We use a B-spline parameterization of the Radon–Nikodym derivative of the underlying’s risk-neutral probability density with respect to a roughly calibrated base model. We show that this method provides smooth arbitrage-free implied volatility surfaces. Finally, we sketch a Galerkin method with B-spline finite elements to the solution of the partial differential equation satisfied by the Radon–Nikodym derivative.