Sparse wavelet methods for option pricing under stochastic volatility

Norbert Hilber, Ana-Maria Matache and Christoph Schwab

Journal of Computational Finance

Abstract: ABSTRACT Prices of European plain vanilla as well as barrier and compound options on one risky asset in a Black-Scholes market with stochastic volatility are expressed as solutions of degenerate parabolic partial differential equations in two spatial variables: the spot price S and the volatility process variable y. We present and analyze a pricing algorithm based on sparse wavelet space discretizations of order p ¡Ý 1 in (S, y) and on hp-discontinuous Galerkin time-stepping with geometric step size reduction towards maturity T . Wavelet preconditioners adapted to the volatility models for a Generalized Minimum Residual method (GMRES) solver allow us to price contracts at all maturities 0 < t ¡Ü T and all spot prices for a given strike K in essentially O(N) memory and work with accuracy of essentially O(N−p), a performance comparable to that of the best Fast Fourier Transform (FFT)-based pricing methods for constant volatility models (where "essentially" means up to powers of log N and |log h|, respectively).

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