AI & ML
A B-Spline Galerkin Method for Option Pricing under the CGMY Tempered Stable Process
arXiv cs.NA0 views
arXiv:2610.07554v1 Announce Type: new Abstract: We develop and analyze a B-spline scaling-function Galerkin method for the tempered fractional partial differential equation governing European and American option prices under the CGMY process. The spatial generator couples exponentially tempered left- and right-sided Riemann--Liouville derivatives of order $Y\in(0,2)$, whose nonlocality and asymmetry present discretization challenges. We derive a corrected Fourier-symbol construction of the tempered connection-coefficient matrices whose interior blocks are Toeplitz, maintain exact fractional scaling, and enable $\mathcal{O}(N\log N)$ matrix--vector products via the fast Fourier transform. In the regime $Y\in(1,2)$, we prove full-line and finite-domain G\aa rding inequalities, coercive whenever the risk-free rate is positive, via zero-extension to the full-line symbol. On this foundation, we establish elliptic-projection, semidiscrete, and fully discrete error estimates with spatial rate $\mathcal{O}(h^{p-Y/2})$, alongside an unconditional shifted-energy stability estimate for the forced Crank--Nicolson scheme with mesh-independent constants. For positive rates the admissible shift vanishes, governing the plain scheme exactly as implemented. A single backward solve prices an entire strike strip at fixed maturity with smooth, analytically differentiated Greeks. The same discretization prices American puts through a projected linear complementarity solve, with early-exercise premiums and exercise boundaries validated against a resolved deterministic reference and a Longstaff--Schwartz Monte Carlo benchmark. Calibration to S\&P~500 option surfaces across low-volatility, crisis, and recovery regimes yields implied-volatility errors near two vol-points on well-identified surfaces, while an accuracy-matched comparison with a stabilized Gr\"unwald--Letnikov baseline delineates precisely when the high-order variational structure pays off.
Source:https://arxiv.org/abs/2610.07554PreviousEfficient Approximation of Structured Linear Operators using Randomized One-Sided QueriesNextA Neural JKO Scheme for Hellinger-Kantorovich Gradient Flows via Monge-Growth Pairs
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