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Advanced Derivatives and Exotic Options / Jumps and Lévy Processes

Fourier Pricing in Exponential Lévy Models — The Carr-Madan FFT Approach

For any exponential Lévy model St=S0eXtS_t = S_0 e^{X_t}, the characteristic function of the log-return XTX_T is given by the Lévy-Khintchine formula in closed form. The Carr-Madan FFT approach (1999) transforms the call price into Fourier space, evaluates it using the characteristic function, and inverts via the Fast Fourier Transform — obtaining an entire grid of European call prices in O(Nlog⁡N)O(N\log N) time.
This is the workhorse pricing algorithm for all exponential Lévy models: VG, NIG, CGMY, and the Bates SVJ model studied in the next lesson. It generalises the Heston Gil-Pelaez inversion (seen in the Heston Fourier pricing lesson) to arbitrary Lévy characteristic exponents.
Learning objectives. By the end of this note you should be able to:
  • explain why the call price function is not directly Fourier-transformable and how the damping trick fixes this;
  • derive the Carr-Madan formula for the damped call price transform;
  • implement the FFT grid scheme in Python for a VG model;
  • calibrate a Lévy model to a cross-section of implied vols.

In this lesson

  1. 7.1 The damped call transform
  2. 7.2 Inverting via FFT
  3. 7.3 Calibration workflow

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