Accelerating the calibration of stochastic volatility models

This paper compares the performance of three methods for pricing vanilla options in models with known characteristic function: (1) Direct integration, (2) Fast Fourier Transform (FFT), (3) Fractional FFT. The most important application of this comparison is the choice of the fastest method for the calibration of stochastic volatility models, e.g. Heston, Bates, Barndorff-Nielsen-Shephard models or Levy models with stochastic time. We show that using additional cache technique makes the calibration with the direct integration method at least seven times faster than the calibration with the fractional FFT method. --Stochastic Volatility Models,Calibration,Numerical Integration,Fast Fourier Transform

Accelerating the calibration of stochastic volatility models

This paper compares the performance of three methods for pricing vanilla options in models with known characteristic function: (1) Direct integration, (2) Fast Fourier Transform (FFT), (3) Fractional FFT. The most important application of this comparison is the choice of the fastest method for the calibration of stochastic volatility models, e.g. Heston, Bates, Barndor®-Nielsen-Shephard models or Levy models with stochastic time. We show that using additional cache technique makes the calibration with the direct integration method at least seven times faster than the calibration with the fractional FFT method.Stochastic Volatility Models; Calibration; Numerical Integration; Fast Fourier Transform

Forward-start options in the Barndorff-Nielsen-Shephard Model

We derive a semi-analytical formula for pricing forward-start options in the Barndorff-Nielsen- Shephard model. In terms of computational time, this formula is equivalent to one-dimensional integration. --Affine Models,Barndorff-Nielsen-Shephard Model,Forward-Start Options

Accelerating the calibration of stochastic volatility models

This paper compares the performance of three methods for pricing vanilla options in models with known characteristic function: (1) Direct integration, (2) Fast Fourier Transform (FFT), (3) Fractional FFT. The most important application of this comparison is the choice of the fastest method for the calibration of stochastic volatility models, e.g. Heston, Bates, Barndor®-Nielsen-Shephard models or Levy models with stochastic time. We show that using additional cache technique makes the calibration with the direct integration method at least seven times faster than the calibration with the fractional FFT method