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Accelerating the calibration of stochastic volatility models

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  • Kilin, Fiodar

Abstract

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.

Suggested Citation

  • Kilin, Fiodar, 2007. "Accelerating the calibration of stochastic volatility models," CPQF Working Paper Series 6, Frankfurt School of Finance and Management, Centre for Practical Quantitative Finance (CPQF).
  • Handle: RePEc:zbw:cpqfwp:6
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    Cited by:

    1. Susanne Griebsch & Uwe Wystup, 2011. "On the valuation of fader and discrete barrier options in Heston's stochastic volatility model," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 693-709.
    2. F. Gerlich & A. Giese & J. Maruhn & E. Sachs, 2012. "Parameter identification in financial market models with a feasible point SQP algorithm," Computational Optimization and Applications, Springer, vol. 51(3), pages 1137-1161, April.
    3. Manfred Gilli & Enrico Schumann, 2010. "Calibrating Option Pricing Models with Heuristics," Working Papers 030, COMISEF.

    More about this item

    Keywords

    Stochastic Volatility Models; Calibration; Numerical Integration; Fast Fourier Transform;

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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