SINH-ACCELERATION: EFFICIENT EVALUATION OF PROBABILITY DISTRIBUTIONS, OPTION PRICING, AND MONTE CARLO SIMULATIONS

Svetlana Boyarchenko and Sergei Levendorskiĭ ()
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Sergei Levendorskiĭ: Calico Science Consulting, 2708 Bolton Street, Austin, TX 78748, USA

International Journal of Theoretical and Applied Finance (IJTAF), 2019, vol. 22, issue 03, 1-49

Abstract: Characteristic functions of several popular classes of distributions and processes admit analytic continuation into unions of strips and open coni around ℝ ⊂ ℂ. The Fourier transform techniques reduce calculation of probability distributions and option prices in the evaluation of integrals whose integrands are analytic in domains enjoying these properties. In the paper, we suggest to use changes of variables of the form ξ = −1ω1 + bsinh(−1ω + y) and the simplified trapezoid rule to evaluate the integrals accurately and fast. We formulate the general scheme, and apply the scheme for calculation probability distributions and pricing European options in Lévy models, the Heston model, the CIR model, and a Lévy model with the CIR-subordinator. We outline applications to fast and accurate calibration procedures and Monte Carlo simulations in Lévy models, regime switching Lévy models that can account for stochastic drift, volatility and skewness, the Heston model, other affine models and quadratic term structure models. For calculation of quantiles in the tails using the Newton or bisection method, it suffices to precalculate several hundreds of values of the characteristic exponent at points on an appropriate grid (conformal principal components) and use these values in formulas for cpdf and pdf.

Keywords: Sinh-regular Lévy processes; sinh-regular distributions; sinh-acceleration; conformal principal components; Heston model; KoBoL; CGMY; CIR; CIR subordinator; Monte Carlo simulations (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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Working Paper: SINH-acceleration: efficient evaluation of probability distributions, option pricing, and Monte-Carlo simulations (2018)
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DOI: 10.1142/S0219024919500110

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