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Multiplicative noise, fast convolution, and pricing

Listed author(s):
  • Giacomo Bormetti
  • Sofia Cazzaniga
Registered author(s):

    In this work we detail the application of a fast convolution algorithm computing high dimensional integrals to the context of multiplicative noise stochastic processes. The algorithm provides a numerical solution to the problem of characterizing conditional probability density functions at arbitrary time, and we applied it successfully to quadratic and piecewise linear diffusion processes. The ability in reproducing statistical features of financial return time series, such as thickness of the tails and scaling properties, makes this processes appealing for option pricing. Since exact analytical results are missing, we exploit the fast convolution as a numerical method alternative to the Monte Carlo simulation both in objective and risk neutral settings. In numerical sections we document how fast convolution outperforms Monte Carlo both in velocity and efficiency terms.

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    Paper provided by in its series Papers with number 1107.1451.

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    Date of creation: Jul 2011
    Publication status: Published in Quantitative Finance, 2012, 1-14 iFirst
    Handle: RePEc:arx:papers:1107.1451
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