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Sensitivity Estimation Using Likelihood Ratio Method with Fixed-Sample-Path Principle

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Book cover Simulation and Modeling Methodologies, Technologies and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 402))

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Abstract

The likelihood ratio method (LRM) is an efficient indirect method for estimating the sensitivity of given expectations with respect to parameters by Monte Carlo simulation. The restriction on application of LRM to real-world problems is that it requires explicit knowledge of the probability density function (pdf) to calculate the score function. In this study, a fixed-sample-path method is proposed, which derives the score function required for LRM not via the pdf but directly from a constructive algorithm that computes the sample path from parameters and random numbers. The boundary residual, which represents the correction associated with the change of the distribution range of the random variables in LRM, is also derived. Some examples including the estimation of risk measures (Greeks) of option and financial flow of funds networks showed the effectiveness of the fixed-sample-path method.

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Authors and Affiliations

  1. Central Research Laboratory, Hitachi, Ltd, Kokubunji-shi, Tokyo, Japan

    Koji Fukuda & Yasuyuki Kudo

Authors
  1. Koji Fukuda
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  2. Yasuyuki Kudo
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Corresponding author

Correspondence to Koji Fukuda .

Editor information

Editors and Affiliations

  1. Dept. of Computer & Info Science, Fordham University, Bronx, New York, USA

    Mohammad S. Obaidat

  2. Faculty of Engg school of ECE, University of Ottawa, Ottawa, Ontario, Canada

    Tuncer Ören

  3. Intelligent Systems Laboratory, Sys Rese Insti of Polish Academy of Sci, Warsaw, Poland

    Janusz Kacprzyk

  4. INSTICC, Setubal, Portugal

    Joaquim Filipe

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Fukuda, K., Kudo, Y. (2015). Sensitivity Estimation Using Likelihood Ratio Method with Fixed-Sample-Path Principle. In: Obaidat, M., Ören, T., Kacprzyk, J., Filipe, J. (eds) Simulation and Modeling Methodologies, Technologies and Applications . Advances in Intelligent Systems and Computing, vol 402. Springer, Cham. https://doi.org/10.1007/978-3-319-26470-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-26470-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26469-1

  • Online ISBN: 978-3-319-26470-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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