Presentation 2012-11-08
Predictability of Approximate Accuracy for Asymptotic Expansion
Ryosuke NOMURA, Hideitsu HINO, Noboru MURATA, Nakahiro YOSHIDA,
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Abstract(in English) The pricing of derivatives is an important problem in finance. Compared to Monte Carlo method, which is high accuracy but computationally expensive, asymptotic expansion which approximates probability distributions deterministically is a hopeful method. In this study, it is aimed to evaluate the availability of asymptotic expansion for various stochastic differential equation models. By estimating error rate between asymptotic expansion and Monte Carlo method, it is experimentally showed that it is possible to determine the criterion to use asymptotic expansion for the pricing of derivatives.
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Keyword(in English) asymptotic expansion / random forest
Paper # IBISML2012-79
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Committee IBISML
Conference Date 2012/10/31(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Predictability of Approximate Accuracy for Asymptotic Expansion
Sub Title (in English)
Keyword(1) asymptotic expansion
Keyword(2) random forest
1st Author's Name Ryosuke NOMURA
1st Author's Affiliation Graduate School of Mathematical Sciences, The University of Tokyo()
2nd Author's Name Hideitsu HINO
2nd Author's Affiliation School of Science and Engineering, Waseda University
3rd Author's Name Noboru MURATA
3rd Author's Affiliation School of Science and Engineering, Waseda University
4th Author's Name Nakahiro YOSHIDA
4th Author's Affiliation Graduate School of Mathematical Sciences, The University of Tokyo
Date 2012-11-08
Paper # IBISML2012-79
Volume (vol) vol.112
Number (no) 279
Page pp.pp.-
#Pages 7
Date of Issue