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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/88839

    Title: Efficient Importance Sampling for Rare Event Simulation with Applications
    Authors: Fuh, Cheng-der;Teng, Huei-Wen;Wang, Ren-Her
    Contributors: 淡江大學財務金融學系
    Keywords: Bootstrap;Confidence region;Exponential tilting;Local asymptotic normal;Moderate deviation;Value at Risk
    Date: 2011-12
    Issue Date: 2013-04-29 20:23:51 (UTC+8)
    Abstract: Importance sampling has been known as a powerful tool to reduce the variance of Monte Carlo estimator for rare event simulation. Based on the criterion of minimizing the variance of Monte Carlo estimator, we propose a simple general account for finding the optimal tilting measure. To this end, we first obtain an explicit expression of the optimal alternative distribution, and then propose a recursive approximation algorithm for the tilting measure. The proposed algorithm is quite general to cover many interesting examples and can also be applied to a locally asymptotically normal (LAN) family around the original distribution. To illustrate the broad applicability of our method, we study value-at-risk (VaR) computation in financial risk management, and bootstrap confidence regions in statistical inferences.
    Relation: International Workshop on Statistical Computing in Quantitative Finance and Biostatistics: A Satellite Meeting for the 7th IASC-ARS Conference
    Appears in Collections:[財務金融學系暨研究所] 會議論文

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