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題名 Risk Management for Linear and Non-Linear Assets: A Bootstrap Method with Importance Resampling to Evaluate Value-at-Risk
作者 Lin, Shih-Kuei;Wang, R. H.;Fuh, C. D.
林士貴
貢獻者 金融系
關鍵詞 Bootstrap ; Heavy-tailed ; Importance resampling ; Monte Carlo simulation ; Multivariate normal distribution ; Multivariate t distribution ; Quadratic approximation ; Value-at-Risk ; Variance reduction
日期 2006.09
上傳時間 27-Mar-2014 10:01:08 (UTC+8)
摘要 Many empirical studies suggest that the distribution of risk factors has heavy tails. One always assumes that the underlying risk factors follow a multivariate normal distribution that is a assumption in conflict with empirical evidence. We consider a multivariate t distribution for capturing the heavy tails and a quadratic function of the changes is generally used in the risk factor for a non-linear asset. Although Monte Carlo analysis is by far the most powerful method to evaluate a portfolio Value-at-Risk (VaR), a major drawback of this method is that it is computationally demanding. In this paper, we first transform the assets into the risk on the returns by using a quadratic approximation for the portfolio. Second, we model the return’s risk factors by using a multivariate normal as well as a multivariate t distribution. Then we provide a bootstrap algorithm with importance resampling and develop the Laplace method to improve the efficiency of simulation, to estimate the portfolio loss probability and evaluate the portfolio VaR. It is a very powerful tool that propose importance sampling to reduce the number of random number generators in the bootstrap setting. In the simulation study and sensitivity analysis of the bootstrap method, we observe that the estimate for the quantile and tail probability with importance resampling is more efficient than the naive Monte Carlo method. We also note that the estimates of the quantile and the tail probability are not sensitive to the estimated parameters for the multivariate normal and the multivariate t distribution.
關聯 Asia-Pacific Financial Markets,13(3), 261-295
資料類型 article
dc.contributor 金融系en_US
dc.creator (作者) Lin, Shih-Kuei;Wang, R. H.;Fuh, C. D.en_US
dc.creator (作者) 林士貴-
dc.date (日期) 2006.09en_US
dc.date.accessioned 27-Mar-2014 10:01:08 (UTC+8)-
dc.date.available 27-Mar-2014 10:01:08 (UTC+8)-
dc.date.issued (上傳時間) 27-Mar-2014 10:01:08 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/64939-
dc.description.abstract (摘要) Many empirical studies suggest that the distribution of risk factors has heavy tails. One always assumes that the underlying risk factors follow a multivariate normal distribution that is a assumption in conflict with empirical evidence. We consider a multivariate t distribution for capturing the heavy tails and a quadratic function of the changes is generally used in the risk factor for a non-linear asset. Although Monte Carlo analysis is by far the most powerful method to evaluate a portfolio Value-at-Risk (VaR), a major drawback of this method is that it is computationally demanding. In this paper, we first transform the assets into the risk on the returns by using a quadratic approximation for the portfolio. Second, we model the return’s risk factors by using a multivariate normal as well as a multivariate t distribution. Then we provide a bootstrap algorithm with importance resampling and develop the Laplace method to improve the efficiency of simulation, to estimate the portfolio loss probability and evaluate the portfolio VaR. It is a very powerful tool that propose importance sampling to reduce the number of random number generators in the bootstrap setting. In the simulation study and sensitivity analysis of the bootstrap method, we observe that the estimate for the quantile and tail probability with importance resampling is more efficient than the naive Monte Carlo method. We also note that the estimates of the quantile and the tail probability are not sensitive to the estimated parameters for the multivariate normal and the multivariate t distribution.en_US
dc.format.extent 404916 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Asia-Pacific Financial Markets,13(3), 261-295en_US
dc.subject (關鍵詞) Bootstrap ; Heavy-tailed ; Importance resampling ; Monte Carlo simulation ; Multivariate normal distribution ; Multivariate t distribution ; Quadratic approximation ; Value-at-Risk ; Variance reductionen_US
dc.title (題名) Risk Management for Linear and Non-Linear Assets: A Bootstrap Method with Importance Resampling to Evaluate Value-at-Risken_US
dc.type (資料類型) articleen