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題名 Time Series Simulation with Randomized Quasi-Monte Carlo Methods: An Application to Value at Risk and Expected Shortfall
作者 曾毓英
Tzeng, Yu-Ying
Beaumont, Paul M.
Ökten , Giray
貢獻者 風管系
關鍵詞 Quasi-Monte Carlo ; Randomized Quasi-Monte Carlo ; Time series simulation ; Value-at-risk ; Expected shortfall
日期 2018
上傳時間 26-May-2020 13:38:00 (UTC+8)
摘要 Quasi-Monte Carlo methods are designed to produce efficient estimates of simulated values but the error statistics of these estimates are difficult to compute. Randomized quasi-Monte Carlo methods have been developed to address this shortcoming. In this paper we compare quasi-Monte Carlo and randomized quasi-Monte Carlo techniques for simulating time series. We use randomized quasi-Monte Carlo to compute value-at-risk and expected shortfall measures for a stock portfolio whose returns follow a highly nonlinear Markov switching stochastic volatility model which does not admit analytical solutions for the returns distribution. Quasi-Monte Carlo methods are more accurate but do not allow the computation of reliable confidence intervals about risk measures. We find that randomized quasi-Monte Carlo methods maintain many of the advantages of quasi-Monte Carlo while also providing the ability to produce reliable confidence intervals of the simulated risk measures. However, the advantages in speed of convergence of randomized quasi-Monte Carlo diminish as the forecast horizon increases.
關聯 Computational Economics, Vol.52, pp.55-77
資料類型 article
DOI https://doi.org/10.1007/s10614-017-9661-0
dc.contributor 風管系
dc.creator (作者) 曾毓英
dc.creator (作者) Tzeng, Yu-Ying
dc.creator (作者) Beaumont, Paul M.
dc.creator (作者) Ökten , Giray
dc.date (日期) 2018
dc.date.accessioned 26-May-2020 13:38:00 (UTC+8)-
dc.date.available 26-May-2020 13:38:00 (UTC+8)-
dc.date.issued (上傳時間) 26-May-2020 13:38:00 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/129849-
dc.description.abstract (摘要) Quasi-Monte Carlo methods are designed to produce efficient estimates of simulated values but the error statistics of these estimates are difficult to compute. Randomized quasi-Monte Carlo methods have been developed to address this shortcoming. In this paper we compare quasi-Monte Carlo and randomized quasi-Monte Carlo techniques for simulating time series. We use randomized quasi-Monte Carlo to compute value-at-risk and expected shortfall measures for a stock portfolio whose returns follow a highly nonlinear Markov switching stochastic volatility model which does not admit analytical solutions for the returns distribution. Quasi-Monte Carlo methods are more accurate but do not allow the computation of reliable confidence intervals about risk measures. We find that randomized quasi-Monte Carlo methods maintain many of the advantages of quasi-Monte Carlo while also providing the ability to produce reliable confidence intervals of the simulated risk measures. However, the advantages in speed of convergence of randomized quasi-Monte Carlo diminish as the forecast horizon increases.
dc.format.extent 1590589 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Computational Economics, Vol.52, pp.55-77
dc.subject (關鍵詞) Quasi-Monte Carlo ; Randomized Quasi-Monte Carlo ; Time series simulation ; Value-at-risk ; Expected shortfall
dc.title (題名) Time Series Simulation with Randomized Quasi-Monte Carlo Methods: An Application to Value at Risk and Expected Shortfall
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1007/s10614-017-9661-0
dc.doi.uri (DOI) https://doi.org/10.1007/s10614-017-9661-0