Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/129849


Title: Time Series Simulation with Randomized Quasi-Monte Carlo Methods: An Application to Value at Risk and Expected Shortfall
Authors: 曾毓英
Tzeng, Yu-Ying
Beaumont, Paul M.
Ökten , Giray
Contributors: 風管系
Keywords: Quasi-Monte Carlo;Randomized Quasi-Monte Carlo;Time series simulation;Value-at-risk;Expected shortfall
Date: 2018
Issue Date: 2020-05-26 13:38:00 (UTC+8)
Abstract: 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.
Relation: Computational Economics, Vol.52, pp.55-77
Data Type: article
DOI 連結: https://doi.org/10.1007/s10614-017-9661-0
Appears in Collections:[風險管理與保險學系 ] 期刊論文

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