Time Series Simulation with Randomized Quasi-Monte Carlo Methods: An Application to Value at Risk and Expected Shortfall

• Published in: Computational economics Vol. 52; no. 1; pp. 55 - 77
• Main Authors: Tzeng, Yu-Ying, Beaumont, Paul M., Ökten, Giray
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2018; Springer Nature B.V
• Subjects: Behavioral/Experimental Economics; Computer Appl. in Social and Behavioral Sciences; Computer simulation; Confidence intervals; Convergence; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Markov chains; Math Applications in Computer Science; Monte Carlo simulation; Operations Research/Decision Theory; Production methods; Randomization; Risk levels; Simulation; Time series; Volatility; Randomized Quasi-Monte Carlo; Time series simulation; Quasi-Monte Carlo; Expected shortfall; Value-at-risk

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 randomi...