Affiliation:
1. City University of Hong Kong, Hong Kong, China
2. Tongji University, Yangpu District, Shanghai, China
Abstract
Value-at-risk (VaR) and conditional value-at-risk (CVaR) are two widely used risk measures of large losses and are employed in the financial industry for risk management purposes. In practice, loss distributions typically do not have closed-form expressions, but they can often be simulated (i.e., random observations of the loss distribution may be obtained by running a computer program). Therefore, Monte Carlo methods that design simulation experiments and utilize simulated observations are often employed in estimation, sensitivity analysis, and optimization of VaRs and CVaRs. In this article, we review some of the recent developments in these methods, provide a unified framework to understand them, and discuss their applications in financial risk management.
Funder
Fundamental Research Funds for the Central Universities
City University of Hong Kong
Research Grants Council, University Grants Committee, Hong Kong
Shanghai Pujiang Program
National Natural Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,Modelling and Simulation
Cited by
68 articles.
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