Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk

Li, Hong; Hu, Zhaolin; Liu, Guangwu

doi:10.1145/2661631

Cited by 84 publications

(46 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, we study estimation, optimization, and sensitivity analysis of SR. This is in line with the content of Hong, Hu, and Liu (), who reviewed Monte Carlo methods for the VaR and conditional value‐at‐risk (CVaR) measures.…”

Section: Introductionsupporting

confidence: 78%

Utility‐based shortfall risk: Efficient computations via Monte Carlo

Zhang

2018

Naval Research Logistics

Self Cite

View full text Add to dashboard Cite

With the development of financial risk management, the notion of convex risk measures has been proposed and has gained increasing attentions. Utility‐based shortfall risk (SR), as a specific and important class of convex risk measures, has become popular in recent years. In this paper we focus on the computational aspects of SR, which are significantly understudied but fundamental for risk assessment and management. We discuss efficient estimation, optimization, and sensitivity analysis of SR, based on Monte Carlo techniques and stochastic optimization methods. We also conduct extensive numerical studies on the proposed approaches. The numerical results further demonstrate the effectiveness of these approaches.

show abstract

Section: Introductionsupporting

confidence: 78%

Utility‐based shortfall risk: Efficient computations via Monte Carlo

Zhang

2018

Naval Research Logistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…They have been extensively used in financial industry, especially after the financial crisis in 2008. An abundant literature has dedicated to studying the estimation and optimization of risk measures under various settings; in particular, [Hong et al 2014] provides a comprehensive review of Monte Carlo methods for VaR and CVaR.…”

Section: Introductionmentioning

confidence: 99%

Risk Quantification in Stochastic Simulation under Input Uncertainty

Zhu

Liu

Zhou

2020

ACM Trans. Model. Comput. Simul.

View full text Add to dashboard Cite

When simulating a complex stochastic system, the behavior of output response depends on input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty into the system. The quantification of the impact of input uncertainty on output response has been extensively studied. Most of the existing literature focuses on providing inferences on the mean response at the true but unknown input parameter, including point estimation and confidence interval construction. Risk quantification of mean response under input uncertainty often plays an important role in system evaluation and control, because it provides inferences on extreme scenarios of mean response in all possible input models. To the best of our knowledge, it has rarely been systematically studied in the literature. In this paper, first we introduce risk measures of mean response under input uncertainty, and propose a nested Monte Carlo simulation approach to estimate them. Then we develop asymptotical properties such as consistency and asymptotic normality for the proposed nested risk estimators. We further study the associated budget allocation problem for efficient nested risk simulation, and finally use a sharing economy example to illustrate the importance of accessing and controlling risk due to input uncertainty.

show abstract

“…To reduce the large amount of simulation effort needed in the inner level, Broadie et al (2015) proposed a regression method and Hong et al (2017) proposed a kernel method to avoid nested simulations. Interested readers may see Hong et al (2014) for a recent comprehensive review on simulation methods in estimating risk measures.…”

Section: Literature Reviewmentioning

confidence: 99%

Online Risk Monitoring Using Offline Simulation

Jiang

Nelson

2019

INFORMS Journal on Computing

Self Cite

View full text Add to dashboard Cite

Authors are encouraged to submit new papers to INFORMS journals by means of a style file template, which includes the journal title. However, use of a template does not certify that the paper has been accepted for publication in the named journal. INFORMS journal templates are for the exclusive purpose of submitting to an INFORMS journal and should not be used to distribute the papers in print or online or to submit the papers to another publication.

show abstract

Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk

Cited by 84 publications

References 86 publications

Utility‐based shortfall risk: Efficient computations via Monte Carlo

Utility‐based shortfall risk: Efficient computations via Monte Carlo

Risk Quantification in Stochastic Simulation under Input Uncertainty

Online Risk Monitoring Using Offline Simulation

Contact Info

Product

Resources

About