The Experts below are selected from a list of 287952 Experts worldwide ranked by ideXlab platform
Dan A. Ralescu - One of the best experts on this subject based on the ideXlab platform.
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Value-at-Risk in uncertain random Risk analysis
Information Sciences, 2017Co-Authors: Yuhan Liu, Dan A. RalescuAbstract:Uncertain random variables provide a tool to deal with phenomena in which uncertainty and randomness simultaneously exist. This paper proposes a concept of Value-at-Risk to quantify the Risk of an uncertain random system. In addition, a Value-at-Risk theorem is proved in order to calculate the Value-at-Risk, and is applied to series systems, parallel system, k-out-of-n system, standby system, and structural system.
L. Jeff Hong - One of the best experts on this subject based on the ideXlab platform.
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Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review
ACM Transactions on Modeling and Computer Simulation, 2014Co-Authors: L. Jeff Hong, Guangwu LiuAbstract: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.
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Winter Simulation Conference - Monte Carlo estimation of Value-at-Risk, conditional Value-at-Risk and their sensitivities
Proceedings of the 2011 Winter Simulation Conference (WSC), 2011Co-Authors: L. Jeff Hong, Guangwu LiuAbstract:Value-at-Risk and conditional Value at Risk are two widely used Risk measures, employed in the financial industry for Risk management purposes. This tutorial discusses Monte Carlo methods for estimating Value-at-Risk, conditional Value-at-Risk and their sensitivities. By relating the mathematical representation of Value-at-Risk to that of conditional Value-at-Risk, it provides a unified view of simulation methodologies for both Risk measures and their sensitivities.
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Asymptotic representations for importance-sampling estimators of Value-at-Risk and conditional Value-at-Risk
Operations Research Letters, 2010Co-Authors: Lihua Sun, L. Jeff HongAbstract:Value-at-Risk (VaR) and conditional Value-at-Risk (CVaR) are important Risk measures. They are often estimated by using importance-sampling (IS) techniques. In this paper, we derive the asymptotic representations for IS estimators of VaR and CVaR. Based on these representations, we are able to prove the consistency and asymptotic normality of the estimators and to provide simple conditions under which the IS estimators have smaller asymptotic variances than the ordinary Monte Carlo estimators.
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Winter Simulation Conference - A general framework of importance sampling for Value-at-Risk and conditional Value-at-Risk
Proceedings of the 2009 Winter Simulation Conference (WSC), 2009Co-Authors: Lihua Sun, L. Jeff HongAbstract:Value-at-Risk (VaR) and conditional Value-at-Risk (CVaR) are important Risk measures. Importance sampling (IS) is often used to estimate them. We derive the asymptotic representations for IS estimators of VaR and CVaR. Based on these representations, we are able to give simple conditions under which the IS estimators have smaller asymptotic variances than the ordinal estimators. We show that the exponential twisting can yield an IS distribution that satisfies the conditions for both the IS estimators of VaR and CVaR. Therefore, we may be able to estimate VaR and CVaR accurately at the same time.
Jin Peng - One of the best experts on this subject based on the ideXlab platform.
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Value at Risk and Tail Value at Risk in Uncertain Environment
2009Co-Authors: Jin PengAbstract:Real-life decisions are usually made in the state of uncertainty or Risk. In this article we present the Risk measuring techniques Value at Risk (VaR) and tail Value at Risk (TVaR) under uncertainty. Firstly, we introduce the VaR concept of uncertain variable based on uncertainty theory and examine its fundamental properties. Then, the TVaR concept is evolved and some fundamental properties of the proposed TVaR are investigated. Finally, uncertain simulation algo- rithms are designed to calculate the VaR and TVaR. The suggested VaR or TVaR methodology can be widely used as tools of Risk analysis in an uncertain environment.
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ICFIE - Average Value at Risk in Fuzzy Risk Analysis
Advances in Soft Computing, 2009Co-Authors: Jin PengAbstract:The average Value at Risk (AVaR) is a Risk measure which is a superior alternative to Value at Risk (VaR). In this paper, we present the average Value at Risk method for fuzzy Risk analysis. Firstly, we put forward the new concept of the average Value at Risk based on credibility theory. Next, we examine some properties of the proposed average Value at Risk. Then, a kind of fuzzy simulation algorithm is given to calculate the average Value at Risk. Finally, numerical example is provided. The proposed average Value at Risk can be applied in many real problems of fuzzy Risk analysis.
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Measuring Fuzzy Risk by Credibilistic Value at Risk
2008 3rd International Conference on Innovative Computing Information and Control, 2008Co-Authors: Jin PengAbstract:The Value at Risk (VaR) methodology is a widely used tool in financial market Risk management. In this paper, we present a new method for fuzzy Risk analysis. First, we present the new concept of the credibilistic Value at Risk based on credibility theory. Then, we examine some properties of the proposed credibilistic Value at Risk. Finally, a kind of fuzzy simulation algorithm is given to show how to calculate the credibilistic Value at Risk. The proposed credibilistic VaR is suitable for use in many real problems of fuzzy Risk analysis.
Guangwu Liu - One of the best experts on this subject based on the ideXlab platform.
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Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review
ACM Transactions on Modeling and Computer Simulation, 2014Co-Authors: L. Jeff Hong, Guangwu LiuAbstract: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.
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Winter Simulation Conference - Monte Carlo estimation of Value-at-Risk, conditional Value-at-Risk and their sensitivities
Proceedings of the 2011 Winter Simulation Conference (WSC), 2011Co-Authors: L. Jeff Hong, Guangwu LiuAbstract:Value-at-Risk and conditional Value at Risk are two widely used Risk measures, employed in the financial industry for Risk management purposes. This tutorial discusses Monte Carlo methods for estimating Value-at-Risk, conditional Value-at-Risk and their sensitivities. By relating the mathematical representation of Value-at-Risk to that of conditional Value-at-Risk, it provides a unified view of simulation methodologies for both Risk measures and their sensitivities.
Yuhan Liu - One of the best experts on this subject based on the ideXlab platform.
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Value-at-Risk in uncertain random Risk analysis
Information Sciences, 2017Co-Authors: Yuhan Liu, Dan A. RalescuAbstract:Uncertain random variables provide a tool to deal with phenomena in which uncertainty and randomness simultaneously exist. This paper proposes a concept of Value-at-Risk to quantify the Risk of an uncertain random system. In addition, a Value-at-Risk theorem is proved in order to calculate the Value-at-Risk, and is applied to series systems, parallel system, k-out-of-n system, standby system, and structural system.
