共查询到20条相似文献,搜索用时 31 毫秒
1.
CVaR风险度量模型在投资组合中的运用 总被引:9,自引:1,他引:8
风险价值(VaR)是近年来金融机构广泛运用的风险度量指标,条件风险价值(CVaR)是VaR的修正模型,也称为平均超额损失或尾部VaR,它比VaR具有更好的性质。在本中,我们将运用风险度量指标VaR和CVaR,提出一个新的最优投资组合模型。介绍了模型的算法,而且利用我国的股票市场进行了实证分析,验证了新模型的有效性,为制定合理的投资组合提供了一种新思路。 相似文献
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We study the effect of capacity uncertainty on the inventory decisions of a risk-averse newsvendor. We consider two well-known risk criteria, namely Value-at-Risk (VaR) included as a constraint and Conditional Value-at-Risk (CVaR). For the risk-neutral newsvendor, we find that the optimal order quantity is not affected by the capacity uncertainty. However, this result does not hold for the risk-averse newsvendor problem. Specifically, we find that capacity uncertainty decreases the order quantity under the CVaR criterion. Under the VaR constraint, capacity uncertainty leads to an order decrease for low confidence levels, but to an order increase for high confidence levels. This implies that the risk criterion should be carefully selected as it has an important effect on inventory decisions. This is shown for the newsvendor problem, but is also likely to hold for other inventory control problems that future research can address. 相似文献
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Credit risk optimization with Conditional Value-at-Risk criterion 总被引:27,自引:0,他引:27
Fredrik Andersson Helmut Mausser Dan Rosen Stanislav Uryasev 《Mathematical Programming》2001,89(2):273-291
This paper examines a new approach for credit risk optimization. The model is based on the Conditional Value-at-Risk (CVaR)
risk measure, the expected loss exceeding Value-at-Risk. CVaR is also known as Mean Excess, Mean Shortfall, or Tail VaR. This
model can simultaneously adjust all positions in a portfolio of financial instruments in order to minimize CVaR subject to
trading and return constraints. The credit risk distribution is generated by Monte Carlo simulations and the optimization
problem is solved effectively by linear programming. The algorithm is very efficient; it can handle hundreds of instruments
and thousands of scenarios in reasonable computer time. The approach is demonstrated with a portfolio of emerging market bonds.
Received: November 1, 1999 / Accepted: October 1, 2000?Published online December 15, 2000 相似文献
4.
This paper deals with the optimal reinsurance strategy from an insurer’s point of view. Our objective is to find the optimal policy that maximises the insurer’s survival probability. To meet the requirement of regulators and provide a tool to risk management, we introduce the dynamic version of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR) and worst-case CVaR (wcCVaR) constraints in diffusion model and the risk measure limit is proportional to company’s surplus in hand. In the dynamic setting, a CVaR/wcCVaR constraint is equivalent to a VaR constraint under a higher confidence level. Applying dynamic programming technique, we obtain closed form expressions of the optimal reinsurance strategies and corresponding survival probabilities under both proportional and excess-of-loss reinsurance. Several numerical examples are provided to illustrate the impact caused by dynamic VaR/CVaR/wcCVaR limit in both types of reinsurance policy. 相似文献
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We develop tractable semidefinite programming based approximations for distributionally robust individual and joint chance constraints, assuming that only the first- and second-order moments as well as the support of the uncertain parameters are given. It is known that robust chance constraints can be conservatively approximated by Worst-Case Conditional Value-at-Risk (CVaR) constraints. We first prove that this approximation is exact for robust individual chance constraints with concave or (not necessarily concave) quadratic constraint functions, and we demonstrate that the Worst-Case CVaR can be computed efficiently for these classes of constraint functions. Next, we study the Worst-Case CVaR approximation for joint chance constraints. This approximation affords intuitive dual interpretations and is provably tighter than two popular benchmark approximations. The tightness depends on a set of scaling parameters, which can be tuned via a sequential convex optimization algorithm. We show that the approximation becomes essentially exact when the scaling parameters are chosen optimally and that the Worst-Case CVaR can be evaluated efficiently if the scaling parameters are kept constant. We evaluate our joint chance constraint approximation in the context of a dynamic water reservoir control problem and numerically demonstrate its superiority over the two benchmark approximations. 相似文献
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We consider the problem of optimal portfolio choice using the Conditional Value-at-Risk (CVaR) and Value-at-Risk (VaR) measures for a market consisting of n risky assets and a riskless asset and where short positions are allowed. When the distribution of returns of risky assets is unknown but the mean return vector and variance/covariance matrix of the risky assets are fixed, we derive the distributionally robust portfolio rules. Then, we address uncertainty (ambiguity) in the mean return vector in addition to distribution ambiguity, and derive the optimal portfolio rules when the uncertainty in the return vector is modeled via an ellipsoidal uncertainty set. In the presence of a riskless asset, the robust CVaR and VaR measures, coupled with a minimum mean return constraint, yield simple, mean-variance efficient optimal portfolio rules. In a market without the riskless asset, we obtain a closed-form portfolio rule that generalizes earlier results, without a minimum mean return restriction. 相似文献
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This paper is concerned with distributionally robust chance constrained problem under interval distribution information. Using worst-case CVaR approximation, we present a tractable convex programming approximation for distributionally robust individual chance constrained problem under interval sets of mean and covariance information. We prove the worst-case CVaR approximation problem is an exact form of the distributionally robust individual chance constrained problem. Then, our result is applied to worst-case Value-at-Risk optimization problem. Moreover, we discuss the problem under several ambiguous distribution information and investigate tractable approximations for distributionally robust joint chance constrained problem. Finally, we provide an illustrative example to show our results. 相似文献
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范臻 《应用数学与计算数学学报》2006,20(1):56-62
本文对于信用资产组合的优化问题给出了一个稳健的模型,所建模型涉及了条件在险值(CVaR)风险度量以及具有补偿限制的随机线性规划框架,其思想是在CVaR与信用资产组合的重构费用之间进行权衡,并降低解对于随机参数的实现的敏感性.为求解相应的非线性规划,本文将基本模型转化为一系列的线性规划的求解问题. 相似文献
10.
基于条件风险值准则的供应链回购契约协调策略 总被引:1,自引:0,他引:1
研究了由具有风险偏好的零售商和风险中性的供应商组成的两级供应链回购契约协调问题.针对具有风险偏好的零售商,考虑了风险中性、风险厌恶和风险喜好三种态度,建立了由风险厌恶程度和悲观系数两个参数描述的基于条件风险值(CVaR)的集成目标决策函数.推导了不同风险偏好态度下的零售商最优订货决策,分析了不同风险偏好参数下的零售商订货决策变化情况.给出了能够完全协调风险偏好零售商和风险中性供应商的供应链回购契约协调机制.最后,进行了数值计算,验证了设计的供应链回购契约协调策略的有效性.结果表明,在给出的回购契约协调机制下,考虑风险偏好情况下的零售商最优订货决策能够保证整个供应链系统实现最优绩效,而供应链成员期望利润却随不同的风险偏好参数而不同. 相似文献
11.
Estimating the probabilities by which different events might occur is usually a delicate task, subject to many sources of inaccuracies. Moreover, these probabilities can change over time, leading to a very difficult evaluation of the risk induced by any particular decision. Given a set of probability measures and a set of nominal risk measures, we define in this paper the concept of robust risk measure as the worst possible of our risks when each of our probability measures is likely to occur. We study how some properties of this new object can be related with those of our nominal risk measures, such as convexity or coherence. We introduce a robust version of the Conditional Value-at-Risk (CVaR) and of entropy-based risk measures. We show how to compute and optimize the Robust CVaR using convex duality methods and illustrate its behavior using data from the New York Stock Exchange and from the NASDAQ between 2005 and 2010. 相似文献
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Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization 总被引:1,自引:0,他引:1
Churlzu Lim Hanif D. Sherali Stan Uryasev 《Computational Optimization and Applications》2010,46(3):391-415
Conditional Value-at-Risk (CVaR) is a portfolio evaluation function having appealing features such as sub-additivity and convexity.
Although the CVaR function is nondifferentiable, scenario-based CVaR minimization problems can be reformulated as linear programs
(LPs) that afford solutions via widely-used commercial softwares. However, finding solutions through LP formulations for problems
having many financial instruments and a large number of price scenarios can be time-consuming as the dimension of the problem
greatly increases. In this paper, we propose a two-phase approach that is suitable for solving CVaR minimization problems
having a large number of price scenarios. In the first phase, conventional differentiable optimization techniques are used
while circumventing nondifferentiable points, and in the second phase, we employ a theoretically convergent, variable target
value nondifferentiable optimization technique. The resultant two-phase procedure guarantees infinite convergence to optimality.
As an optional third phase, we additionally perform a switchover to a simplex solver starting with a crash basis obtained
from the second phase when finite convergence to an exact optimum is desired. This three phase procedure substantially reduces
the effort required in comparison with the direct use of a commercial stand-alone simplex solver (CPLEX 9.0). Moreover, the
two-phase method provides highly-accurate near-optimal solutions with a significantly improved performance over the interior
point barrier implementation of CPLEX 9.0 as well, especially when the number of scenarios is large. We also provide some
benchmarking results on using an alternative popular proximal bundle nondifferentiable optimization technique. 相似文献
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In this paper, the Conditional Value-at-Risk (CVaR) is adopted to measure the total loss of multiple lines of insurance business and two nonparametric estimation methods are introduced to explore the optimal multivariate quota-share reinsurance under a mean-CVaR framework. While almost all the existing literature on optimal reinsurance are based on a probabilistic derivation, the present paper relies on a statistical analysis. The proposed optimal reinsurance models are directly formulated on empirical data and no explicit distributional assumption on the underlying risk vector is required. The resulting nonparametric reinsurance models are convex and computationally amenable, circumventing the difficulty of computing CVaR of the sum of a generally dependent random vector. Statistical consistency of the resulting estimators for the best CVaR is established for both nonparametric models, allowing empirical data to be generated from any stationary process satisfying strong mixing conditions. Finally, numerical experiments are presented to show that a routine bootstrap procedure can capture the distributions of the resulting risk measures well for independent data. 相似文献
18.
Optimal Security Liquidation Algorithms 总被引:1,自引:0,他引:1
Sergiy Butenko Alexander Golodnikov Stanislav Uryasev 《Computational Optimization and Applications》2005,32(1-2):9-27
This paper develops trading strategies for liquidation of a financial security, which maximize the expected return. The problem is formulated as a stochastic programming problem that utilizes the scenario representation of possible returns. Two cases are considered, a case with no constraint on risk and a case when the risk of losses associated with trading strategy is constrained by Conditional Value-at-Risk (CVaR) measure. In the first case, two algorithms are proposed; one is based on linear programming techniques, and the other uses dynamic programming to solve the formulated stochastic program. The third proposed algorithm is obtained by adding the risk constraints to the linear program. The algorithms provide path-dependent strategies, i.e., the fraction of security sold depends upon price sample-path of the security up to the current moment. The performance of the considered approaches is tested using a set of historical sample-paths of prices. 相似文献
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We consider a problem of optimal reinsurance and investment with multiple risky assets for an insurance company whose surplus is governed by a linear diffusion. The insurance company’s risk can be reduced through reinsurance, while in addition the company invests its surplus in a financial market with one risk-free asset and n risky assets. In this paper, we consider the transaction costs when investing in the risky assets. Also, we use Conditional Value-at-Risk (CVaR) to control the whole risk. We consider the optimization problem of maximizing the expected exponential utility of terminal wealth and solve it by using the corresponding Hamilton-Jacobi-Bellman (HJB) equation. Explicit expression for the optimal value function and the corresponding optimal strategies are obtained. 相似文献
20.
We study asymptotic behavior of the empirical conditional value-at-risk (CVaR). In particular, the Berry–Essen bound, the law of iterated logarithm, the moderate deviation principle and the large deviation principle for the empirical CVaR are obtained. We also give some numerical examples. 相似文献