首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献,搜索用时 93 毫秒
1.
研究了Duarte提出的投资组合优化统一模型及条件风险价值(CVaR),分析了以CVaR为风险度量的投资组合优化模型的具体形式,建立了统一七种模型的投资组合优化统一模型,并发现统一模型是一个凸二次规划问题.  相似文献   

2.
基于多目标CVaR模型的证券组合投资的风险度量和策略   总被引:1,自引:0,他引:1  
本文首先定义了多损失函数下的-αVaR,-αCVaR损失值以及-αCVaR损失值的等价函数,给出了多目标CVaR模型.然后,基于多目标CVaR模型,建立了一个多目标证券组合投资优化模型,得出在多置信水平下的证券组合投资比例和CVaR值,据此建立一种证券组合投资的降低风险优化模型.其降低风险策略是在收益率不变的情形下降低风险和总投资比例.数值实验表明,这种策略是可以通过明显地减少总投资比例来达到降低风险的目的.  相似文献   

3.
安佰玲  张杰 《大学数学》2013,29(2):43-49
通过引入光滑因子,改进了基于条件风险值(CVaR)的最优投资组合线性模型,并详细介绍了以VaR最小为目标函数的最优投资组合模型的算法设计思想与过程.  相似文献   

4.
VaR和CVaR是目前两种主流风险度量工具。条件VaR和条件CVaR是基于市场风险因子在已知条件(或信息)下的分布来计量和测算VaR和CVaR,能够及时地根据变化的条件来重新估计风险进而进行有效的风险管理,是对传统的基于边际分布的VaR和CVaR指标的有益补充。另外一方面,近年来非参数核估计方法因模型设定灵活、方便处理变量相依结构等优点备受关注。在本文,我们用条件VaR和条件CVaR的非参数核估计法,对我国A股市场的风险进行测算。结果得出:条件VaR和条件CVaR能揭示出深证成指和上证综指之间的不同风险特征;条件VaR和条件CVaR的测算结果并非总是一致;系统风险估计值对已知条件的敏感性高于深发展A和万科A两只股票的个股风险。以上风险特征在边际VaR和边际CVaR下无法得到。  相似文献   

5.
对选定的风险资产进行组合投资,以条件风险价值(CVaR)作为度量风险的工具,建立单期投资组合优化问题的CVaR模型。目标函数中含有多重积分与plus函数,产生情景矩阵将多重积分计算转化成求和运算,提出plus函数的一个新的一致光滑逼近函数并给出求解CVaR模型的光滑化方法,最后的实证研究表明了本文算法的优越性。  相似文献   

6.
鉴于条件风险价值CVaR具有风险度量的合理性以及两基金分离定理对证券投资的重要意义,以CVaR作为风险度量研究两基金分离定理.在组合收益率服从正态分布的假设下,分别就投资组合含有或没有无风险资产的情形提出并证明了两基金分离定理;放开方差-协方差矩阵为非奇异这一通常假设,证明了CVaR风险度量下的两基金分离定理依然成立.  相似文献   

7.
针对债券投资组合中的风险度量难题,用CVaR作为风险度量方法,构建了基于CVaR的债券投资组合优化模型.采用历史模拟算法处理模型中的随机收益率向量,将随机优化模型转化为确定性优化模型,并且证明了算法的收敛性.通过线性化技术处理CVaR中的非光滑函数,将该模型转化为一般的线性规划模型.结合10只债券的组合投资实例,验证了模型与算法的有效性.  相似文献   

8.
VaR约束下均值-方差模型在基金资产配置的应用   总被引:1,自引:0,他引:1  
随着我国开放式基金的迅猛发展以及证券市场的波动,如何识别和控制基金风险这一问题越显重要。VaR模型是一种有效的风险计量和管理工具,本文刻划VaR约束下均值-方差模型及其优化模型,并运用基于VaR约束下的均值——方差模型,定量地分析投资基金的投资组合收益和风险,提出开放式基金最优资产配置,使投资组合收益最大。  相似文献   

9.
传统的均值-风险(包括方差、VaR、CVaR等)组合选择模型在计算最优投资组合时,常假定均值是已知的常值,但在实际资产配置中,收益的均值估计会有偏差,即存在着估计风险.在利用CVaR测度估计风险的基础上,研究了CVaR鲁棒均值-CVaR投资组合选择模型,给出了另外两种不同的求解方法,即对偶法和光滑优化方法,并探讨了它们的相关性质及特征,数值实验表明在求解大样本或者大规模投资组合选择问题上,对偶法和光滑优化方法在计算上是可行且有效的.  相似文献   

10.
VaR是目前国际上应用最广泛的度量金融风险的指标之一,其核心在于波动率,也就是方差的参数估计.采用EWMA模型估计方差,并且结合风险溢价特征的GARCH(1,1)-M模型计算出沪深300股指及其期货的最优衰减因子为0.933 25,摒弃了以往采用0.940 0作为衰减因子的一贯做法,并且运用Cornish-Fisher方程对正态分布的分位数进行了修正,得到修正后的套期保值比率以及资产组合的VaR,与传统的套期保值模型相比,该模型的风险价值VaR降低的程度明显,并且对投资组合未来的VaR具有很好的预测效果,表明EWMA-GARCH(1,1)-M模型对沪深300股指期货的套期保值效果较好.  相似文献   

11.
Credit risk optimization with Conditional Value-at-Risk criterion   总被引:27,自引:0,他引:27  
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   

12.
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   

13.
本文研究了具有强健性的证券投资组合优化问题.模型以最差条件在值风险为风险度量方法,并且考虑了交易费用对收益的影响.当投资组合的收益率概率分布不能准确确定但是在有界的区间内,尤其是在箱型区间结构和椭球区域结构内时,我们可以把具有强健性的证券投资组合优化问题的模型分别转化成线性规划和二阶锥规划形式.最后,我们用一个真实市场数据的算例来验证此方法.  相似文献   

14.
Conditional Value-at-Risk (CVaR) is a popular risk measure for modelling losses in the case of a rare but extreme event. We consider the problem of estimating CVaR from i.i.d. samples of an unbounded random variable, which is either sub-Gaussian or sub-exponential. We derive a novel one-sided concentration bound for a natural sample-based CVaR estimator in this setting. Our bound relies on a concentration result for a quantile-based estimator for Value-at-Risk (VaR), which may be of independent interest.  相似文献   

15.
本文研究了具有强健性的证券投资组合优化问题.模型以最差条件在值风险为风险度量方法,并且考虑了交易费用对收益的影响.当投资组合的收益率概率分布不能准确确定但是在有界的区间内,尤其是在箱型区间结构和椭球区域结构内时,我们可以把具有强健性的证券投资组合优化问题的模型分别转化成线性规划和二阶锥规划形式.最后,我们用一个真实市场数据的算例来验证此方法.  相似文献   

16.
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   

17.
本文假设投资者是风险厌恶型,用CVaR作为测量投资组合风险的方法.在预算约束的条件下,以最小化CVaR为目标函数,建立了带有交易费用的投资组合模型.将模型转化为两阶段补偿随机优化模型,构造了求解模型的随机L-S算法.为了验证算法的有效性,用中国证券市场中的股票进行数值试验,得到了最优投资组合、VaR和CVaR的值.而且对比分析了有交易费和没有交易费的最优投资组合的不同,给出了相应的有效前沿.  相似文献   

18.
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   

19.
This paper proposes a unified framework to solve distributionally robust mean-risk optimization problem that simultaneously uses variance, value-at-risk (VaR) and conditional value-at-risk (CVaR) as a triple-risk measure. It provides investors with more flexibility to find portfolios in the sense that it allows investors to optimize a return-risk profile in the presence of estimation error. We derive a closed-form expression for the optimal portfolio strategy to the robust mean-multiple risk portfolio selection model under distribution and mean return ambiguity (RMP). Specially, the robust mean-variance, robust maximum return, robust minimum VaR and robust minimum CVaR efficient portfolios are all special instances of RMP portfolios. We analytically and numerically show that the resulting portfolio weight converges to the minimum variance portfolio when the level of ambiguity aversion is in a high value. Using numerical experiment with simulated data, we demonstrate that our robust portfolios under ambiguity are more stable over time than the non-robust portfolios.  相似文献   

20.
The well‐known Markowitz approach to portfolio allocation, based on expected returns and their covariance, seems to provide questionable results in financial management. One motivation for the pitfall is that financial returns have heavier than Gaussian tails, so the covariance of returns, used in the Markowitz model as a measure of portfolio risk, is likely to provide a loose quantification of the effective risk. Additionally, the Markowitz approach is very sensitive to small changes in either the expected returns or their correlation, often leading to irrelevant portfolio allocations. More recent allocation techniques are based on alternative risk measures, such as value at risk (VaR) and conditional VaR (CVaR), which are believed to be more accurate measures of risk for fat‐tailed distributions. Nevertheless, both VaR and CVaR estimates can be influenced by the presence of extreme returns. In this paper, we discuss sensitivity to the presence of extreme returns and outliers when optimizing the allocation, under the constraint of keeping CVaR to a minimum. A robust and efficient approach, based on the forward search, is suggested. A Monte Carlo simulation study shows the advantages of the proposed approach, which outperforms both robust and nonrobust alternatives under a variety of specifications. The performance of the method is also thoroughly evaluated with an application to a set of US stocks.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号