传统投资组合理论局限下偏度投资组合效用分析

传统投资采用的是假设投资者为风险厌恶的理论,也就是说基于期望收益相当的状况下,投资者选用的投资组合为风险较小的组合。但在现实情况中却并非如此。本文充分考虑传统投资组合理论的局限性,阐述了下偏度重大损失风险控制原理,基于信用风险迁移原理、偏度、峰度在资产组合配置中造成的影响,分析下偏度投资组合优化原理,并探讨了下偏度投资组合的特色效用。     

基于VaR模型的股票组合投资风险管理研究

本文探讨了将GARCH模型与方差-协方差方法相结合的VaR风险计量方法,并用VaR风险替代Markiwitz组合投资模型中的方差风险,通过求解非线性规划问题,得到最小化股票投资组合VaR风险的最优投资策略。     

基于CVaR的投资组合优化模型研究

风险价值(VaR)是近年来国际金融机构所倡导的测度和控制金融风险的国际主流技术,但是它在投资组合损益服从非正态分布的情形时,不满足一致性风险度量,出现尾部损失测量的非充分性。为了使具有一致性的条件风险值度量(CVaR)克服VaR的不足,构建基于CVaR约束的投资组合优化模型,该模型虑及了投资组合资产的交易成本、交易限制、资金约束和投资者的风险承受度,为制定合理的最优投资组合提供了一种新的思路。     

基于非理想状况的投资组合模型的建立

经典的麦克维茨投资组合模型是基于无摩擦的理想状况建立的,与实际情况存在很大差距,本文在考虑市场摩擦前提下将税率与交易费用引入模型以使模型与实际情况相符,从而建立起更加有效和实用模型。     

基于非理想状况的投资组合模型的建立

经典的麦克维茨投资组合模型是基于无摩擦的理想状况建立的,与实际情况存在很大差距,本文在考虑市场摩擦前提下将税率与交易费用引入模型以使模型与实际情况相符,从而建立起更加有效和实用模型。     

基于GARCH-GED模型的证券投资市场风险实证分析

近些年来,市场风险逐渐成为了证券投资基金面临的主要风险,目前对于证券投资市场上的风险预测与评估较为流行的方式就是Va R方法。本文将GARCH模型引入Va R计算,以10支开放式基金为样本进行实证分析,研究结果表明基于GARCH-GED模型的Va R方法与传统方法相比更能够有效地反映证券投资市场的风险。     

网络证券投资模型优化

在证券投资中可以运用证券组合投资通过分散投资达到降低投资风险的目标。采用马科威茨理论中的约定,风险证券的评价采用预期收益率和收益率方差两项指标,从风险控制的角度出发,建立证券投资组合,以确定最优化的投资组合。     

基于带红利的控制公司破产风险因素投资组合模型

本文考虑在现金红利投资的收益情形下,投资者通过对所投资的股票公司的财务数据进行研究,通过评分来预测企业破产的概率,从而建立优质的投资组合模型。文章中应用最非线性规划优化的理论和方法建立投资组合模型,并利用优化算法对模型进行求解。     

基矛CVaR的投资组合优化模型研究

风险价值（VaR）是近年来国际金融机构所倡导的测度和控制金融风险的国际主流技术，但是它在投资组合损益服从非正态分布的情形时，不满足一致性风险度量，出现尾部损失测量的非充分性。为了使具有一致性的条件风险值度量（CVaR）克服VaR的不足，构建基于CVaR约束的投资组合优化模型，该模型虑及了投资组合资产的交易成本、交易限制、资金约束和投资者的风险承受度，为制定合理的最优投资组合提供了一种新的思路。     

证券组合投资模型优化

以Markowitz证券组合投资理论为基础,对于几种不同证券组合投资模型分别考虑了证券组合的收益,风险,交易费等因素条件下对模型进行了优化,并对文中模型做了进一步的扩展。为投资者正确选择证券组合投资的最优策略及应用方面提供参考。     

基于信用风险修正的多阶段银行贷款组合优化决策模型

针对银行的信用风险和贷款的周期性等问题,建立一个基于信用风险修正的多阶段银行贷款组合优化决策模型,该模型在多阶段模型中考虑了信用风险修正问题,根据模型的特点给出了把Monte Carlo模拟的动态算法和差分进化的多阶段算法相结合的求解方法,前者求解银行各类贷款的期望收益率,后者求解每一阶段银行对各类贷款的最优投资比重。数值试验表明所建立的模型是合理的且符合商业银行的实际操作要求,给出的方法是有效的和可行的。     

Portfolio Optimization and Martingale Measures

The paper studies connections between risk aversion and martingale measures in a discrete-time incomplete financial market. An investor is considered whose attitude toward risk is specified in terms of the index b of constant proportional risk aversion. Then dynamic portfolios are admissible if the terminal wealth is positive. It is assumed that the return (risk) processes are bounded. Sufficient (and nearly necessary) conditions are given for the existence of an optimal dynamic portfolio which chooses portfolios from the interior of the set of admissible portfolios. This property leads to an equivalent martingale measure defined through the optimal dynamic portfolio and the index 0 < b ≤ 1. Moreover, the option pricing formula of Davis is given by this martingale measure. In the case of b = 1; that is, in the case of the log-utility, the optimal dynamic portfolio defines the numéraire portfolio.     

SKEWNESS‐AWARE ASSET ALLOCATION: A NEW THEORETICAL FRAMEWORK AND EMPIRICAL EVIDENCE

This paper presents a new measure of skewness, skewness‐aware deviation, that can be linked to prospective satisficing risk measures and tail risk measures such as Value‐at‐Risk. We show that this measure of skewness arises naturally also when one thinks of maximizing the certainty equivalent for an investor with a negative exponential utility function, thus bringing together the mean‐risk, expected utility, and prospective satisficing measures frameworks for an important class of investor preferences. We generalize the idea of variance and covariance in the new skewness‐aware asset pricing and allocation framework. We show via computational experiments that the proposed approach results in improved and intuitively appealing asset allocation when returns follow real‐world or simulated skewed distributions. We also suggest a skewness‐aware equivalent of the classical Capital Asset Pricing Model beta, and study its consistency with the observed behavior of the stocks traded at the NYSE between 1963 and 2006.     

OPTIMAL LOT SOLUTION TO CARDINALITY CONSTRAINED MEAN–VARIANCE FORMULATION FOR PORTFOLIO SELECTION

The pioneering work of the mean–variance formulation proposed by Markowitz in the 1950s has provided a scientific foundation for modern portfolio selection. Although the trade practice often confines portfolio selection with certain discrete features, the existing theory and solution methodologies of portfolio selection have been primarily developed for the continuous solution of the portfolio policy that could be far away from the real integer optimum. We consider in this paper an exact solution algorithm in obtaining an optimal lot solution to cardinality constrained mean–variance formulation for portfolio selection under concave transaction costs. Specifically, a convergent Lagrangian and contour-domain cut method is proposed for solving this class of discrete-feature constrained portfolio selection problems by exploiting some prominent features of the mean–variance formulation and the portfolio model under consideration. Computational results are reported using data from the Hong Kong stock market.     

Optimal Portfolios with Bounded Capital at Risk

We consider some continuous-time Markowitz type portfolio problems that consist of maximizing expected terminal wealth under the constraint of an upper bound for the capital at risk. In a Black–Scholes setting we obtain closed-form explicit solutions and compare their form and implications to those of the classical continuous-time mean-variance problem. We also consider more general price processes that allow for larger fluctuations in the returns.