投资组合模型的动态规划解法

文章针对投资组合问题,建立了总风险控制下的以最终的总收益最大化为决策目标的资产投资组合模型,并利用动态规划方法对模型进行了求解,可以求得模型的整体最优解。     

论商业银行资产组合的风险分散

随着我国银行商业化的深化 ,商业银行的风险也在不断增加 ,而且目前尽管我国银行业的资产种类尚比较单一 ,但商业银行投资领域的拓宽 ,资产种类的多样化已是一种必然的趋势。文章运用 Markowitz模型 ,建立了关于资产组合的最优解的数学模型。通过对该模型的求解 ,解决了在存在多种资产可供选择的情况下 ,如何进行最优资产组合 ,以分散风险 ,达到在保证一定的收益的情况下 ,使风险最小的目的 ,并运用实证分析的方法 ,证实了这一结论     

基于VaR约束的带交易费用的商业银行资产负债组合优化模型

在考虑交易费用的条件下,以商业银行各项资产和负债的组合净收益最大化为目标函数,以贷款组合的VaR约束及法律法规和经营管理约束为条件,建立商业银行资产负债组合优化模型,为银行的资产负债管理提供决策方法,并给出具体计算实例,得到最优资产和负债的比例及最大净收益。该模型的特点：一是考虑交易费用,以及贷款利率依赖企业的信用等级,更切合实际;二是同时兼顾资产和负债两个方面的业务和管理。     

基于不确定性均值-方差模型的稳健静态资产组合选择

文章借助相对熵测度风险资产收益的一阶矩和前两阶矩的不确定性对资产组合终期财富期望效用的影响,基于极大极小化理论建立了模型参数不确定下的稳健静态资产组合模型,运用稳健控制方法获得了模型的最优解;根据最优解,以上证综指1997年1月至2009年6月的月收益数据构建了两个不同区间段的样本做实证研究。结果表明,参数不确定性导致资产组合中风险资产的比例降低,并随着投资者不确定性偏好程度增加降低得越多;历史数据或信息越少,参数不确定性影响越强;均值不确定性的影响强于方差不确定性的影响;即使投资者完全不相信方差的预测功能,但仍在一定程度上相信均值的预测功能。     

商业银行信贷资产组合优化研究

本文试图根据我国商业银行的特点,设定目标函数为组合总的意外损失最小,并基于智能化的免疫算法对优化模型进行改进,提出了基于分组匹配的亲和力计算方法,建立了基于免疫算法的资产组合优化模型.同时将之运用于某国有商业银行的背景,结果表明免疫算法能有效地运用于改善信贷资产组合结构.     

基于风险价值约束的投资组合选择模型

把VaR风险度量和最优投资组合选择问题相结合,建立并求解了最优均值-VaR投资组合选择模型,实证检验所得到的结果中可以看出,以均值-VaR的模式来对资产进行优化配置具有较高效率,其效率超过以Markowitz均值进行的配置,通过-VaR的模式进行的配置,如果是在确定置信度的前提下,可以使个别偏好、共同偏好合为一体.在VaR的约束下M-V模型的特征,对于Markowitz均值方差的投资组合模型来说,是一种理论上得到的发展.     

基于Factor-GARCH模型的动态VaR与CVaR度量及实证研究

动态CVaR作为投资组合的风险度量工具较动态VaR有很多优点,但同动态VaR的计算一样,随着投资组合资产数量的增加,计算难度迅速增大。通过改良Factor-GARCH模型,将其用于多种资产时变方差-协方差矩阵的估计,实现了降维的目的,克服了计算多元动态CVaR时的困难。实证研究表明,与动态VaR相比,动态CVaR作为一种更保守的风险度量工具,能准确反映投资组合的可能损失。     

投资组合风险管理中VaR模型的缺陷以及CVaR模型研究

本文对VaR和CVaR这两个风险度量进行了较充分的比较分析,参照了我国证券市场的实际情况,并考虑了交易成本,实际收益率的计算以及最小交易单位等因素,建立了CVaR资产组合优化模型,为如何制定合理的资产组合投资方案提出了新的思路.     

消费习惯、递归效用函数与股权溢价

文章构造了基于习惯形成—递归效用函数的消费—资产组合投资模型,所提出的模型是对Merton(1973)、Bakshi和Chen(1996a)、Anne Epaulard和Aude Pommeret(2003)研究的推广。通过对模型求最优解,并利用投资者的行为参数与消费习惯参数进行模拟计算,发现在不同的参数数值下,所得出的投资者相对风险规避系数是合理的。因此,论文提出的消费投资组合模型在一定程度上解释了股票溢价之谜。     

基于Markov转换模型的前沿资产组合分析

文章将Markov转换模型与资产前沿组合理论有效地结合在一起,在应用Markov转换模型预测出股票下一时期收益率及其方差的基础上,根据马可维茨的投资组合理论得出股票市场的前沿资产组合,并以中国证券市场中的几支样本股票为例进行了实证分析.     

How risky is the optimal portfolio which maximizes the Sharpe ratio?

In this paper, we investigate the properties of the optimal portfolio in the sense of maximizing the Sharpe ratio (SR) and develop a procedure for the calculation of the risk of this portfolio. This is achieved by constructing an optimal portfolio which minimizes the Value-at-Risk (VaR) and at the same time coincides with the tangent (market) portfolio on the efficient frontier which is related to the SR portfolio. The resulting significance level of the minimum VaR portfolio is then used to determine the risk of both the market portfolio and the corresponding SR portfolio. However, the expression of this significance level depends on the unknown parameters which have to be estimated in practice. It leads to an estimator of the significance level whose distributional properties are investigated in detail. Based on these results, a confidence interval for the suggested risk measure of the SR portfolio is constructed and applied to real data. Both theoretical and empirical findings document that the SR portfolio is very risky since the corresponding significance level is smaller than 90 % in most of the considered cases.     

Multiple Regression Model Averaging and the Focused Information Criterion With an Application to Portfolio Choice

ABSTRACT

We consider multiple regression (MR) model averaging using the focused information criterion (FIC). Our approach is motivated by the problem of implementing a mean-variance portfolio choice rule. The usual approach is to estimate parameters ignoring the intention to use them in portfolio choice. We develop an estimation method that focuses on the trading rule of interest. Asymptotic distributions of submodel estimators in the MR case are derived using a localization framework. The localization is of both regression coefficients and error covariances. Distributions of submodel estimators are used for model selection with the FIC. This allows comparison of submodels using the risk of portfolio rule estimators. FIC model averaging estimators are then characterized. This extension further improves risk properties. We show in simulations that applying these methods in the portfolio choice case results in improved estimates compared with several competitors. An application to futures data shows superior performance as well.     

A generalized pivotal quantity approach to portfolio selection

The major problem of mean–variance portfolio optimization is parameter uncertainty. Many methods have been proposed to tackle this problem, including shrinkage methods, resampling techniques, and imposing constraints on the portfolio weights, etc. This paper suggests a new estimation method for mean–variance portfolio weights based on the concept of generalized pivotal quantity (GPQ) in the case when asset returns are multivariate normally distributed and serially independent. Both point and interval estimations of the portfolio weights are considered. Comparing with Markowitz's mean–variance model, resampling and shrinkage methods, we find that the proposed GPQ method typically yields the smallest mean-squared error for the point estimate of the portfolio weights and obtains a satisfactory coverage rate for their simultaneous confidence intervals. Finally, we apply the proposed methodology to address a portfolio rebalancing problem.     

基于模糊概率的Markowitz投资组合分散风险的理论及实证研究

在分析Markowitz模型不足的基础上，提出了一个修正模型。该模型采用模糊概率的方法对投资组合里各资产的权重进行合理调整，更准确地显现投资组合分散风险的效果，并利用实际数据对该模型进行了实证研究，验证了资产数量与组合风险之间关系的理论学说，表明在近年来的上海股票市场中适宜的投资规模不超过20种。     

投资组合动态VaR风险度量

投资组合的VaR风险度量依赖于投资组合中金融资产间联合分布函数的确定，随着投资组合规模的扩大，其VaR的计算难度也不断加大。利用ICA可以将多元联合概率分布函数转化为一元概率分布函数乘积实现简化计算的特点，基于ICA的投资组合动态VaR风险度量方法和计算步骤，克服了多元非正态条件下VaR测算上的困难。实证研究表明，与EWMA模型法、MGARCH模型法相比，ICA法能够准确地度量投资组合动态VaR。     

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Large pharmaceutical companies maintain a portfolio of assets, some of which are projects under development while others are on the market and generating revenue. The budget allocated to R&D may not always be sufficient to fund all the available projects for development. Much attention has been paid to the selection of optimal subsets of available projects to fit within the available budget. In this paper, we argue the need for a forward-looking approach to portfolio decision-making. We develop a quantitative model that allows the portfolio management to evaluate the need for future inflow of new projects to achieve revenue at desired levels, often aspiring to a certain annual revenue growth. Optimisation methods are developed for the presented model, allowing an optimal choice of number, timing and type of projects to be added to the portfolio. The proposed methodology allows for a proactive approach to portfolio management, prioritisation, and optimisation. It provides a quantitatively based support for strategic decisions regarding the efforts needed to secure the future development pipeline and revenue stream of the company.     

基于可信性理论的Mean-CVaR投资组合优化

选取CVaR作为风险度量指标,在可信性理论的基础上构建Mean-CVaR投资组合模型,采用Markov过程预测作为模糊变量的预期投资收益率,并设计基于模糊模拟和遗传算法的混合智能算法以求解;选取上证50成份股2013—2014年的日度历史交易数据,将该模型应用到中国证券市场,结果发现该投资组合模型与中国证券市场的环境具有一定的适应性,能够为投资者的投资决策提供依据。     

遗传算法在投资组合模型中的应用

均值-VaR模型是比较复杂的非线性规划问题，传统的算法不能保证得到全局最优值。鉴于此，引入遗传算法求解资产配置比例。对基于均值-VaR的单目标优化问题，设计了限定搜索空间和惩罚函数的遗传算法；而对多目标优化问题，应用并行选择遗传算法，并以沪深300行业分类指数构建投资组合，分析了行业资产配置的投资组合问题。结果表明，算法取得了良好的效果，解的结果既满足了投资的目标和约束条件，又反映了投资者之间不同的收益风险需求，且具有较好的实践性。     

Optimal Strategies for a Long-Term Static Investor

The optimal strategies for a long-term static investor are studied. Given a portfolio of a stock and a bond, we derive the optimal allocation of the capitals to maximize the expected long-term growth rate of a utility function of the wealth. When the bond has a constant interest rate, three models for the underlying stock price processes are studied: Heston model, 3/2 model, and jump diffusion model. We also study the optimal strategies for a portfolio in which the stock price process follows a Black-Scholes model and the bond process has a Vasicek interest rate that is correlated to the stock price.