不用克希霍克—拉夫假设的弹性圆板理论再探

本文在不用克希霍夫－拉夫假设的弹性板一般理论的基础上，建立了不用克希霍夫－拉夫假设的弹性圆板的一级近似理论，对圆板的四周固定和均布载荷的条件下，得到了具体的轴对称分析解，并和红典的圆薄板解进行了比较，证明本文新解更加接近实验结果，本文也具体地讨论了理论结果中厚度增大时的影响。     

扭转映射的Aubry－Mather理论及其应用

本文介绍Ａｕｂｒｙ－Ｍａｔｈｅｒ理论近年来的发展情况，文章讨论了该理论与ＫＡＭ理论、保面积映射不动点理论的联系，并简单地介绍了它的一些推广。作为该理论的应用，对平面Ｈａｍｉｌ－ｔｏｎ系统Ｍａｔｈｅｒ集的存在性给出了一些结果．     

一类时滞Logistic方程周期正解的存在性

我们利用重合度理论，讨论了一类时滞Logistic方程周期正解的存在 性，获得了一个新结果，改进了某些相关的结果。     

关于新型Hilbert不等式的积分形式

本文利用分析的方法及不等式理论，建立了两个新型Hilbert不等式的积分形式，获得了一些新结果，推广了某些相关的结果．     

关于两个新Hilbert积分不等式

利用分析的方法及不等式理论，推广了Pachpatte最新给出的两个新型Hilbert积分不等式，获得了几个新结果，改进了某些相关的结果。     

扭转映射的Aubry—Mather理论及其应用

本文介绍Ａｕｂｒｙ－Ｍａｔｈｅｒ理论近年来的发展情况。文章讨论了该理论与ＫＡＭ理论、保面积映射不动点理论的联系，简单地介绍了它的一些推广。作为该理论的应用，对平面Ｈａｍｉｌ－ｔｏｎ系统Ｍａｔｈｅｒ集的存在性给出了一些结果。     

弹性圆板在一侧受均载而四周固定的条件下不用克希霍夫—拉夫…

本文在前文「１」，「２」所得的微分方程和有关这界条件的基础上，采用一种新的整体插地，求得了弹性圆板在一侧受均载而四周固定的条件下弯曲问题的不用克希霍夫－拉夫假设的一级近似理论的数值结果，并与经典的克希霍夫－拉夫理论和Ｒｅｉｓｓｎｅｒ修正理论的结果进行了比较。     

层合板一个新的高队理论解析解

本以复合材料的Ｒｅｄｄｙ高阶理论为基础，引进一个位移函数Φ，将原来求解的微分方程组转化为一个高阶微分方程，得到了四边简支情况下的Ｎａｖｉｅｒ型解，和一对边简支另一对边任意情况下的Ｌｅｖｙ型解，中列举了算例进行比较，其数值结果和献上已有结果相吻合，表明本采用的解法是可靠的，Ｒｅｄｄｙ高阶理论未知数不多，但精度比一阶剪切变形理论要好，计算时地需剪切修正系数，计算较为简单。     

A2B分子反映系统经典轨迹计算的有限元方法

本文利用常微分方程的连续有限元法计算了A2B模型的分子的经典轨迹和能量误差，将计算延长到10^-sS，并与辛算法进行了比较．结果表明，在微观反映动力学研究所考虑的时间范围内，有限元法的结果与理论分析一致，能较长时间保持能量守恒和系统整体结构，并在数值计算上探讨了守恒性和近似程度，结果与理论相吻合。     

Banach空间常微分方程理论的若干问题

本文综述了Ｂａｎａｃｈ空间常微分方程理论近期的发展，主要为紧型条件，耗散型条件，非线性半群、上下解方法，边值问题、Ｂａｎａｃｈ空间中的积－微分方程和脉冲方程，以及对于不动点理论和临界理论的应用，并且给出了作者们在这一领域中的一些新结果。     

Robust portfolio optimization with derivative insurance guarantees

Robust portfolio optimization aims to maximize the worst-case portfolio return given that the asset returns are allowed to vary within a prescribed uncertainty set. If the uncertainty set is not too large, the resulting portfolio performs well under normal market conditions. However, its performance may substantially degrade in the presence of market crashes, that is, if the asset returns materialize far outside of the uncertainty set. We propose a novel robust optimization model for designing portfolios that include European-style options. This model trades off weak and strong guarantees on the worst-case portfolio return. The weak guarantee applies as long as the asset returns are realized within the prescribed uncertainty set, while the strong guarantee applies for all possible asset returns. The resulting model constitutes a convex second-order cone program, which is amenable to efficient numerical solution procedures. We evaluate the model using simulated and empirical backtests and analyze the impact of the insurance guarantees on the portfolio performance.     

收益率为模糊数的投资组合问题的讨论

从模糊性的角度考虑选择风险资产投资组合问题 ,对于收益率为模糊数的情形 ,在每一置信水平上 ,以偏离中心值的程度作为风险的度量 ,当预期收益率给定时 ,证明最小风险选择组合的存在性并得到其最优解 ,还给出全局最小风险组合存在的条件及其对应的全局最小风险的算式。     

Risk-adjusted probability measures in portfolio optimization with coherent measures of risk

We consider the problem of optimizing a portfolio of n assets, whose returns are described by a joint discrete distribution. We formulate the mean–risk model, using as risk functionals the semideviation, deviation from quantile, and spectral risk measures. Using the modern theory of measures of risk, we derive an equivalent representation of the portfolio problem as a zero-sum matrix game, and we provide ways to solve it by convex optimization techniques. In this way, we reconstruct new probability measures which constitute part of the saddle point of the game. These risk-adjusted measures always exist, irrespective of the completeness of the market. We provide an illustrative example, in which we derive these measures in a universe of 200 assets and we use them to evaluate the market portfolio and optimal risk-averse portfolios.     

A semi-analytical method for VaR and credit exposure analysis

In this paper, we discuss new analytical methods for computing Value-at-Risk (VaR) and a credit exposure profile. Using a Monte Carlo simulation approach as a benchmark, we find that the analytical methods are more accurate than RiskMetrics delta VaR, and are more efficient than Monte Carlo, for the case of fixed income securities. However the accuracy of the method deteriorates when applied to a portfolio of barrier options.     

Almost exact risk budgeting with return forecasts for portfolio allocation

We revisit the portfolio allocation problem with designated risk-budget. We generalize the problem of arbitrary risk budgets with unequal correlations to one that includes return forecasts and transaction costs while keeping the no-shorting constraint. We offer a convex second order cone formulation that scales well with the number of assets and explore solutions to problem variants - on equity-bond asset allocation problems as well as formulating portfolios using index constituents from the NASDAQ100 index, illustrating the benefits of this approach.     

Quadratic programming for portfolio optimization

Portfolio optimization is a procedure for generating a portfolio composition which yields the highest return for a given level of risk or a minimum risk for given level of return. The problem can be formulated as a quadratic programming problem. We shall present a new and efficient optimization procedure taking advantage of the special structure of the portfolio selection problem. An example of its application to the traditional mean-variance method will be shown. Formulation of the procedure shows that the solution of the problem is vector intensive and fits well with the advanced architecture of recent computers, namely the vector processor.     

投资组合风险的分散化研究

风险是金融投资领域的研究热点问题之下一,投资组合是降低投资风险的有效方法之一。人们在做出投资决策时总是追求在一定收益率下风险最小。本文论述了投资组合收益和风险的数学统计方法,阐明风险可分为系统风险和非系统风险,后者可以通过投资组合分散化。本文还探讨了证券相关性和组合风险之间的关系。最后作了实证分析。     

Life insurance in a portfolio context

The ownership of life insurance may be modeled as a portfolio problem in which the return on the life insurance contract is negatively correlated with the return on a claim to future wage income. The mean-variance model developed in the paper uses such a framework to express the optimal amount of insurance in terms of two components: the expected value of the wage claim and the risk/return characteristics of the insurance contract. The model thus offers an appealing way to formulate the life insurance problem in a portfolio context. Implications of the model for the functioning of a life insurance market are examined and the existence of accidental death contracts is explained.     

The benefits of differential variance-based constraints in portfolio optimization

The main problem of portfolio optimization is parameter estimation error. Various methods have been suggested to mitigate this problem, among which are shrinkage, resampling, Bayesian updating, naïve diversification, and imposing constraints on the portfolio weights. This study suggests two substantial extensions of the constrained optimization approach: the Variance-Based Constraints (VBC), and the Global Variance-Based Constraints (GVBC) methods. By the VBC method the constraint imposed on the weight of a given stock is inversely proportional to its standard deviation: the higher a stock’s sample standard deviation, the higher the potential estimation error of its parameters, and therefore the tighter the constraint imposed on its weight. GVBC employs a similar idea, but instead of imposing a sharp boundary constraint on each stock, a quadratic “cost” is assigned to deviations from the naive 1/N weight, and a single global constraint is imposed on the total cost of all deviations. Comparing ten optimization methods we find that the two new suggested methods typically yield the best performance, as measured by the Sharpe ratio. GVBC ranks first. These results are obtained for two different datasets, and are also robust to the number of assets under consideration.     

投资组合有效选择的一种实用算法

本首先介绍了组合投资中基于离散系数的均衡理论。然后提出了求解高维数组合投资问题的转轴算法,该算法的优点在于能够处理各投资项目之间的协方差矩阵为半正定的情形。