证券投资组合优化问题的强健性的锥优化方法(英文)

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.     

椭球不确定集下具有消费的投资组合鲁棒优化模型

针对含有不确定参数的优化问题,鲁棒优化作为一种有效的优化手段引起了人们的普遍关注。本文主要介绍了CVaR风险投资纽合模型,并在模型中加入消费,将椭球不确定集下鲁棒优化应用到该模型中,这不仅解决了该模型由于参数的不确定性所造成的缺陷,而且也比较符合实际情况。     

二阶锥规划一个超线性收敛的非内部连续化算法

基于非光滑向量值最小函数的一个新光滑函数, 建立了二阶锥规划一个超线性收敛的非内部连续化算法. 该算法的特点如下: 首先, 初始点任意; 其次, 每次迭代只需求解一个线性方程组即可得到搜索方向; 最后, 在无严格互补假设下, 获得算法的全局收敛性、强收敛性和超线性收敛性. 数值结果表明算法是有效的.     

不确定信息多目标线性优化的鲁棒方法

研究不确定信息的多目标线性优化问题,其数据不能精确给出但是属于一个给定的集合.首先,采用鲁棒方法把该问题转化为一个确定的多目标优化问题.然后,给出此问题解存在的充分条件.最后,通过实例验证了用鲁棒方法解决不确定信息的多目标线性优化问题的有效性.     

集值优化强有效解的广义二阶锥方向导数刻画

在实赋范线性空间中考虑集值优化问题的强有效性．借助Henig扩张锥和基泛函的性质,利用广义二阶锥方向相依导数,得到受约束于集值映射的优化问题,取得强有效元的二阶最优性必要条件．当目标函数为近似锥一次类凸映射时,利用强有效点的标量化定理,得到集值优化问题,取得强有效元的二阶充分条件．     

二阶锥线性互补问题内点法的复杂性

本文提出一个二阶锥线性互补问题的长步原始对偶内点法,搜索方向由一个一般的核函数来定义.如果给出初始的严格内点,可以得到本算法的复杂性为O((1+2k)llog(lμ0/ε)).     

线性二阶锥互补问题的一种非精确光滑算法

在光滑算法的框架下,就线性二阶锥互补问题,给出了一种非精确光滑算法. 在适当的条件下,证明了该算法具有全局收敛性. 数值试验表明该算法对高维线性二阶锥互补问题是有效的.     

基于鲁棒二阶随机占优的投资组合优化模型研究

本文主要考虑一类经典的含有二阶随机占优约束的投资组合优化问题,其目标为最大化期望收益,同时利用二阶随机占优约束度量风险,满足期望收益二阶随机占优预定的参考目标收益。与传统的二阶随机占优投资组合优化模型不同,本文考虑不确定的投资收益率,并未知其精确的概率分布,但属于某一不确定集合,建立鲁棒二阶随机占优投资组合优化模型,借助鲁棒优化理论,推导出对应的鲁棒等价问题。最后,采用S&P 500股票市场的实际数据,对模型进行不同训练样本规模和不确定集合下的最优投资组合的权重、样本内和样本外不确定参数对期望收益的影响的分析。结果表明,投资收益率在最新的历史数据规模下得出的投资策略,能够获得较高的样本外期望收益,对未来投资更具参考意义。在保证样本内解的最优性的同时,也能取得较高的样本外期望收益和随机占优约束被满足的可行性。     

二阶锥线性互补问题的低阶罚函数算法

本文研究了二阶锥线性互补问题的低阶罚函数算法.利用低阶罚函数算法将二阶锥线性互补问题转化为低阶罚函数方程组,获得了低阶罚函数方程组的解序列在特定条件下以指数速度收敛于二阶锥线性互补问题解的结果,推广了二阶锥线性互补问题的幂罚函数算法.数值实验结果验证了算法的有效性.     

锥线性算子的延拓定理

建立了一种锥分离定理,据此证明了锥线性算子的延拓定理,作为应用,给出了正线性算子的延拓定理。     

On Distributionally Robust Chance-Constrained Linear Programs

In this paper, we discuss linear programs in which the data that specify the constraints are subject to random uncertainty. A usual approach in this setting is to enforce the constraints up to a given level of probability. We show that, for a wide class of probability distributions (namely, radial distributions) on the data, the probability constraints can be converted explicitly into convex second-order cone constraints; hence, the probability-constrained linear program can be solved exactly with great efficiency. Next, we analyze the situation where the probability distribution of the data is not completely specified, but is only known to belong to a given class of distributions. In this case, we provide explicit convex conditions that guarantee the satisfaction of the probability constraints for any possible distribution belonging to the given class.Communicated by B. T. PolyakThis work was supported by FIRB funds from the Italian Ministry of University and Research.     

Adjustable robust counterpart of conic quadratic problems

This paper presents an approximate affinely adjustable robust counterpart for conic quadratic constraints. The theory is applied to obtain robust solutions to the problems of subway route design with implementation errors and a supply chain management with uncertain demands. Comparison of the adjustable solutions with the nominal and non-adjustable robust solutions shows that the adjustable (dynamic) robust solution maintains feasibility for all possible realizations, while being less conservative than the usual (static) robust counterpart solution.     

Effects of risk aversion and decision preference on equilibriums in supply chain finance incorporating bank credit with credit guarantee

We constructed a Stackelberg game in a supply chain finance (SCF) system including a manufacturer, a capital‐constrained retailer, and a bank that provides loans on the basis of the manufacturer's credit guarantee. To emphasize the financial service providers' risks, we assumed that both the bank and the manufacturer are risk‐averse and formulated trade‐off objective functions for both of them as the convex combination of the expected profit and conditional value‐at‐risk. To explore the effects of the risk preferences and decision preferences on SCF equilibriums, we mathematically analyzed the optimal order quantities, wholesale prices, and interest rates under different risk preference scenarios and performed numerical analyses to quantify the effects. We found that incorporating bank credit with a credit guarantee can effectively balance the retailer's financing risk between the bank and the manufacturer through interest rate charging and wholesale pricing. Moreover, SCF equilibriums with risk aversion are highly affected by the degree of both the lender's and guarantor's risk tolerance in regard to the borrower's default probability and will be more conservative than those in the risk‐neutral cases that only maximize expected profit.     

Precautionary measures for credit risk management in jump models

Sustaining efficiency and stability by properly controlling the equity to asset ratio is one of the most important and difficult challenges in bank management. Due to unexpected and abrupt decline of asset values, a bank must closely monitor its net worth as well as market conditions, and one of its important concerns is when to raise more capital so as not to violate capital adequacy requirements. In this paper, we model the trade-off between avoiding costs of delay and premature capital raising, and solve the corresponding optimal stopping problem. In order to model defaults in a bank's loan/credit business portfolios, we represent its net worth by Lévy processes, and solve explicitly for the double exponential jump-diffusion process and for a general spectrally negative Lévy process.     

Credit risk optimization using factor models

We study portfolio credit risk management using factor models, with a focus on optimal portfolio selection based on the tradeoff of expected return and credit risk. We begin with a discussion of factor models and their known analytic properties, paying particular attention to the asymptotic limit of a large, finely grained portfolio. We recall prior results on the convergence of risk measures in this “large portfolio approximation” which are important for credit risk optimization. We then show how the results on the large portfolio approximation can be used to reduce significantly the computational effort required for credit risk optimization. For example, when determining the fraction of capital to be assigned to particular ratings classes, it is sufficient to solve the optimization problem for the large portfolio approximation, rather than for the actual portfolio. This dramatically reduces the dimensionality of the problem, and the amount of computation required for its solution. Numerical results illustrating the application of this principle are also presented. JEL Classification G11     

Gompertz zero-inflated cure rate regression models applied to credit risk data

In the financial market, it is important to consider that there is a proportion of customers that have settled their debt in time zero, immediately recovering their ability to pay. In this context, in this paper, we propose a survival analysis methodology that allows the insertion of times equal to zero in scenarios where credit risk is observed. The proposed model addresses the survival analysis model of the zero-inflated cure rate which incorporates the heterogeneity of three subgroups (individuals having events in the initial time, and individuals not susceptible and susceptible to the event). In our proposal, all available survival data of customers are modeled considering that the number of competitive causes follows a Poisson distribution and the baseline risk function follows a Gompertz distribution. The model parameter estimation is obtained by the maximum likelihood estimation procedure and simulation studies are conducted to evaluate the estimators' performance. The studied methodology will be applied to a credit database provided by a financial institution in Brazil.     

A complete characterization of disjunctive conic cuts for mixed integer second order cone optimization

We study the convex hull of the intersection of a disjunctive set defined by parallel hyperplanes and the feasible set of a mixed integer second order cone optimization (MISOCO) problem. We extend our prior work on disjunctive conic cuts (DCCs), which has thus far been restricted to the case in which the intersection of the hyperplanes and the feasible set is bounded. Using a similar technique, we show that one can extend our previous results to the case in which that intersection is unbounded. We provide a complete characterization in closed form of the conic inequalities required to describe the convex hull when the hyperplanes defining the disjunction are parallel.     

Optimality conditions for vector optimization problem governed by the cone constrained generalized equations

ABSTRACT

The paper considers a class of vector optimization problems with cone constrained generalized equations. By virtue of advanced tools of variational analysis and generalized differentiation, a limiting normal cone of the graph of the normal cone constrained by the second-order cone is obtained. Based on the calmness condition, we derive an upper estimate of the coderivative for a composite set-valued mapping. The necessary optimality condition for the problem is established under the linear independent constraint qualification. As a special case, the obtained optimality condition is simplified with the help of strict complementarity relaxation conditions. The numerical results on different examples are given to illustrate the efficiency of the optimality conditions.     

Restructuring risk in credit default swaps: An empirical analysis

This paper estimates the price for restructuring risk in the US corporate bond market during 1999–2005. Comparing quotes from default swap (CDS) contracts with a restructuring event and without, we find that the average premium for restructuring risk represents 6%–8% of the swap rate without restructuring. We show that the restructuring premium depends on firm-specific balance-sheet and macroeconomic variables. And, when default swap rates without a restructuring event increase, the increase in restructuring premia is higher for low-credit-quality firms than for high-credit-quality firms. We propose a reduced-form arbitrage-free model for pricing default swaps that explicitly incorporates the distinction between restructuring and default events. A case study illustrating the model’s implementation is provided.