不可交易资产的平方套期保值问题

提出并解决了不可交易资产的套期保值问题.基于金融实际构建了不可交易资产套期保值模型,在风险资产价格服从跳扩散模型的假设下提出了三个平方套期保值问题.借助于一个辅助过程和Hilbert空间投影定理,利用市场可观测量以后向形式给出了平方套期保值标准下的最优策略.最后通过Monte Carlo方法验证了套期保值策略的有效性.     

跳扩散结构下风险最小化动态套期保值策略研究

在标的资产价格服从跳-扩散过程情况下,研究了风险最小化动态套期保值问题.首先用MCMC方法估计得到模型参数值,克服了传统的直接用样本均值和样本方差进行参数估计值的不足,与市场实际更吻合;然后在风险最小目标下,采用逐步倒推法得到随时间改变的动态最优套期保值策略解析表达式,由此可以及时做出策略调整,达到既对冲风险又节约成本的目的.文章最后通过对比分析不同期限、不同策略调整频率情况下的费用投入,得出期限和策略调整频率之间的关系,为套期保值者根据不同情况做出合理的套保策略提供了参考,另外,为满足金融机构进行压力测试或投资者为适应费率调整的需要,也分析说明了不同交易费率和策略之间的关系.     

内部信息下的平方套期保值策略

建立了内部信息市场模型,提出并解决了内部信息投资者的平方最优套期保值问题.首先利用初始滤波扩张方法给出了内部信息市场中风险资产的价格动态.其次利用Ito公式和Galtchouk-Kunita-Watanabe分解给出了最优策略的显式表示.     

基于Monte-Carlo模拟的最小亏损套期保值策略研究

把终期的期望亏损定义为风险,研究了标的资产价格服从跳扩散结构时的自筹资最小亏损风险套期保值.首先通过Monte-Carlo模拟生成标的资产若干条价格路径并用所有路径上的终期亏损平均值作为优化目标期望值的估计,然后引入基函数作为套期保值头寸的近似逼近,最后通过数值方法得到最优套期保值策略.最后通过实例分析表明:1)套期保值头寸调整的频率相对较高时,可以更好地应对市场出现的价格波动,从而降低可能面临的损失风险,达到较好的保值效果;2)欧式看涨期权的交割价格与对冲头寸呈反向变化,交割价格越高,可适当调低持有的对冲头寸,反之则反,这样,即对冲风险又节约成本.     

内部附加信息下的风险最小套期保值策略

在跳扩散价格模型下,利用扩大信息流方法解决了内部信息者的风险最小套期保值问题.首先构建了内部信息市场模型,提出了内部附加信息下的风险最小套期保值问题.然后利用利用风险资产价格的Markov性、Ito公式得到了内部信息下的风险最小套期保值策略的显式表示.     

带交易费的未定权益有偏好套期保值定价

本文首先引进了正常化市场和有偏好套期保值概念，给出了有偏好系数的未定权益套期保值定价．由此进一步给出未定权益的卖方价和买方价，以及未定权益的定价区间．     

基于动态条件相关系数模型的石油市场套期保值比估计

以WTI和Brent两地的原油现货市场和期货市场为研究对象,选择对角化的动态条件相关(DCC)模型估计了市场间的动态条件相关系数,求解了WTI市场、Brent市场及跨市的动态套期保值比,评价了各种市场组合的套期保值效果.得到如下几点结论:第一,WTI市场的一体化程度高于Brent市场;第二,两个月期货的套期保值比高于1个月期货的套期保值比,WTI相应市场组合的套期保值比要高于Brent市场;第三,采取Brent期货对WTI现货进行对冲时,其套期保值比要高于用WTI期货对Brent现货对冲时的情形,也高于Brent市场的套期保值比;第四,套期保值比越高,套期保值效果越好.     

股指期货考虑套期保值成本时的套期保值比率研究

所谓股指期货,就是以某种股票指数为标的物的金融期货合约.它同时具有股票与期货的特性,是组合投资者规避系统风险的重要金融衍生工具.针对股指期货,在考虑套期保值成本的前提下,利用套利和CAPM模型给出最优套期保值比率的计算公式.这将在一定的程度上,提高了计算的准确性,并且减少计算的工作量.     

期货市场多阶段系列展期套期保值策略研究

在资产价格波动受多种因素影响时,系列套期保值是一种比成堆展期套期保值效果更好的策略,针对SH策略的不足,提出一类拓展的系列展期策略——MSRH,定义了其基差,建立了一般决策模型,最后使用一个实例进行说明.     

基于EWMA模型的铜期货动态套期保值效果研究

利用我国铜期货市场的真实交易数据以及铜现货市场的日结算价为研究对象,以投资组合收益率方差最小化为目标,建立了OLS,ECM,VECM,B-VAR 4种静态套期保值模型,针对金融市场收益率尖峰厚尾和波动率聚集的特征,构建了基于最优衰减因子的时变方差的EWMA模型的动态套期保值方案,并且对静态与动态模型的套期保值效果进行分析比较,不但考虑了所用实证数据的实际特点,而且考虑了套期保值比率预测的准确性和经济性,实证结果表明,该动态模型优于传统的静态套期保值模型.     

Efficient Sequential Quadratic Programming Implementations for Equality-Constrained Discrete-Time Optimal Control

Efficient sequential quadratic programming (SQP) implementations are presented for equality-constrained, discrete-time, optimal control problems. The algorithm developed calculates the search direction for the equality-based variant of SQP and is applicable to problems with either fixed or free final time. Problem solutions are obtained by solving iteratively a series of constrained quadratic programs. The number of mathematical operations required for each iteration is proportional to the number of discrete times N. This is contrasted by conventional methods in which this number is proportional to N ³. The algorithm results in quadratic convergence of the iterates under the same conditions as those for SQP and simplifies to an existing dynamic programming approach when there are no constraints and the final time is fixed. A simple test problem and two application problems are presented. The application examples include a satellite dynamics problem and a set of brachistochrone problems involving viscous friction.     

A Semidefinite Programming Heuristic for Quadratic Programming Problems with Complementarity Constraints

The presence of complementarity constraints brings a combinatorial flavour to an optimization problem. A quadratic programming problem with complementarity constraints can be relaxed to give a semidefinite programming problem. The solution to this relaxation can be used to generate feasible solutions to the complementarity constraints. A quadratic programming problem is solved for each of these feasible solutions and the best resulting solution provides an estimate for the optimal solution to the quadratic program with complementarity constraints. Computational testing of such an approach is described for a problem arising in portfolio optimization.Research supported in part by the National Science Foundations VIGRE Program (Grant DMS-9983646).Research partially supported by NSF Grant number CCR-9901822.     

粘塑性问题的参数二次规划法

本文应用参变量变分方法处理Perzyna粘塑性问题,将原问题化为求解带约束条件的二次规划问题,文中具体讨论了用于该问题的有限单元形式及具体实施过程.     

Stabilized Sequential Quadratic Programming

Recently, Wright proposed a stabilized sequential quadratic programming algorithm for inequality constrained optimization. Assuming the Mangasarian-Fromovitz constraint qualification and the existence of a strictly positive multiplier (but possibly dependent constraint gradients), he proved a local quadratic convergence result. In this paper, we establish quadratic convergence in cases where both strict complementarity and the Mangasarian-Fromovitz constraint qualification do not hold. The constraints on the stabilization parameter are relaxed, and linear convergence is demonstrated when the parameter is kept fixed. We show that the analysis of this method can be carried out using recent results for the stability of variational problems.     

无界域上不定二次规划的一个算法

本文给出了无界域上不定二次规划一个算法 ,该算法将不定二次规划转化为一系列凸二次规划 ,并证明了算法的收敛性 .     

Dynamic Programming Method for Constrained Discrete-Time Optimal Control

A dynamic programming method is presented for solving constrained, discrete-time, optimal control problems. The method is based on an efficient algorithm for solving the subproblems of sequential quadratic programming. By using an interior-point method to accommodate inequality constraints, a modification of an existing algorithm for equality constrained problems can be used iteratively to solve the subproblems. Two test problems and two application problems are presented. The application examples include a rest-to-rest maneuver of a flexible structure and a constrained brachistochrone problem.     

New Theoretical Results on Recursive Quadratic Programming Algorithms

Recursive quadratic programming is a family of techniques developed by Bartholomew-Biggs and other authors for solving nonlinear programming problems. The first-order optimality conditions for a local minimizer of the augmented Lagrangian are transformed into a nonlinear system where both primal and dual variables appear explicitly. The inner iteration of the algorithm is a Newton-like procedure that updates simultaneously primal variables and Lagrange multipliers. In this way, as observed by Gould, the implementation of the Newton method becomes stable, in spite of the possibility of having large penalization parameters. In this paper, the inner iteration is analyzed from a different point of view. Namely, the size of the convergence region and the speed of convergence of the inner process are considered and it is shown that, in some sense, both are independent of the penalization parameter when an adequate version of the Newton method is used. In other words, classical Newton-like iterations are improved, not only in relation to stability of the linear algebra involved, but also with regard to the ovearll convergence of the nonlinear process. Some numerical experiments suggset that, in fact, practical efficiency of the methods is related to these theoretical results.     

一类0-1二次规划最优解的新算法

从矩阵的基础知识出发,给出了当目标函数矩阵是严格对角占优阵时,快速地获得0-1二次规划最优解的一个新算法;该方法具有很强的实用性,是此类问题的一个高效求解算法.     

Redundancies in Positive-Semidefinite Quadratic Programming

The question investigated is how to detect nonactive restrictions in positive-semidefinite quadratic programming. If the optimization problem satisfies some regularity conditions, we can use parametric optimization techniques for that analysis. It turns out that results obtained in Ref. 1, where only positive-definite matrices are considered, can be generalized to the semidefinite case. Simple calculations based exclusively on the problem data allow one to delete superfluous restrictions for this problem class during an optimization procedure.