随机非线性互补问题的条件风险价值模型及其求解方法

随机非线性互补问题(SNCP)在交通运输,工程力学,金融等许多方面都有着非常广泛的应用,由于随机因素的存在,SNCP通常无解.为解决这个问题,考虑构造一个合理的确定性模型,并将这个确定性模型的解作为SNCP的解.文章利用限定非线性互补函数(NCP函数)来构造投资组合优化中的损失函数,提出求解随机非线性互补问题(SNCP)的条件风险价值(CVaR)模型.由于该模型中含有数学期望及非光滑函数,为求解此模型,文章应用样本均值近似方法和光滑化方法,给出此模型的近似问题并进一步给出求解算法.在理论上,文章还考虑了条件风险价值模型水平集的有界性及该模型近似问题全局最优解序列以及稳定点的收敛性结果.以上结果从理论上保证了文章所提求解SNCP的新模型及其近似问题的可行性.此外,数值结果表明上述方法是有效的.     

随机广义纳什均衡问题带有条件风险价值约束的确定性模型及其求解方法

主要考虑随机广义纳什均衡问题(SGNEP),由于随机变量的存在,SGNEP通常无解.对此问题,文章首先给出一阶必要性条件并利用NCP函数得到优化模型的目标函数,为降低所得解的"风险",再利用条件风险价值(CVaR)给出约束条件,从而构造出求解SGNEP的一个低风险模型,并将此模型所得解视为SGNEP的解.然而,直接求解该低风险模型可能会遇到两个问题:一是该模型含有非光滑约束,二是目标函数和约束条件包含期望值.考虑到这两个问题,采用光滑化和罚样本均值近似方法提出该模型的近似问题,并进一步给出近似问题最优解的收敛性结果.最后,文章给出数值算例,以验证所提方法的可行性.     

考虑交易费用的二阶随机占优投资组合风险控制模型

本文通过引入交易费用函数,建立了一个更符合实际的带有二阶随机占优约束的投资组合风险控制模型.该模型不需要对投资者的效用函数和风险资产收益的分布作任何假设,就可以确保风险厌恶投资者所做的选择都会随机占优于一个基准值,从而可以规避高风险投资.针对优化模型的求解,设计了一种光滑化样本平均值近似罚函数方法,理论上证明了光滑化罚问题与原问题的等价性.数值结果验证了模型和算法的有效性.     

基于分位点的广义Pareto分布函数最小二乘拟合方法

广义Pareto分布函数(GPD,generalized Pareto distribution)是一种针对随机参数尾部进行渐进插值的方法,能够对高可靠性问题进行评估.应用该函数进行随机参数尾部近似时,需要对函数中的两个重要未知参数进行拟合确定.最常用的拟合方法是最大似然拟合和最小二乘拟合,需要将所有的尾部样本进行计算;需要大量尾部样本,计算效率低.该文提出依据少量的分位点进行最小二乘拟合,既保证了尾部样本空间足够大,同时又降低了计算成本;进一步提出了Kriging模型的两阶段更新,实现了分位点求解的快速收敛.算例表明,该文提出的方法能够快速提高模型精度,求得指定的分位点,而且与基于大量尾部样本的最大似然拟合结果精度一致.     

系统安全运行约束下的动态环境经济调度

文章研究风电入网情况下系统安全运行的动态环境经济调度问题.基于风电预测误差,权衡效益和系统运行风险,建立了购买可中断负荷(interruptible load,IL)的经济调度优化模型.新模型的目标函数考虑了传统火电机组能耗成本、环境成本、阀点效应成本和可中断负荷补偿成本.系统运行约束上,采用条件风险价值(conditional value-at-risk,CVaR)刻画因风电间歇性和随机性导致的系统不安全运行风险,结合火电机组出力和多时间段爬坡限制建立了系统的安全运行约束.模型的求解上,采用罚函数方法、光滑化技术和样本平均方法相结合,提出了一类新的随机优化算法;IEEE-30节点系统测试了模型和算法的有效性.     

计及碳排放配限额的虚拟电厂多目标风险规避优化模型

将风电场、光伏发电、生物质发电、储能和燃气轮机及柔性负荷聚合为虚拟电厂(Virtual power plant,VPP).进一步,为刻画风光不确定性风险,分别利用条件风险价值方法(Conditional risk at value,CVaR)构造最小化运营风险目标函数及利用鲁棒随机优化理论转化含不确定性变量约束条件,并选取最大化运营收益和最小化碳排放总量,构建VPP多目标风险规避优化模型.最后,选取改进IEEE30节点系统进行算例分析,结果表明:1)所提风险规避模型能够兼顾效益、风险和碳排放多方诉求;特别是,当鲁棒系数Γ≤0.85,较小的不确定性会带来较大的风险,表明决策者风险态度会影响VPP调度方案;2)预测误差e较高时,相同的Γ增长幅度会带来更高的CVaR增长幅度,表明较低的预测精度会放大不确定性风险,意味着决策者需通过提升预测精度以降低VPP运营风险;3) META能凸显清洁能源环境友好特性,实现VPP整体的最优均衡运行.综上,所提模型能够为决策制定最优VPP调度策略提供决策支撑.     

求解随机二阶锥互补问题的期望值模型及其近似问题的收敛性分析

利用二阶锥互补函数φ_(NR)给出求解随机二阶锥互补问题的确定期望值(EV)模型.由于该模型的目标函数非光滑,利用光滑化方法给出该模型的光滑化近似问题.当期望值可以求得时,考虑了光滑近似问题的收敛性结果.当期望值不易求得时,利用样本均值近似方法给出光滑化样本均值近似问题,并考虑了当光滑参数不变的情况下,光滑化样本均值近似问题的收敛性结果.     

基于风险约束的套期保值模型及沪深300股指实证研究

提出利用风险价值VaR建立套期保值资产组合的风险约束.以套期保值资产组合收益最大为目标,以控制套期保值资产组合风险为约束,建立了基于风险约束的套期保值模型.该模型在有效控制风险的基础上,可以大幅提高套期保值资产组合的收益.对沪深300股指现货和期货的数据进行了实证分析,对比了现有研究的最小二乘((OLS)、向量自回归(VAR)、向量误差修正(VEC)三种模型以及本文建立的基于风险约束的期货套期保值模型.样本内检验结果表明,本模型比现有研究模型的收益有大幅提高,平均增加81.6%.同时并没有失去对风险的控制,与现有研究模型只有5.32%的差别.对于样本外检验,模型在控制风险和提高收益两个方面都要优于现有研究模型.模型比现有研究模型平均可提高收益21.4%,平均降低风险3.61%.     

基于矩分析的资产组合选择

在风险资产收益分布为非正态的情景下,通过矩分析,研究其收益的高阶矩对资产组合选择的影响.首先,假设风险资产收益存在有限阶矩,泰勒展开边际财富期望效用,获得静态资产组合选择的近似解;其次,假设收益过程的跳跃产生收益分布的非正态性,运用随机控制方法获得动态资产组合选择的近似解析解,从高阶矩角度解释其特征。分析表明,超出峰度的存在导致减少风险资产投资,正(负)的偏度导致增加(减少)风险资产投资,该影响性随着它们及风险规避系数的增大而增强;可预测性导致资产组合存在正或负的对冲需求,取决于相关系数的符号和风险规避系数;跳跃性总体上减少风险资产投资;可预测性和跳跃性对动态资产组合选择的影响具有内在关联性。     

基于自适应动态规划的随机时滞线性二次型最优跟踪控制

针对模型未知且带有时滞的随机线性二次型(SLQ)最优跟踪控制问题,提出了一种自适应动态规划(ADP)算法.首先,利用双因果坐标变换导出原时滞系统的等效系统,构造一个新的由等效系统和命令生成器组成的增广系统,并给出该增广系统的随机代数方程.其次,为了解决随机线性二次最优跟踪控制问题,将随机问题转化为确定性问题.然后提出ADP算法,并给出该算法的收敛性分析.为了实现ADP算法,设计了三种神经网络,分别近似最优性能指标函数,最优控制增益矩阵和系统模型.最后,通过一个数值算例验证算法的有效性.     

Single and multi-period optimal inventory control models with risk-averse constraints

This paper presents some convex stochastic programming models for single and multi-period inventory control problems where the market demand is random and order quantities need to be decided before demand is realized. Both models minimize the expected losses subject to risk aversion constraints expressed through Value at Risk (VaR) and Conditional Value at Risk (CVaR) as risk measures. A sample average approximation method is proposed for solving the models and convergence analysis of optimal solutions of the sample average approximation problem is presented. Finally, some numerical examples are given to illustrate the convergence of the algorithm.     

基于双因子定价模型的投资组合风险价值的多分辨率特征研究

为了识别金融市场投资风险的多分辨率特征及优化投资组合,以资本市场风险和汇率市场风险为双因子定价模型的风险因子,利用小波方差及协方差无偏估计量,给出了单个资产风险因子敏感度多分辨率计算方法,以此为基础还得到了投资组合的风险值(VaR)及其边际风险值(MVaR)的多分辨率分解公式.对上证A股市场的实证分析表明投资组合的风险价值及边际风险价值依赖于投资时限,短线投资潜在的损失比长线投资要大;进一步分析表明中国股市存在多分辨率风险特征,这种风险特点可能是由市场系统风险和汇率风险以及异质性投资活动共同作用的结果.研究还发现多分辨率边际风险价值是确定多分辨率投资组合优化模型求解的重要条件之一.     

TTRISK: Tensor train decomposition algorithm for risk averse optimization

This article develops a new algorithm named TTRISK to solve high-dimensional risk-averse optimization problems governed by differential equations (ODEs and/or partial differential equations [PDEs]) under uncertainty. As an example, we focus on the so-called Conditional Value at Risk (CVaR), but the approach is equally applicable to other coherent risk measures. Both the full and reduced space formulations are considered. The algorithm is based on low rank tensor approximations of random fields discretized using stochastic collocation. To avoid nonsmoothness of the objective function underpinning the CVaR, we propose an adaptive strategy to select the width parameter of the smoothed CVaR to balance the smoothing and tensor approximation errors. Moreover, unbiased Monte Carlo CVaR estimate can be computed by using the smoothed CVaR as a control variate. To accelerate the computations, we introduce an efficient preconditioner for the Karush–Kuhn–Tucker (KKT) system in the full space formulation.The numerical experiments demonstrate that the proposed method enables accurate CVaR optimization constrained by large-scale discretized systems. In particular, the first example consists of an elliptic PDE with random coefficients as constraints. The second example is motivated by a realistic application to devise a lockdown plan for United Kingdom under COVID-19. The results indicate that the risk-averse framework is feasible with the tensor approximations under tens of random variables.     

The value of rolling-horizon policies for risk-averse hydro-thermal planning

We consider the optimal management of a hydro-thermal power system in the mid and long terms. From the optimization point of view, this amounts to a large-scale multistage stochastic linear program, often solved by combining sampling with decomposition algorithms, like stochastic dual dynamic programming. Such methodologies, however, may entail prohibitive computational time, especially when applied to a risk-averse formulation of the problem. We propose instead a risk-averse rolling-horizon policy that is nonanticipative, feasible, and time consistent. The policy is obtained by solving a sequence of risk-averse problems with deterministic constraints for the current time step and future chance and CVaR constraints.The considered hydro-thermal model takes into account losses resulting from run-of-river plants efficiencies as well as uncertain demand and streamflows. Constraints aim at satisfying demand while keeping reservoir levels above minzones almost surely. We show that if the problem uncertainty is represented by a periodic autoregressive stochastic process with lag one, then the probabilistic constraints can be computed explicitly. As a result, each one of the aforementioned risk-averse problems is a medium-size linear program, easy to solve.For a real-life power system we compare our approach with three alternative policies. Namely, a robust nonrolling-horizon policy and two risk-neutral policies obtained by stochastic dual dynamic programming, implemented in nonrolling- and rolling-horizon modes, respectively. Our numerical assessment confirms the superiority of the risk-averse rolling-horizon policy that yields comparable average indicators, but with reduced volatility and with substantially less computational effort.     

基于Bayes估计的金融风险值——VaR计算

初步研究了用Bayes估计计算金融风险值VaR,同时阐明了运用极值理论方法在Bayes估计下的金融风险值计算。并且借助统计计算方法——MCMC算法来求解参数的Bayes估计,有效的将Bayes思想融入到了VaR的计算中。用Bayes估计计算金融风险值VsR,可以帮助投资者将观测数据和自己所掌握的经验信息对VaR模型进行调整,使得vsR模型能够更准确地反映出金融市场的风险状况,据此做出更加正确的投资决策。     

基于MCMC模拟的MGPD模型及其在地质灾害风险度量中的应用

基于马尔科夫链蒙特卡洛(简记为MCMC)模拟的参数贝叶斯估计,对改进的广义帕累托分布(简记为MGPD)模型进行了优化,并利用该模型得到了地质灾害损失的在险损失值(简记为VaR)和条件损失值(简记为CVaR).以湖南娄底市地质灾害损失数据进行实证分析及模型适应性检验,结果表明:优化后的模型不仅具有很好的极值数据描述能力,而且具有较强的适用性.     

Tail VaR Measures in a Multi-period Setting

Abstract

This paper studies a coherent acceptability measure which is a negative coherent risk measure, in a multi-period model. When a coherent acceptability measure changes according to new information in the market, a time consistency plays an important role. The usual strong time consistency gives too severe a multi-period Tail Value at Risk (Tail VaR) from a practical viewpoint. We study a weak type of time consistency and propose new multi-period Tail VaR measures.     

Optimal and coherent economic-capital structures: evidence from long and short-sales trading positions under illiquid market perspectives

This paper broadens research literature associated with the assessment of modern portfolio risk management techniques by presenting a thorough modeling of nonlinear dynamic asset allocation and management under the supposition of illiquid and adverse market settings. Specifically, the paper proposes a re-engineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (L-VaR) framework, and particularly from the perspective of trading portfolios that have both long and short-sales trading positions. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios, over the holding period, with the aid of an appropriate scaling of the multiple-assets’ L-VaR matrix along with GARCH-M technique to forecast conditional volatility and expected return. Moreover, in this paper, the authors develop a dynamic nonlinear portfolio selection model and an optimization algorithm which allocates both economic capital and trading assets subject to some selected financial and operational rational constraints. The empirical results strongly confirm the importance of enforcing financially and operationally meaningful nonlinear and dynamic constraints, when they are available, on economic capital optimization procedure. The empirical results are interesting in terms of theory as well as practical applications and can aid in developing robust portfolio management algorithms that financial entities could consider in light of the aftermath of the latest financial crisis.     

Robust portfolio optimization with copulas

Conditional Value at Risk (CVaR) is widely used in portfolio optimization as a measure of risk. CVaR is clearly dependent on the underlying probability distribution of the portfolio. We show how copulas can be introduced to any problem that involves distributions and how they can provide solutions for the modeling of the portfolio. We use this to provide the copula formulation of the CVaR of a portfolio. Given the critical dependence of CVaR on the underlying distribution, we use a robust framework to extend our approach to Worst Case CVaR (WCVaR). WCVaR is achieved through the use of rival copulas. These rival copulas have the advantage of exploiting a variety of dependence structures, symmetric and not. We compare our model against two other models, Gaussian CVaR and Worst Case Markowitz. Our empirical analysis shows that WCVaR can asses the risk more adequately than the two competitive models during periods of crisis.     

Market risk forecasting for high dimensional portfolios via factor copulas with GAS dynamics

In this paper we propose forecasting market risk measures, such as Value at Risk (VaR) and Expected Shortfall (ES), for large dimensional portfolios via copula modeling. For that we compare several high dimensional copula models, from naive ones to complex factor copulas, which are able to simultaneously tackle the curse of dimensionality and introduce a high level of complexity into the model. We explore both static and dynamic copula fitting. In the dynamic case we allow different levels of flexibility for the dependence parameters which are driven by a GAS (Generalized Autoregressive Scores) model, in the spirit of Oh and Patton (2015). Our empirical results, for assets negotiated at Brazilian BOVESPA stock market from January, 2008 to December, 2014, suggest that, compared to the other copula models, the GAS dynamic factor copula approach has a superior performance in terms of AIC (Akaike Information Criterion) and a non-inferior performance with respect to VaR and ES forecasting.