离散非线性系统Worst-Case辨识——小波逼近法

利用小波逼近的软阈（Ｓｏｆｔ－Ｔｈｒｅｓｈｏｌｄｉｎｇ）方法，研究了离散非线性系统的Ｗｏｒｓｔ－Ｃａｓｅ辨识问题．证明了该算法在Ｗｏｒｓｔ－Ｃａｓｅ误差下的拟最优性和光滑性；估计了该算法的Ｗｏｒｓｔ－Ｃａｓｅ误差：给出了存在鲁棒收敛的辨识算法的充要条件；最后，证明了小波网逼近算法是鲁棒收敛的．

A Genetic Approach to Analyze Algorithm Performance Based on the Worst-Case Instances

Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst case of algorithms by such a technique. In this paper, in terms of non-functional testing, we re-define the worst case of some algorithms, respectively. By using genetic algorithms (GAs), we illustrate the strategies corresponding to each type of instances. We here adopt three problems for examples;the sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP). In some algorithms solving these problems, we could find the worst-case instances successfully;the successfulness of the result is based on a statistical approach and comparison to the results by using the random testing. Our tried examples introduce informative guidelines to the use of genetic algorithms in generating the worst-case instance, which is defined in the aspect of algorithm performance.

Research on Multi-Core Processor Analysis for WCET Estimation

Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.

Analysis of Conditional Value-at-Risk for Newsvendor with Holding and Backorder Cost under Market Search

We consider a distribution system with one supplier and two retailers. For the two retailers, they face different demand and are both risk averse. We study a single period model which the supplier has ample goods and the retailers order goods separately. Market search is measured as the fraction of customers who unsatisfied with their ＂local＂ retailer due to stock-out, and search for the goods at the other retailer before leaving the system. We investigate how the retailers game for order quantity in a Conditional Value-at-Risk framework and study how risk averse degree, market search level, holding cost and backorder cost influence the optimal order strategies. Furthermore, we use uniform distribution to illustrate these results and obtain Nash equilibrium of order strategies.

On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting

This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models:sGARCH,girGARCH,eGARCH,iGARCH,aGARCH,TGARCH,NGARCH,NAGARCH,and AVGARCH along with value at risk estimation and backtesting.We use daily data for Total Nigeria Plc returns for the period January 2,2001 to May 8,2017,and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations.This investigation of the volatility,VaR,and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach.We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable.Additionally,for student t innovation,the sGARCH and girGARCH models failed to converge;the mean reverting number of days for returns differed from model to model.From the analysis of VaR and its backtesting,this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices.Furthermore,risk was reflected by significant up and down movement in the stock price at a 99%confidence level,suggesting that high risk brings a high return.

Empirical Analysis of Value-at-Risk Estimation Methods Using Extreme Value Theory

This paper investigates methods of value-at-risk (VaR) estimation using extreme value theory (EVT). It compares two different estimation methods, 'two-step subsample bootstrap' based on moment estimation and maximum likelihood estimation (MLE), according to their theoretical bases and computation procedures. Then, the estimation results are analyzed together with those of normal method and empirical method. The empirical research of foreign exchange data shows that the EVT methods have good characters in estimating VaR under extreme conditions and 'two-step subsample bootstrap' method is preferable to MLE.

Conditional Value-at-Risk for Random Immediate Reward Variables in Markov Decision Processes

We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional value-at-risk for random immediate reward variables in Markov decision processes, under whose risk measure criteria the risk-optimal policies are characterized by the optimality equations for the discounted or average case. As an application, the inventory models are considered.

Calendar Effects in AAPL Value-at-Risk

This study investigates calendar anomalies： day-of-the-week effect and seasonal effect in the Value-at-Risk （VaR） analysis of stock returns for AAPL during the period of 1995 through 2015. The statistical properties are examined and a comprehensive set of diagnostic checks are made on the two decades of AAPL daily stock returns. Combing the Extreme Value Approach together with a statistical analysis, it is learnt that the lowest VaR occurs on Fridays and Mondays typically. Moreover, high Q4 and Q3 VaR are observed during the test period. These results are valuable for anyone who needs evaluation and forecasts of the risk situation in AAPL. Moreover, this methodology, which is applicable to any other stocks or portfolios, is more realistic and comprehensive than the standard normal distribution based VaR model that is commonly used.

Variance Reduction Technique for Estimating Value-at-Risk based on the Cross - Entropy

Value-at-Risk （VaR） estimation via Monte Carlo （MC） simulation is studied here. The variance reduction technique is proposed in order to speed up MC algorithm. The algorithm for estimating the probability of high portfolio losses （more general risk measure） based on the Cross - Entropy importance sampling is developed. This algorithm can easily be applied in any light- or heavy-tailed case without an extra adaptation. Besides, it does not loose in the performance in comparison to other known methods. A numerical study in both cases is performed and the variance reduction rate is compared with other known methods. The problem of VaR estimation using procedures for estimating the probability of high portfolio losses is also discussed.

Measuring Policy Performance in Online Pricing with Offline Data:Worst-case Perspective and Bayesian Perspective

The problems of online pricing with offline data,among other similar online decision making with offline data problems,aim at designing and evaluating online pricing policies in presence of a certain amount of existing offline data.To evaluate pricing policies when offline data are available,the decision maker can either position herself at the time point when the offline data are already observed and viewed as deterministic,or at the time point when the offline data are not yet generated and viewed as stochastic.We write a framework to discuss how and why these two different positions are relevant to online policy evaluations,from a worst-case perspective and from a Bayesian perspective.We then use a simple online pricing setting with offline data to illustrate the constructions of optimal policies for these two approaches and discuss their differences,especially whether we can decompose the searching for the optimal policy into independent subproblems and optimize separately,and whether there exists a deterministic optimal policy.

收益率椭球分布不确定下的均值-CVaR优化研究

The article explores a mean-CVaR ratio model with returns distribution uncertainty.To describe the uncertainty of returns distribution,a mixture ellipsoidal distribution absorbing some typical distributions such as the mixture distribution and and ellipsoidal distribution is introduced.Then,by using robust technique with some assumptions,the original robust mean-CVaR ratio model can be formulated as a second-order cone optimization model where the underlying random returns have a mixture ellipsoidal distribution.As an illustration,the corresponding robust optimization models are applied to allocations of assets in securities market.Numerical simulations are presented to illustrate the relation between robustness and optimality and to compare mixture ellipsoidal distribution to some typical distributions as well.

Uncertainty Comparison Between Value-at-Risk and Expected Shortfall

Value-at-Risk(VaR)and expected shortfall(ES)are two key risk measures in financial risk management.Comparing these two measures has been a hot debate,and most discussions focus on risk measure properties.This paper uses independent data and autoregressive models with normal or t-distribution to examine the effect of the heavy tail and dependence on comparing the nonparametric inference uncertainty of these two risk measures.Theoretical and numerical analyses suggest that VaR at 99%level is better than ES at 97.5%level for distributions with heavier tails.

Real-time Risk-averse Dispatch of an Integrated Electricity and Natural Gas System via Condi-tional Value-at-risk-based Lookup-table Ap-proximate Dynamic Programming

The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.

面向WCET估计的Cache分析研究综述

实时系统时间分析的首要任务是估计程序的最坏情况执行时间(worst-case execution time,简称WCET).程序的WCET通常受到硬件体系结构的影响,Cache则是其中最为突出的因素之一.对面向WCET计算的Cache分析研究进行了综述,介绍了经典Cache分析框架与Cache分析核心技术,并从循环结构分析、数据Cache分析、多级Cache分析、多核共享Cache分析、非LRU替换策略分析等角度介绍了Cache分析在不同维度上的研究问题与主要挑战,总结了现有技术的优缺点,展望了Cache分析研究的未来发展方向.

A Worst-Case Risk Measure by G-VaR

G-VaR,which is a type of worst-case value-at-risk(VaR),is defined as measuring risk incorporating model uncertainty.Compared with most extant notions of worst-case VaR,G-VaR can be computed using an explicit formula,and can be applied to large portfolios of several hundred dimensions with low computational cost.We also apply G-VaR to robust portfolio optimization,thereby providing a tractable means to facilitate optimal allocations under the condition of market ambiguity.

极值理论在风险度量中的应用——基于上证180指数

精确度量风险是金融风险管理的关键问题。本文引入广义帕雷托分布代替传统的正态分布等,精确描述金融收益的厚尾特征。并将基于广义帕雷托分布的VaR模型和其它模型方法,如GARCH(1,1)、GARCH(1,1)-t、历史模拟法、方差-协方差方法,进行比较分析。实证研究表明,基于广义帕雷托分布的VaR模型比传统的模型方法更适合厚尾分布高分位点的预测,并且其预测结果比较稳定。这使得基于广义帕雷托分布的VaR模型成为VaR度量方法中最稳健的方法之一。

l_1系统辨识中的代数算法及其Worst－case误差

讨论单输入单输出、离散时不变因果系统的ｌ１系统辨识问题。首先提出基于代数方法的代数算法，并分析了该算法的特点；然后估计其Ｗｏｒｓｔ－ｃａｓｅ误差，并证明了该算法的收敛性；最后讨论了在某些特殊情况下该算法的相应形式。所给结果是面向鲁棒控制的。

RAROC方法在证券投资基金绩效评估中的应用

介绍了基于VaR的证券投资基金绩效评估方法———RAROC方法,通过该方法对基金投资绩效的分析来揭示投资风险,并对基于RAROC的绩效评估方法与传统的指数评估方法(夏普指数法、特雷诺指数法及詹森指数法)进行比较分析.结果表明:引进VaR风险度量模型,把经风险调整后的绩效评估方法RAROC应用到投资基金绩效评估中,能更客观、准确地反映证券投资基金的绩效.

金融市场风险测量的VaR方法及其应用

近年来 ,金融市场的波动性增加 ,金融机构需要准确的测量其市场风险。对市场风险的正确测量构成了市场风险管理的基础。在介绍广泛应用于测量市场风险的VaR的实质、计算方法及发展方向的基础上 。

基于WCET分析的实时系统轨迹获取技术

时序约束是判断实时系统运行是否正确的重要规约.为了减小测试时由于对系统进行插装而产生的对实时系统行为的影响,提出了一种混合式监控方法.它对系统的时间干扰比纯软件方式小,并支持对系统的完全测试.此外,还提出一种基于WCET(worst-caseexecutiontime)分析技术的目标系统时间补偿方法,在精确地计算插入断言对目标系统的时间影响基础上,给出时间补偿.