Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

Financial volatility modeling: The feedback asymmetric conditional autoregressive range model

An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross‐interdependence between the upward range and the downward range. Regression test shows that the cross‐interdependence cannot be explained by leverage effect. To include the cross‐interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in‐sample and out‐of‐sample forecasting.     

第三代蜜网在园区网中的应用

蜜网（Honeynet）是信息安全领域相对较新的一个概念,主要作用是收集黑客的攻击信息。随着蜜网技术的引入,可以将安全防护从被动方式变为主动方式。阐述了第三代蜜网体系结构,分析了数据控制、数据捕获、数据采集和数据分析模块在蜜网中的实现,并介绍了蜜网在校园网中的应用。     

云南高原湖泊群的统计学聚类识别 及水质响应模式研究

为探究湖泊群水质变量的响应模式,构建适用于监测数据匮乏的湖泊群聚类和响应模式识别方法体系(PCA-BN),包括4个步骤:数据预处理、PCA降维与湖泊聚类、贝叶斯网络构建及参数学习、湖泊响应关系模拟。以云南高原湖泊群为例开展研究,结果表明:所研究的26个湖泊可分为两类;由于第一类湖泊受到的人为干扰更严重,因而叶绿素a对总氮和总磷的响应比第二类湖泊更敏感;第一类湖泊表层水温高,溶解氧趋近饱和,随叶绿素a变化不显著,第二类湖泊溶解氧随叶绿素a升高而显著升高;两类湖泊的透明度与叶绿素a的关系一致。     

高等院校辅导员队伍专业化建设对策研究

我国高等院校辅导员队伍建设取得了一定的进展,但目前高等院校辅导员队伍建设仍面临角色定位误区、队伍结构不协调及队伍缺乏稳定性等严峻挑战。面对以上挑战,需要将高等院校辅导员队伍从普通职业转向专门职业。对此,既需要将辅导员队伍进行专业化定位,也需要从知识、年龄、学历以及专职与兼职等方面对其进行结构调整,同时,还需要在选拔机制、培养机制、激励机制和淘汰机制等方面对其进行规范化管理,从而促进高等院校辅导员队伍专业化建设。     

HD分子X1Σ+g, B1Σ+u和C1Πu态的势能函数

使用SAC/SAC-CI方法，利用D95++**、6-311++g**以及cc-PVTZ等基组，对HD分子的基态(X1Σ+g)、第二激发态(B1Σ+u)和第三激发态(C1Πu)的平衡结构和谐振频率进行了优化计算．通过对3个基组的计算结果的比较，得出了cc-PVTZ基组为三个基组中的最优基组的结论；使用cc-PVTZ基组，利用SAC的GSUM(Group Sum of Operators)方法对基态(X1Σ+g)、SAC-CI的GSUM方法对激发态(B1Σ+u)和(C1Πu)进行单点能扫描计算，用正规方程组拟合Murrell-Sorbie函数，得到了相应电子态的完整势能函数；从得到的势能函数计算了与基态(X1Σ+g)、第二激发态(B1Σ+u)和第三激发态(C1Πu)相对应的光谱常数(Be, αe, ωe 和ωeχe)，结果与实验数据基本吻合．     

Simultaneous mineralization of CO2 and recovery of soluble potassium using earth-abundant potassium feldspar

CO2 capture and storage(CCS) is an important strategy in combatting anthropogenic climate change.However,commercial application of the CCS technique is currently hampered by its high energy expenditure and costs.To overcome this issue,CO2 capture and utilization(CCU) is a promising CO2 disposal method.We,for the first time,developed a promising method to mineralize CO2 using earth-abundant potassium feldspar in order to effectively reduce CO2 emissions.Our experiments demonstrate that,after adding calcium chloride hexahydrate as an additive,the K-feldspar can be transformed to Ca-silicates at 800 C,which can easily mineralize CO2 to form stable calcium carbonate and recover soluble potassium.The conversion of this process reached 84.7%.With further study,the pretreatment temperature can be reduced to 250 C using hydrothermal method by adding the solution of triethanolamine(TEA).The highest conversion can be reached 40.1%.The process of simultaneous mineralization of CO2 and recovery of soluble potassium can be easily implemented in practice and may provide an economically feasible way to tackle global anthropogenic climate change.     

Small molecule activators of SIRT1 as therapeutics for the treatment of type 2 diabetes

Calorie restriction extends lifespan and produces a metabolic profile desirable for treating diseases of ageing such as type 2 diabetes. SIRT1, an NAD+-dependent deacetylase, is a principal modulator of pathways downstream of calorie restriction that produce beneficial effects on glucose homeostasis and insulin sensitivity. Resveratrol, a polyphenolic SIRT1 activator, mimics the anti-ageing effects of calorie restriction in lower organisms and in mice fed a high-fat diet ameliorates insulin resistance, increases mitochondrial content, and prolongs survival. Here we describe the identification and characterization of small molecule activators of SIRT1 that are structurally unrelated to, and 1,000-fold more potent than, resveratrol. These compounds bind to the SIRT1 enzyme-peptide substrate complex at an allosteric site amino-terminal to the catalytic domain and lower the Michaelis constant for acetylated substrates. In diet-induced obese and genetically obese mice, these compounds improve insulin sensitivity, lower plasma glucose, and increase mitochondrial capacity. In Zucker fa/fa rats, hyperinsulinaemic-euglycaemic clamp studies demonstrate that SIRT1 activators improve whole-body glucose homeostasis and insulin sensitivity in adipose tissue, skeletal muscle and liver. Thus, SIRT1 activation is a promising new therapeutic approach for treating diseases of ageing such as type 2 diabetes.