Long memory and nonlinear dependence structure in crude oil futures returns and volatility

In order to investigate the nature of international crude oil futures and present evidence of long memory and nonlinear dependence for crude oil futures volatility as well as returns, a certain number of recent statistical tests, such as the powerful BDS test, the fractional integration test and other known statistics, are applied. The results show that though the returns themselves contain little serial correlation, the market volatility series have significant long-term dependence structures which may have important implications for volatility forecasts and derivative pricing. On the other hand, evidence of strong ARCH effect is also presented, and, moreover, the BDS statistics on the standardized residuals of the fitted GARCH model indicate that the ARCH-type process may generally explain the nonlinearities in the data. It seems that the crude oil futures market can be appropriately modeled by ARCH and fractal processes. These findings indicate that it would be beneficial to assess the behavior of the crude oil and price the oil derivative contracts by encompassing long memory and nonlinear structure.     

从《中国大学评价》看长江大学的发展现状

现行的大学评价指标体系和排名方式,需要不断改进和完善;但这种以定量为主的评价方法,与传统的单纯定性评价的方式相比,具有直观清晰、数据客观的特点,说服力更强。分析《中国大学评价》对长江大学的排名,可为长江大学开展战略管理和年度发展评价提供一种新的视角。     

我国开放式基金波动择时能力的实证分析

通过改进的投资组合表现评价单因素模型，并结合FF3模型，运用参数检验方法来探讨开放式基金的波动择时能力，研究表明我国开放式基金具有一定的波动择时能力，但比较弱．     

中美股市波动的联动性的实证分析

用二元GARCH模型的方法建立了中美股票市场的波动模型,考察了中美两个股票市场从2002—2007年间的股指波动的联动性问题。结果显示:随着中国经济的不断开放,中国的股票市场的波动和美国股票市场的波动存在递增的联动性,且中国股票市场的波动对美国股票市场的波动的单方面影响尤其显著。分析了产生这种结果的原因,并提出相关的建议     

Examining drug and side effect relation using author–entity pair bipartite networks

The current study has two objectives. First, we explore the characteristics of biological entities, such as drugs, and their side effects using an author–entity pair bipartite network. Second, we use the constructed network to examine whether there are outstanding features of relations between drugs and side effects. We extracted drug and side effect names from 169,766 PubMed abstracts published between 2010 to 2014 and constructed author–entity pair bipartite networks after ambiguous author names were processed. We propose a new ranking algorithm that takes into consideration the characteristics of bipartite networks to identify top-ranked biological drug and side effect pairs. To investigate the relationship between a particular drug and a side effect, we compared the drug and side effect pairs obtained from the network containing both drug and side effect with those observed in SIDER, a human expert-curated database. The results of this study indicate that our approach was able to identify a wide range of patterns of drug–side effect relations from the perspective of authors’ research interests. Further, our approach also identified the unique characteristics of the relation of biomedical entities obtained using an author–entity pair bipartite network.     

Globalised vs averaged: Bias and ranking performance on the author level

We analyse the difference between the averaged (average of ratios) and globalised (ratio of averages) author-level aggregation approaches based on various paper-level metrics. We evaluate the aggregation variants in terms of (1) their field bias on the author-level and (2) their ranking performance based on test data that comprises researchers that have received fellowship status or won prestigious awards for their long-lasting and high-impact research contributions to their fields. We consider various direct and indirect paper-level metrics with different normalisation approaches (mean-based, percentile-based, co-citation-based) and focus on the bias and performance differences between the two aggregation variants of each metric. We execute all experiments on two publication databases which use different field categorisation schemes. The first uses author-chosen concept categories and covers the computer science literature. The second covers all disciplines and categorises papers by keywords based on their contents. In terms of bias, we find relatively little difference between the averaged and globalised variants. For mean-normalised citation counts we find no significant difference between the two approaches. However, the percentile-based metric shows less bias with the globalised approach, except for citation windows smaller than four years. On the multi-disciplinary database, PageRank has the overall least bias but shows no significant difference between the two aggregation variants. The averaged variants of most metrics have less bias for small citation windows. For larger citation windows the differences are smaller and are mostly insignificant.In terms of ranking the well-established researchers who have received accolades for their high-impact contributions, we find that the globalised variant of the percentile-based metric performs better. Again we find no significant differences between the globalised and averaged variants based on citation counts and PageRank scores.     

Subset selection of best simulated systems

In this paper, we consider the problem of selecting a subset of k systems that is contained in the set of the best s simulated systems when the number of alternative systems is huge. We propose a sequential method that uses the ordinal optimization to select a subset G randomly from the search space that contains the best simulated systems with high probability. To guarantee that this subset contains the best systems it needs to be relatively large. Then methods of ranking and selections will be applied to select a subset of k best systems of the subset G with high probability. The remaining systems of G will be replaced by newly selected alternatives from the search space. This procedure is repeated until the probability of correct selection (a subset of the best k simulated systems is selected) becomes very high. The optimal computing budget allocation is also used to allocate the available computing budget in a way that maximizes the probability of correct selection. Numerical experiments for comparing these algorithms are presented.     

金融波动持续性的研究

本文首先介绍了金融市场中单个变量波动持续性的含义，包括一阶意义上的长记忆性以及二阶意义上的方差持续性，概述了已有的模型，运用分形市场理论阐明了波动持续性的经济涵义和市场机制。然后，指出了多个变量波动之间协同关系的研究方法：分数维协整和方差协同持续，并介绍了各自的建模方法。最后，研究了文献中尚属空白的VaR的波动持续性问题，提出了FITSGARCH模型并进行了VaR波动持续性的讨论。     

基于EWMA-VaR的企业整体现金流量预测模型

在指数平滑法和VaR方法基础上，以预测企业现金流量为目标，以现金流量波动为约束条件，建立基于EWMA-VaR的企业整体现金流量预测模型。本模型的特点一是对企业整体现金流量逐年进行预测。得出预测现金流量的均值，提高现金流量的预测精度。二是建立了企业整体现金流量风险约束条件，为企业财务风险规避提供依据。三是建立整体现金流量预测区间，使企业更好地做出财务决策。     

碳市场与大类资产之间的波动信息传导

本文研究了碳排放权价格与大类资产价格之间波动风险和信息的传导。运用ARMA（1,1）-CGARCH模型,构建了价格时间序列的长、短期波动性作为风险和信息的度量,并根据格兰杰因果检验的遍历性,判断条件风险和极端风险下的欧盟碳排放权配额（EUA）期货当日价格与19个金融、能源和大类资产当日价格之间的溢出关系,并以深圳碳现货价格为例,测算国内碳市场和其他市场的波动信息传导,数据来源为WIND数据库。研究结果表明：①欧盟碳期货市场与金融、能源等大类资产市场存在波动风险和信息的传导,其中与金融市场的关系略强;②这个传导关系是动态变化的,在欧盟碳市场发展的三个阶段表现不同;③极端风险和条件风险下波动信息传导表现不同;④中国碳市场和大类资产价格之间基本上不存在波动风险和信息的传导。研究结果对未来中国碳市场建设提供了风险管理方面的启示,尤其在极端风险下应该加大重视。此外,有些指标同时具有溢入和溢出的因果关系,其他市场的参与者应该充分重视来自碳市场的风险冲击,前瞻性的采取风险控制和风险规避。