Does the stock market value the inclusion in a sustainability stock index? An event study analysis for German firms

This paper empirically analyzes the effect of the inclusion of German corporations in the Dow Jones STOXX Sustainability Index (DJSI STOXX) and the Dow Jones Sustainability World Index (DJSI World) on stock performance. In order to receive robust estimation results, we apply an (short-term) event study approach that is based on both a modern asset pricing model, namely the three-factor model according to Fama and French [24], and additionally a t-GARCH(1,1) model. Our empirical results suggest that stock markets may penalize the inclusion of a firm in sustainability stock indexes. This finding is mainly driven by a strongly negative effect of the inclusion in the DJSI World. In contrast, we do not find significant average cumulative abnormal returns for the inclusion in the DJSI STOXX. This suggests that the inclusion in a more visible sustainability stock index may have larger negative impacts.     

OPTIMIZING FLOOD CONTROL ALLOCATION FOR A MULTIPURPOSE RESERVOIR1

ABSTRACT. In the last decade much research has been devoted to applying the systems analysis approach to water resources problems. A popular research goal has been determination of the “best” method of operating a multipurpose reservoir. The goal of this study was to derive the economically optimum flood control diagram for a multipurpose reservoir by systems analysis. The technique employed to optimize the flood control diagram was programmed so that the optimization process could be applied to other multipurpose reservoirs. Two computer programs developed at the U.S. Army Corps of Engineers' Hydrologic Engineering Center were utilized with modifications to simulate the operation of Folsom Reservoir in central California. Economic analyses were incorporated along with an optimization technique into the reservoir operations program; and the resultant program was capable of routing a sequence of monthly reservoir inflows, computing benefits for various flood control diagrams (as dictated by the optimization procedure), and selecting the economically optimum flood control diagram. The univariate gradient technique was the optimization procedure employed. The two computer programs are on file at the Hydrologic Engineering Center in Davis, California.     

Short-term wind speed forecasting: Application of linear and non-linear time series models

This study forecasts day-ahead wind speed at 15 minute intervals at the site of a wind turbine located in Maharashtra, India. Wind speed exhibits non-stationarity, seasonality and time-varying volatility clustering. Univariate linear and non-linear time series techniques namely MSARIMA, MSARIMA-GARCH and MSARIMA-EGARCH have been employed for forecasting wind speed using data span ranging from 3 days to 15 days. Study suggests that mean absolute percentage error (MAPE) values first decrease with the increase in data span, reaches its minima and then start increasing. All models provide superior forecasting performances with 5 days data span. It is further evident that ARIMA-GARCH model generates lowest MAPE with 5 days data span. All these models provide superior forecasts with respect to current industry practices. This study establishes that employing various linear and non-linear time series techniques for forecasting day-ahead wind speed can benefit the industry in terms of better operational management of wind turbines and better integration of wind energy into the power system, which have huge financial implications for wind power generators in India.     

X~2分布、t分布和F分布的近似计算

当自由度很大时,三大抽样分布的P值无表可查,而且如果编程计算P值时,需要计算一个无穷区间上的广义积分,还会面临数值溢出的问题,解决这些问题需要较高的计算机编程技巧,这些不利于开发实用程序,限制了它们的实际应用。本文利用χ^2分布、一元t分布和一元F分布的一致渐进正态性、标准正态分布的分布函数近似计算公式和软件Mathematica 5．0,通过计算机仿真,指出当自由度较大时,可以用正态分布进行近似计算,避免了数值溢出和无穷区间的积分问题,为使用常用开发语言编程解决实际问题提供了一种可行方案,具有一定的应用价值。     

规范变换降维与误差修正结合的环境系统的一元线性回归预测

针对高维、非线性环境系统的传统预测模型存在结构复杂、收敛速度慢、求解精度低的局限,提出对环境系统预测量及其影响因子进行幂函数与对数函数相结合的规范变换.此规范变换能使变换后的各影响因子皆等效于一个线性化的规范因子,从而将多因子、非线性的预测建模简化为简单的一个"等效"规范因子的一元线性回归建模;并对预测样本的模型输出进行误差修正,以提高样本的预测精度.在规范变换的基础上,由有m个规范因子的每个建模样本生成一个规范因子的m个"等效"训练样本,n个建模样本共生成N=m×n个训练样本.应用最小二乘法,建立基于规范变换的一元线性回归预测模型.将基于规范变换的一元线性回归预测模型与相似样本误差修正法相结合,分别用于某市5个点位的SO_2浓度预测和南昌市城市降水酸度pH值预测及某河段COD_(Mn)预测,并与多种传统预测模型和方法及基于规范变换与误差修正的3种智能预测模型的预测结果进行了比较.结果表明:该预测模型用于3个实例预测的相对误差绝对值的平均值分别为1.14%、0.49%和1.45%;最大相对误差绝对值分别为2.22%、0.87%和1.85%,与基于规范变换与误差修正的3种智能预测模型的相应误差几乎没有差异,甚至还要小;均远小于多种传统预测模型和方法的相应误差,其预测精度甚至提高了一个数量级以上.基于规范变换与误差修正的一元线性回归预测模型简单、预测精度高、稳定性好,不存在"维数灾难",因而可广泛用于任意系统的预测建模.     

UNIVARLATE LEAST SQUARES MUSKINGUM FLOOD ROUTING1

ABSTRACT: The Muskingum method of hydrologic flood routing is based on a graphical bivariate curve fitting procedure. The subjectivity of this procedure may lead to problems of reproducibility and provides no guarantee of model coefficients which minimize the error of estimation. A transformation is developed to express the Muskingum method as a univariate transcendental function suitable for numeric solution. Solution of this function minimizes the sum of the squared deviations between estimated and measured rates of discharge. Five textbook examples of the Muskingum method are evaluated by the univariate transformation. Error reductions range from negligible to tenfold. While the univariate transformation may not improve upon graphically-based results in every case, in no instances are the univariate results inferior; in many cases they are superior.     

栖息地适宜度评价方法研究进展

栖息地模拟法是根据指示物种所需的物理生境条件确定河流流量,评价栖息地适宜度,为水生生物提供一个适宜的物理生境。与其他方法相比,栖息地模拟法考虑了生物本身对物理生境的要求,需要建立物种-生境评价指标。文章总结了目前用于描述物种—生境关系的栖息地适宜度评价方法,包括栖息地适宜度指数、多元统计方法、模糊逻辑方法、人工神经网络、对多物种和群落的统计分析,归纳了各个方法的进展和应用情况,重点分析了统计学方法在评估栖息地适宜度时的优势和不足。栖息地适宜度指数有二元、单变量、多变量3种格式,前2种方法只考虑单一因子,实际应用中多变量格式应用较多。多变量格式方法直观、所需数据容易获取、实际操作性强,是栖息地定量化的经典方法,也是目前应用最多的方法,然而其对专家经验依赖较多,主观性较强。近些年来,多元统计方法在栖息地适宜度评价方面的应用不断增加,它考虑物理变量之间的相互作用和相关性。模糊逻辑法在处理栖息地模拟中的不确定性方面具有优势,能更好地利用专家知识,更合理的处理建模过程中测量的不准确性和不确定性,同时也考虑了多个变量之间的相互作用,但是当考虑的变量数增加时,模糊规则的数量会迅速增加,给计算带来不便。人工神经网络能够隐性地找出响应变量和环境变量之间的复杂关系,但是其解释能力不足,并需要大量的实测数据对其进行训练,实际应用受到限制。通过排序分析或梯度分析可以对多物种和群落进行统计分析。这些方法都各有优劣,在实际应用中,应结合实际情况选择最合适的栖息地适宜度评价方法。