三阶随机占优准则在证券选择中的应用

通常可以用分布函数和分位数函数描述随机占优准则,Man-ChungNg列举的两个例子说明在三阶随机占优条件下用两种方法得到的结论是不一致的,这与Levy的观点相反.该文分别将这两种方法描述的三阶随机占优准则用于上海证券市场的基金选择,发现用两种方法在应用中得到的结论并不总是一致的.由此验证用Levy提出的分位数方法描述的随机占优准则进行实证研究是不正确的,一阶和二阶条件除外.     

考虑前三阶模态时悬索桥的车过载荷响应

本文通过Galerkin方法对经典的悬索桥垂向弯曲振动方程进行离散,得到了包含三个二阶方程的常微分组。通过该常微分方程组的解析解,研究了车过载荷作用下结构的动力学行为,并和有限元计算结果进行对比。研究结果显示,Galerkin方法得到的一阶、二阶振动频率和有限元结果一致,但三阶模态的振动频率比有限元的结果大;车过载荷作用下,三阶模态截断得到的解析解在整体运动趋势和有限元计算结果一致。因此,要较精确的描述悬索桥的动力学行为,需要考虑二阶和三阶模态。     

中国工业部门生产函数的参数估计

生产函数表示投入与产出关系的特定技术关系,用于说明一组生产要素投入所能得到的最大产出量。在每一种既定的技术条件下,都存在着一个生产函数。 一、函数选择 函数选择是参数估计的前提。在对生产函数进行估计之前,必须作出几项选择。 第一个选择我们需要确定,究竟是选择基本的生产函数还是对偶的成本函数。给定总成本的定义和生产者均衡的必要条件,就可以把总成本表示为所有投入价格和产出水平的函数,即成本函数、成本函数是生产函数的对偶,也是对于生产单位技术的等价描述。正是因为在描述生产技术方面这两种函数不分高低,所以…     

基于模糊失效准则的混凝土箱梁桥可靠度分析

随着模糊数学的广泛应用,研究人员认识到结构的失效标准具有谱的性质,在谱的高值发生高度破坏,在谱的低值发生轻度破坏。因而结构的安全状态不能再用简单的安全与失效两种状态来描述,而是用一系列的中间状态来描述更符合结构的实际情况。结构的可靠度不仅与确定影响因素和不确定影响因素有关,而且与破坏准则的模糊不确定性有关。引入模糊数学理论,建立了预应力混凝土箱梁桥模糊失效准则的隶属函数;基于模糊失效准则,建立了开裂预应力混凝土桥的可靠度计算模型;并以HS大桥为例进行了实例分析。     

项目投资评价的两种盈亏平衡点比较分析

净现值准则是最重要的项目投资财务评价方法,当其他准则与其得出冲突的结果时,应以净现值准则为准。然而,在对项目进行盈亏平衡分析时仅仅计算会计利润盈亏平衡点,因而它与评估项目的准则不一致。鉴于此,笔者认为有必要计算现值盈亏平衡点。本文通过两种不同盈亏平衡点的计算和比较,进一步得出现值盈亏平衡点更具有优越性的结论。     

中国农民收入分配趋势分析——基于随机占优分析

本文介绍了随机占优的概念及其在收入分配问题研究中的应用，给出了随机占优关系的检验方法并运用这一方法考察了10年来中国农民收入分布情况的变动，发现农民的平均收入水平和较高收入组的农民的比重都得到了提高，并且收入水平更加趋近而不是发散。     

项目投资评价的两种盈亏平衡点比较分析

净现值准则是最重要的项目投资财务评价方法,当其他准则与其得出冲突的结果时,应以净现值准则为准.然而,在对项目进行盈亏平衡分析时仅仅计算会计利润盈亏平衡点,因而它与评估项目的准则不一致.鉴于此,笔者认为有必要计算现值盈亏平衡点.本文通过两种不同盈亏平衡点的计算和比较,进一步得出现值盈亏平衡点更具有优越性的结论.     

资产组合选择问题的一致性研究

本文首先依据多准则决策原理,从理论上证明期望效用最大化的资产组合选择准则只是均值—风险资产组合选择准则的一个特例。然后根据期望效用原理、随机占优原理,分析使均值—风险资产组合选择准则与期望效用最大化资产组合选择准则具有内在一致性时,风险度量方法和收益度量方法应该满足的必要条件。     

经济效益的两种计算方法探讨

净现值法和收入/支出比法是经济效益的两种常用计算方法。本文阐述了如何运用这两种方法进行技术经济分析。并对应用这两种方法进行多方案选优结论一致的条件进行了探讨。     

模糊需求环境下的制造商零售商系统决策分析

在模糊需求环境下,分合作与非合作两种情况对制造商与零售商的决策互动与利润函数情况进行建模,用重心法和Yager法两种方法进行分析,结合数据示例对模型结果进行比较,得出一定结论。     

Nonparametric estimation of distributional policy effects

This paper proposes a fully nonparametric procedure to evaluate the effect of a counterfactual change in the distribution of some covariates on the unconditional distribution of an outcome variable of interest. In contrast to other methods, we do not restrict attention to the effect on the mean. In particular, our method can be used to conduct inference on the change of the distribution function as a whole, its moments and quantiles, inequality measures such as the Lorenz curve or Gini coefficient, and to test for stochastic dominance. The practical applicability of our procedure is illustrated via a simulation study and an empirical example.     

A REVIEW OF STOCHASTIC DOMINANCE METHODS FOR POVERTY ANALYSIS

Stochastic dominance techniques have been mainly employed in poverty analyses to overcome what it is called the multiplicity of poverty indices problem. Moreover, in the multidimensional context, stochastic dominance techniques capture the possible relationships between the dimensions of poverty as they rely upon their joint distribution, unlike most multidimensional poverty indices, which are only based on marginal distributions. In this paper, we first review the general definition of unidimensional stochastic dominance and its relationship with poverty orderings. Then we focus on the conditions of multivariate stochastic dominance and their relationship with multidimensional poverty orderings, highlighting the additional difficulties that the multivariate setting involves. In both cases, we focus our discussion on first‐ and second‐order dominance, though some guidelines on higher order dominance are also mentioned. We also present an overview of some relevant empirical applications of these methods that can be found in the literature in both univariate and multivariate contexts.     

Variance-Gamma and Normal-Inverse Gaussian models: Goodness-of-fit to Chinese high-frequency index returns

In this study Variance-Gamma (VG) and Normal-Inverse Gaussian (NIG) distributions are compared with the benchmark of generalized hyperbolic distribution in terms of their fit to the empirical distribution of high-frequency stock market index returns in China. First, we estimate the considered models in a Markov regime switching framework for the identification of different volatility regimes. Second, the goodness-of-fit results are compared at different time scales of log-returns. Third, the goodness-of-fit results are validated through bootstrapping experiments. Our results show that as the time scale of log-returns decrease NIG model outperforms the VG model consistently and the difference between the goodness-of-fit statistics increase. For high-frequency Chinese index returns, NIG model is more robust and provides a better fit to the empirical distributions of returns at different time scales.     

Quantile models and estimators for data analysis

Quantile regression is used to estimate the cross sectional relationship between high school characteristics and student achievement as measured by ACT scores. The importance of school characteristics on student achievement has been traditionally framed in terms of the effect on the expected value. With quantile regression the impact of school characteristics is allowed to be different at the mean and quantiles of the conditional distribution. Like robust estimation, the quantile approach detects relationships missed by traditional data analysis. Robust estimates detect the influence of the bulk of the data, whereas quantile estimates detect the influence of co-variates on alternate parts of the conditional distribution. Since our design consists of multiple responses (individual student ACT scores) at fixed explanatory variables (school characteristics) the quantile model can be estimated by the usual regression quantiles, but additionally by a regression on the empirical quantile at each school. This is similar to least squares where the estimate based on the entire data is identical to weighted least squares on the school averages. Unlike least squares however, the regression through the quantiles produces a different estimate than the regression quantiles.     

Does transaction activity predict Bitcoin returns? Evidence from quantile-on-quantile analysis

This paper uses the quantile-on-quantile regression to examine the predictive power of transaction activity for Bitcoin returns over the period from January 2013 to December 2018. We measure the Bitcoin transaction activity using trading volumes, the number of unique Bitcoin transactions, and the number of unique Bitcoin addresses. Considering the onset of structural breaks, we identify considerable effects of the heterogeneity concerning the quantiles of transaction activity, which cannot be depicted fully by the traditional quantile regression method. The empirical results show that higher transaction activity tends to predict higher/lower Bitcoin returns when the market is in a bullish/bearish state. We find that the nexus is asymmetric across quantiles, depending on the sign and size of the transaction activity, and the predictive relationship intensifies in the upper or lower quantiles of the conditional distribution. In addition, this empirical evidence is in line with the volume-return association in the equity market due to private informative and noninformative trading actions. Overall, our findings suggest that transaction activity-based strategies should be made with respect to Bitcoin market performance, specifically during extreme conditions.     

Oil price shocks,geopolitical risks,and green bond market dynamics

This research explores the causal relation among oil price, geopolitical risks, and green bond index in the United States from December 2013 to January 2019. Unlike the conventional linear model specification used in earlier works, we evaluate causal relations based on Granger-causality in quantile analysis. Our empirical results reveal unidirectional Granger-causality from geopolitical risk to oil price at the extreme quantiles. We also observe a significant bi-directional causality from oil price to green bond index for the lower quantiles. Findings also reveal causality from geopolitical risk to green bond index in the lower quantiles of the distribution. Therefore, knowledge of these causal relationships can help policy makers to evaluate and implement effective policies to prevent sudden and substantial oil price shocks and geopolitical risk.     

Making inferences about the polarization,welfare and poverty of nations: a study of 101 countries 1970–1995

Stochastic Dominance techniques are adapted and employed to study the extent and progress of Polarization, Welfare and Poverty of 101 nations over the period 1970–1995. The adaptations provide methods of comparing mass relocation by evaluating various degrees of right and left separation between distributions. The results reveal that, whilst welfare increased and then diminished and poverty diminished and then increased, polarization between rich and poor countries continued unabated throughout the period emphasizing the distinction between polarization and inequality. Copyright © 2004 John Wiley & Sons, Ltd.     

Empirical prediction intervals revisited

Empirical prediction intervals are constructed based on the distribution of previous out-of-sample forecast errors. Given historical data, a sample of such forecast errors is generated by successively applying a chosen point forecasting model to a sequence of fixed windows of past observations and recording the associated deviations of the model predictions from the actual observations out-of-sample. The suitable quantiles of the distribution of these forecast errors are then used along with the point forecast made by the selected model to construct an empirical prediction interval. This paper re-examines the properties of the empirical prediction interval. Specifically, we provide conditions for its asymptotic validity, evaluate its small sample performance and discuss its limitations.     

Validating multiple structural change models–a case study

In a recent article, Bai and Perron ( 2003 , Journal of Applied Econometrics) present a comprehensive discussion of computational aspects of multiple structural change models along with several empirical examples. Here, we report on the results of a replication study using the R statistical software package. We are able to verify most of their findings; however, some confidence intervals associated with breakpoints cannot be reproduced. These confidence intervals require computation of the quantiles of a nonstandard distribution, the distribution of the argmax functional of a certain stochastic process. Interestingly, the difficulties appear to be due to numerical problems in GAUSS, the software package used by Bai and Perron. Copyright © 2005 John Wiley & Sons, Ltd.     

Relocation of second degree: Moving towards a new place or returning home?

We study the “Relocations of Second Degree” (RSDs), i.e., the location decisions that modify the country of destination of a previous offshoring investment. Specifically, we distinguish between two types of RSDs, i.e., “Relocation to the Home Country (RHC)”, also known as back-reshoring, and “Relocation to a Third Country (RTC)”, i.e., the choice to move to a second host country.Specifically, we explore how the location advantages underlying the previous offshoring decision affect the probability to undertake an RHC, rather than an RTC. Location advantages reflect the favourable conditions that a foreign country offers with respect to the home one, in terms of market-seeking, asset-seeking and efficiency-seeking (i.e., cost-saving and productivity-enhancing) opportunities. Using data from the European Restructuring Monitor, we focus on the RSDs regarding manufacturing activities, implemented across European countries between 2002 and 2015. We find that, on the one hand, when a previous offshoring investment is driven by market-seeking location advantage, firms undertaking the RSD are more likely to opt for an RHC, except during the economic crisis where market-seeking European firms seem to prefer RTCs. On the other hand, RTC is a preferred choice when the location advantage is of efficiency-seeking type. In addition to offering a broader characterization of RSDs, our study provides empirical evidence of the relationship between the offshoring and relocation decisions. Managers should be aware of this connection when designing their manufacturing internationalization strategies.