The Role of Local Agglomeration Economies and Regional Characteristics in Attracting FDI: Italian Evidence

The objective of this paper is to test for the importance of local agglomeration externalities in determining inward foreign direct investment (FDI) intensity, viewed as a measure of regional attractiveness to FDI. The links between the degree of FDI inflow penetration into Italy and its determinants at the regional level are examined using alternative fixed effects panel data model specifications, also extended to include spatial effects. It is found that sectoral and regional specificities are relevant in attracting inward FDI into Italy and the different effects of intra‐sector and inter‐sector spillovers can be distinguished. There is also evidence that the importance of agglomeration spillovers connected to the geographical distance varies across regions, indicating both substitution and complementarity effects from FDI inflows into neighboring regions.     

Industry co-movement and cross-listing: Do home country factors matter?

This paper investigates the co-movement of American depositary receipts (ADRs) and the industry returns of home and U.S. markets with a focus on industry co-movement by applying time-varying and constant copulas model specifications. We also examine the impacts of country-specific factors on the industry co-movement in cross-listing issue. Co-movements are found in relation to the industry returns and economic asymmetry. The industry co-movement of ADR is higher with home country than that with the U.S., supporting the fundamental view. The results pertaining to the ADR-home pairs are as follows: (1) during economic recessions, the factors of influencing industry co-movement include import and the number of listed companies; (2) in contrast, during economic booms, industry co-movement is significantly influenced by energy import, export, import, the number of listed companies, and high-tech export. The factors that impact the industry co-movement of ADR-US contain foreign direct investment, reserve, cash surplus, energy import, and the number of listed companies.     

时变Copula模型的非参数推断

运用Copula模型研究金融变量之间的相关结构,是近年来金融分析中的一个热点,如何估计Copula模型中的时变参数则是一个重点和难点问题。本文从非参数建模思想为切入点,提出经验分布函数—局部极大似然法(ECDF-LML)估计Copula函数中的时变参数,研究了Copula模型参数是否时变的统计假设检验问题。最后通过大量随机模拟研究验证了本文所提出的方法较DCC-MGARCH方法在刻画随机变量动态相关性方面更具优越性且很稳健。     

基于结构突变和截面相关的面板协整检验

本文基于Westerlund和Edgerton（2008）,考虑了无时间趋势和有时间趋势的面板协整检验。在检验协整时,本文不仅允许误差项存在异方差、序列相关以及截面相关,而且还允许各截面在截距和协整斜率上存在未知时点的多个突变点。蒙特卡洛模拟结果表明,（1）该检验的具有较小的水平扭曲和较高的检验势,（2）将模型拓展到不含有趋势项的情形是必要的。在此基础上,使用基于动态最小二乘估计量的新统计量对国际CO2排放和经济增长关系进行检验,发现在考虑了突变和截面相关的情形下,两者间的长期均衡关系确实存在。     

中国银行业系统性风险监测研究——基于SCCA技术的实现与优化

金融危机以来,随着国际金融监管组织、各国监管当局和学术界加强系统性风险监测技术的开发和应用,新方法、新技术不断涌现,SCCA是其中具有代表性的方法之一。本文结合我国银行业实际情况,设计了针对SCCA技术关键环节的优化算法,并采用非参数统计方法估计时变相依函数,提出了新的系统性风险监测指标J-VaR。在此基础上,本文动态监测了后危机时代我国银行业系统性风险的演变过程。研究表明,优化后的SCCA技术具有较好的适用性,时变风险相依结构对系统性风险理论和实证研究至关重要。     

新加坡中央公积金制度变迁因素分析

新加坡中央公积金制度属于强制积累型的社会保障制度。中央公积金制度的变迁是新加坡经济、政治、文化和社会因素综合作用的结果。新加坡中央公积金制度变迁的经验表明社会保障制度安排必须要与本国的国情相适应,要重视家庭在社会保障中的作用,要兼顾一些其他目标。     

Lock-in situations in supply chains: A social exchange theoretic study of sourcing arrangements in buyer–supplier relationships

Social exchange theory is used to gain a better understanding of the relationship between a buyer and a supplier that is characterized by lock-in situations. We begin by reviewing the theoretical foundations of social exchange theory. Next, we use an illustrative multinational business example from a Danish Business Group to demonstrate the complexities of the lock-in situation. Conjectures related to lock-in behaviors are initially developed and then examined by means of a game-theoretic model. The analysis provides a basis for the development of propositions which are examined employing a behavioral experiment. The results indicate that the optimal pricing strategy of the supplier is to lower the price with increasing demand and the optimal investment intensity of the buyer decreases with increasing demand. The paper concludes by presenting directions for future research.     

Co-dependence of extreme events in high frequency FX returns

In this paper, we investigate extreme events in high frequency, multivariate FX returns within a purposely built framework. We generalize univariate tests and concepts to multidimensional settings and employ these novel techniques for parametric and nonparametric analysis. In particular, we investigate and quantify the co-dependence of cross-sectional and intertemporal extreme events. We find evidence of the cubic law of extreme returns, their increasing and asymmetric dependence and of the scaling property of extreme risk in joint symmetric tails.     

Smooth nonparametric Bernstein vine copulas

We consider the problem of accurately modelling the distribution of the market risk of a multivariate financial portfolio. We employ a multivariate GARCH model in which the dependence structure between the assets is modelled via a vine copula. We address the problem of how the parametric pair-copulas in a vine copula should be chosen by proposing to use nonparametric Bernstein copulas as bivariate pair-copulas. An extensive simulation study illustrates that our smooth nonparametric vine copula model is able to match the results of a competing parametric vine model calibrated via Akaike’s Information Criterion while at the same time significantly reducing model risk. Our empirical analysis of financial market data demonstrates that our proposed model yields Value-at-Risk forecasts that are significantly more accurate than those of a benchmark parametric model.     

Robust portfolio optimization

We address the problem of estimating risk-minimizing portfolios from a sample of historical returns, when the underlying distribution that generates returns exhibits departures from the standard Gaussian assumption. Specifically, we examine how the underlying estimation problem is influenced by marginal heavy tails, as modeled by the univariate Student-t distribution, and multivariate tail-dependence, as modeled by the copula of a multivariate Student-t distribution. We show that when such departures from normality are present, robust alternatives to the classical variance portfolio estimator have lower risk.