International stock markets interactions and conditional correlations

This paper investigates the transmission of price and volatility spillovers across the US and European stock markets in bivariate combinations. The framework used encompasses the most popular multivariate GARCH models, with News Impact Surfaces employed for interpretation. By using synchronous data the dynamic conditional correlation model (Engle, R., 2002. Dynamic conditional correlation: a simple class of multivariate GARCH models. Journal of Business and Economic Statistics 20, 339–350) is found to best capture the relationships for over half of the bivariate combinations of markets. Other findings include volatility spillovers from the US to European markets, and a reverse spillover. In addition, the magnitude of the correlation between markets is higher not only for negative shocks in both markets, but also when a combination of shocks of opposite signs occurs.     

次贷危机下美国和全球股市之联动

本文选用NADCC-EGARCH模型研究了次贷危机影响下全球主要股票市场的反应,并捕捉到欧亚五个股市同美国股市联动关系的变化过程。实证显示,由美国次贷危机引发的金融危机造成了全球股票市场不同程度的动荡,出现熊市迹象。美国同全球股市间的联动关系在危机扩散后存在结构性变化,且欧洲、日本等成熟市场与美国市场的联动关系受美国股市负面消息的影响较大。中国虽受到一定程度的冲击,但是国内股票市场结构相对稳定。     

International linkage of the Russian market and the Russian financial crisis: A multivariate GARCH analysis

This study considers the linkage of the Russian equity market to the world market, examining the international transmission of the Russia's 1998 financial crisis utilizing the GARCH–BEKK model proposed by Engle and Kroner [Engle, R.F., Kroner, K.F., 1995. Multivariate simultaneous generalized ARCH. Economet. Theor. 11, 122–150]. We find evidence of direct linkage between the Russian equity market with regards to returns and volatility, while the weakness of the linkage suggests that the Russian equity market was only partially integrated into the world market. At the time of the crisis, evidence of contagion is clear.     

Does the Probability of Informed Trading Model Fit Empirical Data?

The probability of informed trading (PIN) is used widely as a measure of information asymmetry. Relatively little work has appeared on how well PIN models fit empirical trade data. We reveal structural limitations in PIN models by examining their marginal distributions and dependence structures represented by copulas. We develop a distribution-free test of the goodness-of-fit of PIN models. Our results indicate that estimated PIN models have generally poor fit to actual trade data. These results suggest that researchers should be cautious when PIN estimates are plugged into empirical models as explanatory variables.     

Adoption of erosion management practices in New Zealand

Soil erosion is a serious environmental threat to New Zealand’s agricultural sector. Economic costs of soil erosion are significant and the costs of adopting mitigation and management practices, given the targets set by environmetal policy, do not spread uniformly across space, economic activities and types of erosion processes. Management practices have been widely employed by farmers and promoted by several policy programs. Practices are not mutually exclusive and could be jointly adopted by comparing productivity gains against costs of implementation. However, research on the identification of the drivers of adoption is scarce in New Zealand. To identify the determinants of adoption of management practices in New Zealand farms, we combine novel survey information with data on climate and erosion in a multivariate probit framework. This framework allows identification of potential complementarity or substitution between management practices. We find significant and heterogeneous effects from erosion levels, temperature, wind velocity and primary land uses on the adoption of any of the practices. In addition, we also find significant complementarities between management practices. The results of this study are important because the complementarities relate to external effects of erosion mitigation which can help to promote public acceptability of mitigation policies.     

Freight distribution in megacities: Perspectives of shippers,logistics service providers and carriers

We analyse the opinions of shippers, logistic service providers (LSPs) and carriers related to regulations and issues faced by these companies regarding freight distribution in megacities, and the logistical performance measures likely affected by those regulations and issues. We present a review of the freight distribution literature focusing on a large number of freight distribution aspects, such as regulatory, collaborative, environmental, logistical and risk. We also investigate some logistical performance indicators adopted by the companies. Subsequently, we conduct a survey with 147 companies working in the São Paulo Metropolitan Region (SPMR). We use multivariate analysis of variance to assess the logistical performance indicators and non-linear canonical correlation analysis to identify the most relevant freight distribution attributes. The results that the majority of carriers are located inside the SPMR and efficiently handle these issues better than others actors. The lack of collaboration, cargo theft, traffic congestion and some regulations affect the LSP’s logistical performance. Moreover, the actors perceive regulatory aspects, mainly traffic congestion, and a lack of security for deliveries in unsafe areas as the significant issues for deliveries in megacities.     

Economic evaluation of asymmetric and price range information in gold and general financial markets

Gold is widely perceived as a good diversification or safe haven tool for general financial markets, especially in market turmoil. To fully understand the potential, this study constructs an asymmetric multivariate range-based volatility model to investigate the dependence and volatility structures of gold, stock, and bond markets and further to compare the difference between the financial crisis and post-financial crisis periods. We find a striking explanatory ability to volatility structures provided by the price range information and significant evidence of asymmetric dependence across gold, stock, and bond markets. We implement an asset-allocation strategy incorporating asymmetric dependence and price range information to explore their economic importance. The out-of-sample results show that between 35 and 517 basis points and between 90 and 1111 basis points are earned annually when acknowledging asymmetric dependence and price range information, respectively. These economic benefits are inversely related to the level of investors’ risk aversion and are particularly significant in the period of the global financial crisis.     

Copula Gaussian graphical models with penalized ascent Monte Carlo EM algorithm

Typical data that arise from surveys, experiments, and observational studies include continuous and discrete variables. In this article, we study the interdependence among a mixed (continuous, count, ordered categorical, and binary) set of variables via graphical models. We propose an ?₁‐penalized extended rank likelihood with an ascent Monte Carlo expectation maximization approach for the copula Gaussian graphical models and establish near conditional independence relations and zero elements of a precision matrix. In particular, we focus on high‐dimensional inference where the number of observations are in the same order or less than the number of variables under consideration. To illustrate how to infer networks for mixed variables through conditional independence, we consider two datasets: one in the area of sports and the other concerning breast cancer.     

A semiparametric graphical modelling approach for large-scale equity selection

We propose a new stock selection strategy that exploits rebalancing returns and improves portfolio performance. To effectively harvest rebalancing gains, we apply ideas from elliptical-copula graphical modelling and stability inference to select stocks that are as independent as possible. The proposed elliptical-copula graphical model has a latent Gaussian representation; its structure can be effectively inferred using the regularized rank-based estimators. The resulting algorithm is computationally efficient and scales to large data-sets. To show the efficacy of the proposed method, we apply it to conduct equity selection based on a 16-year health care stock data-set and a large 34-year stock data-set. Empirical tests show that the proposed method is superior to alternative strategies including a principal component analysis-based approach and the classical Markowitz strategy based on the traditional buy-and-hold assumption.     

Internal and external spillover effects for the BRIC countries: Multivariate GARCH-in-mean approach

This paper examines mean-to-mean, volatility-to-mean and volatility-to-volatility spillover effects for the stock markets of BRIC countries. External and internal spillovers of returns and volatilities are estimated using 4-dimensional BEKK-GARCH-in-mean model. The model also includes the returns of stock markets in the USA, Germany, Japan and the MSCI Emerging market index, as well as time-return interaction terms which allow taking into account the dynamics of their influence on BRIC stock markets during pre-crisis, crisis and recovery time periods. Some evidence for the famous ‘decoupling’ phenomenon is found. The research contributes to the literature on spillover effects by using multivariate GARCH models.