Demand-following or supply-leading? Trade openness and financial development in China

This study explores the causal relationship between financial development and trade openness in China, using the research methodology namely bootstrap Granger full-sample causality test and sub-sample rolling-window estimation. The results reveal that there is a significant bidirectional relationship between financial development and trade openness in China, pointing out the existence of both ‘demand-following’ and ‘supply-leading’ hypotheses. Furthermore, their relationship fits well the fact that China has experienced economic restructuring and structural changes in the past few decades. As financial development improves trade openness robustly, the Chinese policymakers should further increase financial reform to promote trade development.     

Global and Extreme Dependence Between Investor Sentiment and Stock Returns in European Markets

The authors investigate the global and extreme dependence structure between investor sentiment and stock returns in 7 European stock markets (Belgium, France, Germany, Greece, the Netherlands, Portugal, and the UK), over the period 1985–2015. Global dependence refers to the correlation of changes in sentiment and stock returns over the whole range of these 2 variables, and extreme dependence refers to the local correlation of high (i.e. asymptotic) changes in sentiment and high stock returns. Using copula models and a bootstrap procedure, 6 statistical tests are performed for this purpose. Among the results of the tests, the authors highlight those that provide evidence of contemporaneous lower extreme dependence and contemporaneous upper extreme independence between sentiment and returns. As policy implications, these results suggest that financial stability can be promoted if regulators consider the impact of their decisions on investor sentiment. Also, the results seem to support the arguments in favor of short selling ban during turmoil periods. Finally, overall, the results are relevant for both investors and regulators and reinforce the importance of considering investor sentiment to better understand the behavior of financial markets.     

“金砖四国”股票市场间相依结构分析

金融市场间的相依关系及其结构分析是金融风险测度、资产组合管理等金融理论和实务中的一个重要问题,而基于线性相关系数是难以正确刻画非线性条件下金融资产间的相关结构,特别是尾相依关系。为研究"金砖四国"新兴股票市场间的相依结构,文章构建了一个混合Copula模型,对"金砖四国"股票市场间的相依结构进行建模分析,并将结果与单一阿基米德Copula模型进行了比较,表明混合Copula模型既能保留单一Copula模型的特性,更能灵活、全面地刻画变量间的相依关系。实证研究的结果表明:在样本数据期间,"金砖四国"股票市场间存在非对称相关关系,相依结构上存在差异;中国上证综指波动较为剧烈,与其它三国指数间以下尾相依为主;巴西、印度、俄罗斯三国股指间的相依结构相似,联动相关性相近,并无显著的下尾相依关系。     

Dynamics of sectoral business cycle comovement

Sectoral comovement accounts for a considerable share of the variance of aggregate variables. However, little is known about its time-varying aspects by now. In this article, a multivariate DCC- GARCH framework is employed to study dynamics of sectoral comovement across manufacturing sectors both in the United States and in Germany. To account for possible nonlinearities, asymmetric effects in conditional volatilities as well as in conditional correlations are being assessed. We find that comovement across sectors is not stable but shows irregular movements. Particularly, contractions tend to be more synchronized than expansions in manufacturing sector. Moreover, we examine the role of various aggregate factors for the fluctuations in conditional correlations. Our findings reveal that both the non-constant variability of common factors and the changes in the effects of these factors play role for the fluctuations in sectoral comovement.     

宏观经济与股票价格波动之间的关系研究

利用Copula模型,研究宏观经济变量与上证股指收益率之间的相关关系,在选择合适的边缘分布函数的基础上,分别建立了常相关的二元正态Copula函数、t—Copula函数、FrankCopula函数、ClaytonCopula函数以及Gum—belCopula函数模型,并且利用欧氏距离方法选择出最佳拟合Copula模型。选取2001年1月至2011年12月的月度数据作为处理对象,并利用最佳拟合模型分析宏观经济变量与上证股指收益率间相关关系及相关结构,从而揭示了中国宏观经济与股票市场之间的相关性。     

Time-varying correlations in oil,gas and CO2 prices: an application using BEKK,CCC and DCC-MGARCH models

Previous literature has identified oil and gas prices as being the main drivers of CO₂ prices in a univariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) econometric framework (Alberola et al., 2008; Oberndorfer, 2009). By contrast, we argue in this article that the interrelationships between energy and emissions markets shall be modelled in a Vector Autoregressive (VAR) and Multivariate GARCH (MGARCH) framework, so as to reflect the dynamics of the correlations between the oil, gas and CO₂ variables overtime. Using the Baba–Engle–Kraft–Kroner (BEKK), Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation MGARCH (DCC-MGARCH) models on daily data from April 2005 to December 2008, we highlight significant own-volatility, cross-volatility spillovers, and own persistent volatility effects for nearly all markets, indicating the presence of strong Autoregressive Conditional Heteroscedasticity (ARCH) and GARCH effects. Besides, we provide strong empirical evidence of time-varying correlations in the range of [?0.3;?0.3] between oil and gas, [?0.05;?0.05] between oil and CO₂, and [?0.2;?0.2] between gas and CO₂, that have not been considered by previous studies. These findings are of interest for traders and utilities in the energy sector, but also for a broader applied economics audience.     

基于关联交易的企业集团信用风险研究

基于合作博弈理论构建了企业集团关联交易基本模型,刻画了企业集团的公允关联交易以及两种广泛存在的、典型的非公允关联交易行为。利用一类Survival Copula函数刻画了不同类型关联交易下的企业集团信用风险,从理论上阐明了关联交易影响企业集团信用风险的基本机制。研究结果表明:通过关联交易"掏空"母公司的行为会使企业集团的信用风险显著增加;存在最优内部资源配置方式可使企业集团的信用风险水平最低。     

人民币外汇市场间不对称汇率变动的实证研究

现有关于人民币汇率各市场间关系的研究一般是基于多元GARCH模型,探讨各市场间的线性相关关系,未能考虑各市场间汇率变动可能存在的"不对称效应":面临正(反)向的较大冲击时,各市场汇率变动表现出同步性;而面临反(正)向的较大冲击时,各市场汇率变动不同步。本文运用SJC-Copula-MGARCH模型对人民币汇率境内SPOT市场、境内DF市场和境外NDF市场之间的相依关系进行实证分析,发现境内汇率市场(SPOT市场和DF市场)和境外NDF市场间的联系仍较弱;SPOT-DF市场在面临大的正冲击和负冲击时均表现出较强的联动性,而SPOT-NDF市场和DF-NDF市场在面临大的冲击时汇率变动表现出"不对称效应":在面临大的正冲击(人民币相对贬值)时,境内汇率市场和境外NDF市场汇率变动不同步,当面临大的负冲击(人民币相对升值)时,境内汇率市场和境外NDF市场汇率变动表现出较强的同步性。本文进一步分析了上述"不对称效应"的经济机理,探讨了其经济学含义。     

中美主要金融市场相关结构及风险传导路径研究——基于Copula理论与方法

金融危机背景下的股市表现出更加复杂的动荡性,本文在传统GARCH模型的基础上引入了风险值对收益率的影响因素,运用GARCH-M模型来刻画股票收益率序列边缘分布,通过构建GARCH-M-t边缘分布过滤模型获取收益率残差序列,最后采用Copula函数对边缘分布拟合后的残差序列建模构建出Copula-GARCH-M-t相关结构模型。经过参数估计及多种Copula函数的拟合优度检验,最终成功刻画出中美金融市场五大证券交易中心股票收益率之间的相关结构模型。通过秩相关系数、尾部相关系数等相关性度量工具对中美两国金融市场的相关性进行分析,最后通过对不同股票市场之间的尾部相关性分析确定两国金融市场之间风险传导路径。     

Is exchange rate stability beneficial for stabilizing consumer prices in China?

This study examines the relationship between real effective exchange rates (REERs) and the consumer price index (CPI) in China, utilizing a bootstrap Granger full-sample causality test and a sub-sample rolling-window estimation. Considering structural changes, we assess the stability of the parameters and find that both the short-run and long-run relationships between the two estimated variables are unstable. This result suggests that full-sample causality tests cannot be relied upon. We instead employ a time-varying (bootstrap) rolling-window approach to revisit the dynamic causal relationship, and we find that the CPI is affected by the REER for several sub-samples due to the role of exchange rate pass-through (ERPT) under the managed floating exchange rate regime in China. These findings provide further proof of the impact of stable exchange rates on the maintenance of relatively steady price levels especially during the economic crisis and economic reform in China. The policy implication of these findings is that maintaining exchange rate stability is beneficial for controlling inflation during the economic crisis and economic reform.