Linear time-varying regression with a DCC-GARCH model for volatility

This article provides a new linear state space model with time-varying parameters for forecasting financial volatility. The volatility estimates obtained from the model by using the US stock market data almost exactly match the realized volatility. We further compare our model with traditional volatility models in the ex post volatility forecast evaluations. In particular, we use the superior predictive ability and the reality check for data snooping. Evidence can be found supporting that our simple but powerful regression model provides superior forecasts for volatility.     

基于变参数模型的城乡居民收入刺激机制研究--以山东省为例

文章根据山东省1978-2011年城乡居民收入、地区生产总值以及居民消费数据,采用变参数模型,分析经济增长和居民消费对城镇与农村居民收入的横向贡献度及其纵向贡献度差异。检验发现,居民消费对城镇与农村居民收入的刺激作用要更强,且其对农村居民收入的刺激强度较之城镇居民收入要更大,这对山东省制定提高居民收入政策具有积极的借鉴作用。     

公共事业指数与工业指数的相关性研究

本文根据阿基米德类Copula函数与Kendall's秩相关系数的关系,通过非参数估计法得到描述沪市行业指数中的公共事业指数与工业指数组合相关结构的最佳Copula函数形式,即用来描述牛市特征的Gumbel Copula,以及相应的尾部相关系数.尾部相关性分析结果表明,两指数收益率之间存在明显的非对称的尾部相关性,而且是上尾相关程度强于下尾相关程度,说明两指数牛市期间的相关性强于熊市期间的相关性.从规避风险角度分析,公共事业指数/工业指数组合是有效的指数组合.     

基于Copula函数的国际原油价格与股票市场收益的相关性研究

针对国际原油价格与金砖五国股票市场收益之间的相关性问题,使用 AR(p)-GARCH(1,1)-Copula 模型进行检验。运用广义误差分布(GED)获取收益残差序列,对 WTI 原油价格和金砖五国股市收益之间的相关性进行实证分析。研究结果表明,国际原油价格与中国股市收益呈现微弱的相关关系,而与其他四国股市收益的相关关系较为明显。用时变 SJC Copula 模型刻画国际原油价格与金砖五国股票市场收益的相关性最为合适。     

基于Copula-GARCH的金融市场时变相关性分析

进入21世纪全球各金融市场之间的联系日益紧密,一个国家的金融状况会不同程度的影响到其他的国家,尤其在金融危机爆发的境况下,各主要金融市场之间的相关性分析更是具有实践意义。由于Copula函数其自身的性质,在研究相关性分析上的优势,逐渐被应用到金融分析的模型当中。本文正是以二元正态Copula—GARCH（1,1）一t模型借助MATLAB分析工具箱,在金融危机的大背景下,对主要金融国家的金融市场波动情况的相关性分析。     

Directional time-varying partial correlation with the Gaussian copula–DCC–GARCH model

This article suggests a directional time-varying partial correlation based on the dynamic conditional correlation (DCC) method. A recent study proposed the copula DCC based on the vine structure. Due to the arbitrary variable selection, their method can produce unnecessary dependence in the multivariate structure, with extra economic and computational burdens. To overcome this limitation, we incorporate directional dependence by copula to track the causal relationship among multiple variables and then extend the copula bivariate DCC method to a directional time varying partial correlation in the multivariate structure. Our proposed method provides a reasonable and efficient conditional dependence structure, without the trial and error process. We offer an application of our method to the U.S. stock market as an illustrated example.     

Welfare costs of monetary policy uncertainty in the economy with shifting trend inflation

We study welfare costs of the uncertainty about monetary policy in the economy featuring shifting trend inflation. We follow Ruge-Murcia (J Econ Dyn Control 36: 914–-938, 2012) to employ the SMM approach to fit the model to the US data (1979Q1-2015Q1). We find that the monetary policy uncertainty affects economic welfare through different dimensions. On the one hand, the policy uncertainty itself distorts the economic welfare negligibly, not only by increasing volatilities of consumption and leisure, but also by decreasing their average levels. A higher level of trend inflation then signifies these changes to produce greater welfare costs. Furthermore, the adverse impacts of policy uncertainty on the economy, documented by the impulse response functions of macroeconomic variables to policy uncertainty shock, become larger when central banks raise their inflation targets. On the other hand, the costs of exogenous variations in trend inflation are larger if there is policy uncertainty.     

三种Copula-VaR计算方法与传统VaR方法的比较

金融风险测量VaR方法广泛应用于银行等金融机构,Copula技术以其处理非正态联合分布函数所具有的良好性质逐渐成为国内外研究的热点。本文将Copula理论应用于VaR的计算方法,并与传统的VaR方法进行比较,通过美元和欧元组合的实证研究,得到基于Copula的VaR方法能够更加有效地测量风险的结论。     

On the time-varying nature of the debt-growth nexus: evidence from the euro area

This article uses the DCC-generalized autoregressive conditional heteroskedasticity model to investigate the existence of time-varying correlations between public debt and economic growth. To that end, we use annual data from both central and peripheral countries of the euro area for the period 1961–2015. The results suggest that the relationships between these variables are time-varying and that in some countries and for some periods, there is a positive association between them.     

Accounting for the Accuracy of Beta Estimates in CAPM Tests on Assets with Time-varying Risks

This paper advocates two ways to make more efficient use of available information in reducing the bias of the risk premium estimate in two-pass tests of the CAPM. First, explicit modelling of the time-variability of betas can improve the accuracy of the beta forecasts. Second, the cross-sectional information available can be exploited more efficiently using individual stocks instead of portfolios provided that noisy beta predictions are given a smaller weight than more accurate ones. This paper proposes an adjustment of the cross-sectional regressions of excess returns against betas to give larger weights to more reliable beta forecasts. A significant positive relationship between returns and the beta forecast is obtained when the proposed approach is applied to data from the Helsinki Stock Exchange, while the traditional Fama–MacBeth approach as such finds no relationship at all.