国内外原油市场收益率及其波动性的双长记忆性测度

本文基于我国原油现货价格和欧洲Brent原油现货价格的数据,运用多种计量模型对原油市场收益率及其波动性的长记忆性进行测度。研究发现,我国原油市场收益率序列不存在长相依性特征,波动率序列则存在长记忆性效应;国外原油市场收益率及波动率序列均存在显著且较强的长记忆性。同时,检验结果表明,采用Student-t分布来刻画"尖峰厚尾"分布性质并利用TGARCH模型来描述"杠杆效应"是非常必要的。     

我国消费增长均值过程和波动过程的双长期记忆性测度

本文基于ARFIMA-FIGARCH模型对我国1995年1月至2009年12月期间社会消费品零售总额增速的动态过程进行检验,发现我国消费增长的一阶矩和二阶矩均存在显著的长期记忆性特征,即我国消费增长序列具有双长期记忆性特征,这表明我国消费增长序列具有一定的粘性。同时,ARFIMA-FIGARCH模型的估计结果说明,相对于ARFIMA模型以及FIGARCH模型而言,ARFIMA-FIGARCH模型的估计效果更优。     

质押式回购利率的风险度量研究——基于ARMA-GARCH模型的实证检验

文章对2002年1月4日至2009年3月31日我国银行间质押式回购市场进行实证研究,结果表明:(1)t-分布和g-分布下的模型能更好地捕捉回购利率序列的尖峰厚尾性;(2)回购利率波动具有显著的非对称性,利率上升时的波动更大;(3)ARMA-PARCH-M模型是估计回购利率VaR值的理想模型,t-分布下的模型适合多头头寸VaR值的预测,而g-分布下的模型适合空头头寸VaR值的预测.这说明我国回购市场的利率风险较高.     

基于FIGARCH模型的中国房地产指数的实证分析

在T分布和正态分布假设下采用GARCH模型和FIGARCH模型对上证地产股指数日收益率序列进行建模分析,结果表明,上证地产股指数日收益率序列的波动具有显著的长记忆性,表明外部冲击对波动有着长期的影响。因此,采用FIGARCH模型建模的效果优于采用GARCH模型建模的效果,并且在T分布假设下拟合模型,其效果优于在正态分布假设下拟合的模型。     

宏观经济环境对并购活动的影响——基于时间序列数据的经验研究

基于宏观经济动因,研究经济发展水平、利率与股票价格指数对我国总体性并购活动的影响,实证检验结果表明：除利率之外,经济发展水平及股票价格均与总体性并购活动存在长期的均衡关系,并且为总体性并购活动的Granger原因;在短期内,经济发展水平波动与股票价格波动对并购活动波动均存在正向影响,而利率波动对并购活动波动存在负向影响。这说明经济发展水平与股票价格是我国并购浪潮的重要驱动因素。     

股市收益率与波动性长期记忆效应的实证研究

股票市场长期记忆效应问题是近来金融实证研究的一个热点.多数的研究集中在收益率长期相关性的考察上,较少有对波动率序列的研究.然而,波动率的长期记忆性不仅会导致金融市场上的波动持久性特征,而且将对波动率的预测与衍生证券定价产生重要的影响.基于此,本文通过修正的R/S分析与ARFIMA模型对我国股市收益率及其波动性的长期相关性进行了实证研究.结果表明:中国股市具有显著的非线性特征,虽然收益率序列的自相关性较弱,但波动性序列却表现出显著的长期记忆效应.这一结论将为研究股票价格行为特征与金融经济学理论提供新的方向.     

我国银行间同业拆借市场利率风险度量

本文利用1996年1月5日至2008年9月17日的我国银行间隔日同业拆借利率序列,通过GARCH模型对收益数据中的自相关和异方差现象进行了实证研究,采用MLE方法估计模型参数,再利用所得参数分别计算了不同收益率分布假设下的不同置信水平的VaR值;在此基础上,进行回测检验,比较了各种模型估计效果,并进一步分析了我国同业拆借利率市场的系统性风险历史波动趋势;最后提出了相关结论与政策建议。     

中国石油与联通A、H和N股的长期记忆生评估

针对上海、香港、纽约的中国联通、中国石油股票市场日收益率以及波动序列是否具有长期记忆性的问题,提出分别运用非参数统计法(R/S,V/S)和半参数估计法(GPH,tapered GPH)进行评估.通过选择2000年6月23日至2009年7月24日的中国联通、中国石油的日收益率及其波动性为研究对象,结果发现:它们在中国上海和香港市场的日收益率序列具有长期记忆性,而在纽约市场的不具有长期记忆性;它们的日收益率波动序列在中国上海、香港、纽约都具有长期记忆性.具有长期记忆特征表明了股票系统非线性结构与市场的非有效性,即中国的股票市场效率是有待于提高的.建议尽快完善A股的做空机制.     

中国石油与联通A、H和N股的长期记忆性评估

针对上海、香港、纽约的中国联通、中国石油股票市场日收益率以及波动序列是否具有长期记忆性的问题,提出分别运用非参数统计法（R／S,V／S）和半参数估计法（GPH,tapered GPH）进行评估。通过选择2000年6月23日至2009年7月24日的中国联通、中国石油的日收益率及其波动性为研究对象,结果发现：它们在中国上海和香港市场的日收益率序列具有长期记忆性,而在纽约市场的不具有长期记忆性;它们的日收益率波动序列在中国上海、香港、纽约都具有长期记忆性。具有长期记忆特征表明了股票系统非线性结构与市场的非有效性。即中国的股票市场效率是有待于提高的。建议尽快完善A股的做空机制。     

ARCH类模型拟合我国股市波动的实证分析

股票价格的频繁波动是股票市场最明显的特征之一。在国内外长期的金融时间序列实证分析中，人们发现金融时间序列通常带有一些明显的特征：金融时间序列波动的集聚性，即在某些时间内波动十分剧烈，而在另一些时间内波动又相对平静；金融时间序列的收益率分布存在尖峰厚尾性，即收益率分布的峰度比标准正态分布的峰度高等。虽然大量的事实表明，短期金融资产价格及收益率是不可预测的，但研究表明，ARCH类模型可以成功预测金融资产收益率的方差。本文采用ARCH类模型对我国股票价格指数进行拟合，从而得出一些有益的结论和启示。     

Modelling exchange rate volatility with random level shifts

Recent literature has shown that the volatility of exchange rate returns displays long memory features. It has also been shown that if a short memory process is contaminated by level shifts, the estimate of the long memory parameter tends to be upward biased. In this article, we directly estimate a random level shift model to the logarithm of the absolute returns of five exchange rates series, in order to assess whether random level shifts (RLSs) can explain this long memory property. Our results show that there are few level shifts for the five series, but once they are taken into account the long memory property of the series disappears. We also provide out-of-sample forecasting comparisons, which show that, in most cases, the RLS model outperforms popular models in forecasting volatility. We further support our results using a variety of robustness checks.     

Long memory revisit in Chinese stock markets: Based on GARCH-class models and multiscale analysis

In the present work we propose the rescaled range analysis (R/S), modified R/S method and detrended fluctuation analysis (DFA) to investigate the long memory property of Chinese stock markets based on the conditional and actual volatility series, and show that the stock markets in China display moderate positive degree of long memory. For the robustness, we implement the multiscale analysis on dynamic changes of time-varying Hurst exponents by applying the rolling window method based on DFA. Our results reveal that APGARCH model with the superior forecasting ability captures the long memory property better than other GARCH-class models for different time scale interval. Interestingly, the time-varying Hurst exponents of the sudden “jumps” for the conditional volatility calculated by the DFA method using the APGARCH model are smaller than that of the actual volatility series, which indicates that APGARCH model may underestimate the long memory property in the Chinese stock market. Our evidences provide new perspectives for the financial market forecasting.     

A general model for short-term interest rates

A general one-factor model for short-term interest rates is proposed. Besides the long memory fractionally integrated mean process, the model also consists of a power function of the interest rate as well as the GARCH effect in the conditional variance. The estimation results show that, while there is no evidence for fractional integration in the mean beyond the well-known martingale property, both the power function of the interest rate and the GARCH effect (but not the ARCH effect) are highly significant in the formation of the conditional variance. Test results also confirm a structure change in October 1979 due to the shift in the Federal Reserve monetary policy.     

Market volatility and retail interest rate pass-through

Using the asymmetric threshold cointegration test proposed by Enders and Siklos [Enders, W., Siklos, P., 2001. Cointegration and threshold adjustment. Journal of Business and Economic Statistics 19, 166–176] and the EC-EGARCH (1, 1)-M model, this study examines the interest rate pass-through mechanism between the money market rate and the retail interest rate. In addition, we also investigate the impact of the interest rate volatility on the interest rates of the U.S. and nine Asian countries. We find that the complete pass-through only exists in the U.S. deposit rate. The threshold cointegration test results show that the asymmetric cointegration relation exists in the deposit interest rate in five countries and in the lending rate in three countries. The symmetric cointegration relation exists in two countries. Besides, an estimation of the conditional means using the EC-EGARCH (1, 1)-M model shows that the effect of interest rate volatility on the retail interest rate differs from country to country. Among the eight countries that exhibit asymmetric adjustments in the short run, five of them are found to have upwards rigid adjustments in the deposit interest rate and three downwards rigid adjustments in the lending rate. The empirical results of Hong Kong, Taiwan, and the Philippines support the hypothesis of collusive pricing arrangements. As to the estimation of the conditional variance, among the seven countries that exhibit asymmetric volatility, the leverage effect in the lending rate exists in two of them.     

我国股市波动的非对称性和杠杆效应研究

本文选取2006年1月4日到2008年12月31日期间上证综合价格指数日收益率和收益波动率的数据,建立二者变量指标的GARCH模型、AGARCH模型、EGARCH模型,对我国牛熊市轮替过程中股票市场波动的非对称性和杠杆效应进行实证分析。结果发现,股改后牛熊市期间我国股票市场的波动表现出显著的长记忆性、非对称性和杠杆效应,股票市场波动性对"利好"和"利空"消息呈现出不平衡性反应,我国股票市场出现了强市恒强、弱市恒弱现象。最后,从投资者心理预期、过度反应与反应不足、投资者构成和交易机制等方面对该结论进行了分析。     

Can GARCH-class models capture long memory in WTI crude oil markets?

This paper investigates the issue whether GARCH-type models can well capture the long memory widely existed in the volatility of WTI crude oil returns. In this frame, we model the volatility of spot and futures returns employing several GARCH-class models. Then, using two non-parametric methods, detrended fluctuation analysis (DFA) and rescaled range analysis (R/S), we compare the long memory properties of conditional volatility series obtained from GARCH-class models to that of actual volatility series. Our results show that GARCH-class models can well capture the long memory properties for the time scale larger than a year. However, for the time scale smaller than a year, the GARCH-class models are misspecified.     

Long memory in the volatility of China stock returns

In this paper, we empirically examine the volatility process of China's stock market returns using daily and weekly Shanghai and Shenzhen stock indices during January 1990 to August 2008. To investigate the property of the process, we used the FIGARCH (fractionally integrated GARCH) model including GARCH and IGARCH processes as special cases. Since the FIGARCH model allows fractional integration order, it can detect hyperbolically decaying volatility processes which cannot be explained by previous models with integer integration order. Our results show that the Shanghai and Shenzhen stock indices exhibit long-term dependencies. The long memory properties of the Shanghai and Shenzhen stock markets do not seem to be spuriously induced without exception.