Minnesota-type adaptive hierarchical priors for large Bayesian VARs

Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to making these highly parameterized VARs useful is the use of shrinkage priors. We develop a family of priors that captures the best features of two prominent classes of shrinkage priors: adaptive hierarchical priors and Minnesota priors. Like adaptive hierarchical priors, these new priors ensure that only ‘small’ coefficients are strongly shrunk to zero, while ‘large’ coefficients remain intact. At the same time, these new priors can also incorporate many useful features of the Minnesota priors such as cross-variable shrinkage and shrinking coefficients on higher lags more aggressively. We introduce a fast posterior sampler to estimate BVARs with this family of priors—for a BVAR with 25 variables and 4 lags, obtaining 10,000 posterior draws takes about 3 min on a standard desktop computer. In a forecasting exercise, we show that these new priors outperform both adaptive hierarchical priors and Minnesota priors.     

How does news sentiment affect the states of Japanese stock return volatility?

This paper examines the impact of public news sentiment on the volatility states of firm-level returns on the Japanese Stock market. We firstly adopt a novel Markov Regime Switching Long Memory GARCH (MRS-LMGARCH), which is employed to estimate the latent volatility states of intraday stock return. By using the RavenPack Dow Jones News Analytics database, we fit discrete choice models to investigate the impact of news sentiment on changes of volatility states of the constituent stocks in the TOPIX Core 30 Index. Our findings suggest that news occurrence and sentiment, especially those of macro-economic news, are a key factor that significantly drives the volatility state of Japanese stock returns. This provides essential information for traders of the Japanese stock market to optimize their trading strategies and risk management plans to combat volatility.     

小麦期货市场价格波动与到期效应的实证研究

通过对小麦期货市场期货品种收益率的分布与波动性进行实证分析,论证其时间序列存在ARCH效应;运用ARMA-GARCH模型对小麦期货品种进行了拟合分析和统计检验,结果表明小麦期货品种的波动性具有很高的持续性。通过添加到期时间的哑变量,可以证明大多数小麦期货合约存在到期效应。     

基于高频数据的深圳基金市场日内周期性研究

本文利用深市基金指数高频数据,采用Anderson和Bollerslev(1997)提出的弹性傅立叶回归(FlexibleFourierFormregression,即FFF回归)方法首次对深市基金市场进行了日内周期性的研究。通过对高频收益的定性分析,发现基金市场具有同股票市场相似的周期性,并对这一周期性进行了初步的理论解释。通过FFF方法,将该周期因子进行滤波处理以后,基金指数高频绝对收益不再具有明显周期性。FFF回归能较好地确定日内周期因子。     

一体化管理体系实施策划的过程及其主要内容

当前,企业正迫切需要建立一个综合性的管理体系,以期在满足各个标准的基础上实现组织整体的发展目标。本文对一体化管理体系(IMS)的实施策划过程及其主要内容进行探讨,旨在为企业建立一体化的管理体系提供参考。     

我国沪、深股市的波动性研究——基于GARCH族模型

金融市场的波动性不仅是投资者关注的焦点之一,而且也是被研究的热点之一。中国股市还非常年轻,股票市场的价格常常表现出大幅波动的特征。本研究以上证综合指数和深圳成分指数为研究对象,分别运用GARCH模型、TARCH模型和EGARCH模型同时拟合,并对比分析了中国股市日收益率波动的动态特征;结果显示,EGACH模型能更有效拟合股市的波动性。     

Testing for unit roots in time series models with non-stationary volatility

Many of the key macro-economic and financial variables in developed economies are characterized by permanent volatility shifts. It is known that conventional unit root tests are potentially unreliable in the presence of such behaviour, depending on a particular function (the variance profile) of the underlying volatility process. Somewhat surprisingly then, very little work has been undertaken to develop unit root tests which are robust to the presence of permanent volatility shifts. In this paper we fill this gap in the literature by proposing tests which are valid in the presence of a quite general class of permanent variance changes which includes single and multiple (abrupt and smooth-transition) volatility change processes as special cases. Our solution uses numerical methods to simulate the asymptotic null distribution of the statistics based on a consistent estimate of the variance profile which we also develop. The practitioner is not required to specify a parametric model for volatility. An empirical illustration using producer price inflation series from the Stock–Watson database is reported.     

QFII对中国证券市场影响的实证研究

我国施行QFII制度，外资进入中国A股市场的大门正式打开。本文利用GARCH（1，1）模型研究了，QFII对我国证券市场和投资基金的影响。结果表明，QFII后证券市场和投资基金的波动加大了，波动的持续性增加了，但是QFII对中国证券市场的整体影响还不大。     

Further development and sustainable utilization mode of grassland tourism resources: a case study of Inner Mongolia Autonomous Region, China

Tourism resources are important foundation for the development of tourism industry. Grassland is not only a kind of important resource but also a great attraction to visitors. Therefore, the integrated development and sustainable utilization of grassland tourism resources are of great significance. This paper, based on the analysis of literatures and the current problems existing in grassland tourism, summarizes the speciality of grassland tourism development, deals with the problem of speciality, and raises the modes of regional development, product development and management. This paper takes Inner Mongolia, the famous grassland tourism destination as example and carries out empirical research. Based on the status quo of grassland tourism resources in Inner Mongolia, this study analyzes the character- istics of grassland tourism resources, the necessity and feasibility of integrated development, then proposes the following suggestions: idea for development, mode of development, regional cooperation, tourism products development, espe- cially the three modes of development based on the resources conditions - the mode of relying on market, the mode of combination aggregation and the mode of relying on quality.     

Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models

We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.