Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.     

Refined Inference on Long Memory in Realized Volatility

There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et al., 2001; Martens et al., 2004). The present article provides some illustrative analysis of how long memory may arise from the accumulative process underlying realized volatility. The article also uses results in Lieberman and Phillips (2004, 2005) to refine statistical inference about d by higher order theory. Standard asymptotic theory has an O(n^-1/2) error rate for error rejection probabilities, and the theory used here refines the approximation to an error rate of o(n^-1/2). The new formula is independent of unknown parameters, is simple to calculate and user-friendly. The method is applied to test whether the reported long memory parameter estimates of Andersen et al. (2001) and Martens et al. (2004) differ significantly from the lower boundary (d = 0.5) of nonstationary long memory, and generally confirms earlier findings.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

股票股利变化市场反应的实证研究

对公司分配方案中股票股利发生变化时股票收益率的研究,显示证券市场对股票股利分配方案发生变化时有一定反应,但与国外成熟资本市场的反应并不一致,说明我国证券市场距离国外成熟资本市场还有一定差距,证券市场信息披露制度尚不规范。     

水库移民返迁的成因及对策研究

从建国后水库移民返迁这一历史事实出发 ,分析了移民返迁既有经济状况、人文地理环境改变与区域文化差异等客观原因 ;又有自身素质特点以及期望值过高等主观原因 ;还有主管机构及相关部门管理松懈、思想政治工作不到位等外在原因 ,并在此基础上提出了缩小利益差别、实施对口支援扶助工程、增强移民的社会适应心态、走“文化移民”道路等建议     

上海股票市场波动实证性研究:2001--从交易方式角度透视

我国股票市场上的交易方式有两种,即集合竞价和连续竞价,交易方式的差异会对股价的波动性产生影响,而市场的波动性对股票市场而言是双刃剑,因此从交易方式角度探求股市的适度波动成为理论工作的一个重心.本文以上海股票市场为研究对象,针对2001年的全部交易数据进行实证研究.结果表明,集合竞价形成的开盘价格收益率的方差大于连续竞价形成的收盘价格收益率的方差,其原因在于集合竞价与连续竞价相比,其交易过程的透明度差和交易指令具有不可更改性,据此,从交易制度上提出政策建议.     

合乎历史 顺乎民意——论《反分裂国家法》出台的意义

《反分裂国家法》是中国政府为了反对和遏制“台独”势力分裂祖国,促进祖国和平统一所制订的一部法律。它体现了国家意志,代表了全中国人民的心声,是一部和平统一的法、一部促进两岸关系发展的法,也是一部中国自主解决台湾问题的法。它的出台有助于反对和遏制“台独”势力分裂国家,有助于促进两岸关系稳定发展,有助于维护台海地区的和平与稳定,合乎历史,顺乎民心。     

论《所罗门之歌》中的布鲁斯美学

布鲁斯是由美国黑人创造的音乐流派。美国黑人女作家托尼.莫里森用文字来吟唱布鲁斯,其小说《所罗门之歌》中一首古老的布鲁斯歌曲贯穿全文始终,整部小说洋溢着浓厚的布鲁斯美学。本文从布鲁斯美学的角度探讨黑人的身份认同、文化传统的回归。     

Finite Sample Performance in Cointegration Analysis of Nonlinear Time Series with Long Memory

Nonlinear functions of multivariate financial time series can exhibit long memory and fractional cointegration. However, tools for analysing these phenomena have principally been justified under assumptions that are invalid in this setting. Determination of asymptotic theory under more plausible assumptions can be complicated and lengthy. We discuss these issues and present a Monte Carlo study, showing that asymptotic theory should not necessarily be expected to provide a good approximation to finite-sample behavior.