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.     

中国A、H股市场分割的根源分析

按照投资者的身份不同,中国的股票市场可分为A、B、H股三个市场,三个市场处于严重的分割状态,其最显著的表现就是A、B或A、H股双重上市公司股票的价差现象。本文拟以A、H双重上市股票为例,深入分析价差产生的原因,揭示中国股票市场分割产生的根源。     

鲇鱼(Silurus asotus)产卵类型的研究

本文侧重研究了站鱼个体生殖力及产卵类型。结果表明：雌性个体1冬龄达性成熟,性腺一次成熟,卵子分批产出,产卵期是6—8月份,性成熟系数在7月份达高峰,8月份是产卵盛期,在产卵前的卵巢切片只发现有第4时相和第5时相卵母细胞,确定产卵类型为一次产卵。     

Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non-linear dependencies in stock returns

The main purpose of this work is to decompose the predictive performance of the moving average (MA) trading rule and find out the portion that could be attributed to the possible exploitation of linear and non-linear dependencies in stock returns. Data from the General Index of the Athens Stock Exchange, from the Standard and Poor-500 Index of the New York Stock Exchange and from the Austrian Traded Index of the Vienna Stock Exchange are filtered by linear filters so as the resulting simulated ‘returns’ exhibit no serial correlation. Applying MA trading rules to both the original and the simulated indices and using a new statistical testing procedure that takes into account the sensitivity of the performance of the trading rule as a function of the length of the MA it is found that the predictive performance of the trading rule is clearly weakened when applied to the simulated indices indicating that a substantial part of the rule's predictive performance is due to the exploitation of linear dependencies in stock returns. This weakening is uneven; in general the shorter the MA length the more pronounced the attenuation.     

Detecting Randomness: A Review of Existing Tests with New Comparisons

In this article we review and compare a number of existing tests for detecting randomness in time series data, with emphasis on stock market index data. By comparing variance ratio tests with traditional statistical tests, we have the most extensive simulation comparison of such procedures. The investigated tests are compared over a diverse group of distributions, models, and stock market applications. In our stock market data analysis, the choice of data transformation can have a noticeable effect on test results. This study provides the reader with a guide as to which test and transformation is most appropriate for their use.     

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This article develops a new identification procedure to estimate the contemporaneous relation between monetary policy and the stock market within a vector autoregression (VAR) framework. The approach combines high-frequency data from the futures market with the VAR methodology to circumvent exclusion restrictions and achieve identification. Our analysis casts doubt on VAR models imposing a recursive structure between innovations in policy rates and stock returns. We find that a tightening in policy rates has a negative impact on stock prices and that the Federal Reserve (Fed) has responded significantly to movements in the stock market. Estimates are robust to various model specifications.     

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

ABSTRACT

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.     

对完善股份合作制的认识

股份合作制是现代市场经济的必然产物 ,是历史的必然选择。但作为我国经济生活中不容忽视的一种企业组织形式 ,也存在着诸多问题。认清几个发展趋势 ,进一步规范和完善股份合作制在当前尤为重要     

关于我国股指期货设计方案的几点建议

本文主要阐述了作者对我国股指期货设计方案的一些看法。关于指数选择问题,本文认为不宜将现有的股价指数直接作为期货合约的交易基础,而应在对沪深两地现有成分股指数进行调整的基础上合成一个新指数;关于期货合约的设计,本文认为在确定乘数,最小波动点,合约交割月份,保证全水平及每日涨跌幅限制时,既要参考发达国家的经验,又要结合我国的国情;关于交易场所的选择,本文认为上海期货交易所应是首选。     

股票期权制度在我国的实践研究

随着企业制度改革的推进 ,股票期权制度逐渐成为我国理论界和实务界关注的热点问题 ,出现了大量的关于股票期权理论及其制度设计的研究成果 ,但实证分析其在我国实践效果的却比较少见。本文以上市公司为样本 ,通过对公司经营业绩的实证比较 ,对股票期权的实践效应进行分析 ,并对造成股票期权激励无效的原因进行了一些探讨