Estimates of the likelihood of extreme returns in international stock markets

This study applies extreme-value theory to daily international stock-market returns to determine (1) whether or not returns follow a heavy-tailed stable distribution, (2) the likelihood of an extreme return, such as a 20% drop in a single day, and (3) whether or not the likelihood of an extreme event has changed since October 1987. Empirical results reject a heavy-tailed stable distribution for returns. Instead, a Student-t distribution or an autoregressive conditional heteroscedastic process is better able to capture the salient features of returns. We find that the likelihood of a large single-day return diff ers widely across markets and, for the G-7 countries, the 1987 stock-market drop appears to be largely an isolated event. A drop of this magnitude, however, is not rare in the case of Hong Kong. Finally, there is only limited evidence that the chance of a large single-day decline is more likely since the October 1987 market drop; however, exceptions include stock markets in Germany, The Netherlands and the UK.     

生命周期中教育收益率变动轨迹分析

有很多使用长期面板数据的研究指出,当期收入与终身收入并不平行,表明教育收益率在生命中的不同时期可能是有差异的。认识教育收益率的异质性是有效制定相关政策的基础,也是研究领域非常关注的问题,但是很少有研究分析教育收益率在生命周期中的异质性。本研究使用中国家庭健康与营养调查面板数据以及多水平分析方法,探讨了教育收益率在生命周期中的变动轨迹,并进行了性别差异分析。研究发现：教育收益率在整个生命周期中呈现先上升后下降的倒U型分布,在初期为负值;在生命周期前期女性的教育收益率大于男性,后期是男性大于女性;教育水平越高,收入增长所持续的时期越长。     

Additive Nonparametric Regression in the Presence of Endogenous Regressors

In this article we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient, and free from the curse of dimensionality. Monte Carlo simulations support the asymptotic developments. We employ a partially linear extension of our model to study the relationship between child care and cognitive outcomes. Some of our (average) results are consistent with the literature (e.g., negative returns to child care when mothers have higher levels of education). However, as our estimators allow for heterogeneity both across and within groups, we are able to contradict many findings in the literature (e.g., we do not find any significant differences in returns between boys and girls or for formal versus informal child care). Supplementary materials for this article are available online.     

Detecting intraday periodicities with application to high frequency exchange rates

Summary.  Many recent papers have documented periodicities in returns, return volatility, bid–ask spreads and trading volume, in both equity and foreign exchange markets. We propose and employ a new test for detecting subtle periodicities in time series data based on a signal coherence function. The technique is applied to a set of seven half-hourly exchange rate series. Overall, we find the signal coherence to be maximal at the 8-h and 12-h frequencies. Retaining only the most coherent frequencies for each series, we implement a trading rule that is based on these observed periodicities. Our results demonstrate in all cases except one that, in gross terms, the rules can generate returns that are considerably greater than those of a buy-and-hold strategy, although they cannot retain their profitability net of transactions costs. We conjecture that this methodology could constitute an important tool for financial market researchers which will enable them to detect, quantify and rank the various periodic components in financial data better.     

Exponential GARCH Modeling With Realized Measures of Volatility

We introduce the realized exponential GARCH model that can use multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between returns and volatility. We apply the model to 27 stocks and an exchange traded fund that tracks the S&P 500 index and find specifications with multiple realized measures that dominate those that rely on a single realized measure. The empirical analysis suggests some convenient simplifications and highlights the advantages of the new specification.     

跨期风险—收益权衡关系研究——来源于沪港通的新证据

风险—收益权衡关系是金融经济学重要内容之一。应用扩展的EGARCH-M模型考察了上海股票市场组合跨期风险收益权衡关系以及沪港通对这种关系的影响。研究发现,上海股票市场组合的跨期风险收益权衡关系显著为正,沪港通的开通正向加强了这种关系,提高了投资者的风险溢价需求。通过不同样本的对比和不同条件分布的假定证实了结果的稳健性。此外,还识别出了SGED分布可能更适合用于描述上海股票市场组合收益率的条件概率分布。     

2003—2008年间中国教育收益变动趋势研究

人力资本理论认为个人收入分配在一定程度上取决于人力资本的分配和人力资本的回报,提高人力资本的最主要途径为教育。教育对个人收入的积极促进作用已得到许多研究的证实。教育收益率的估算及其变动趋势的研究具有重要的现实意义。运用CGSS2003、2005、2006与2008年数据对中国教育明瑟收益率的回归结果显示2003-2008年间中国的平均教育收益率得到了明显提高,与20世纪八、九十年代相比呈现出进一步提升的态势。     

The predictability of stock returns - a nonparametric approach

This paper reexamines the predictability of stock returns with a nonparametric model. We first identify, through a set of diagnostic tests, five lagged predictive factors from a linear model. Using these factors, we predict one-month-ahead stock index returns with a nonparametric approach. We find that our nonparametricmodel. We first identify, through a set of diagnostic tests, five lagged predictive factors from a linear model. Using these factors, we predict on -month-ahead stock index returns with a nonparametric approach. We find that our nonparametric model can correctly predict about 74% of stock index return signs. With various ex ante trading rules based on nonparametric predictions and transaction cost schedules, we then compare the performance of "managed" portfolios with that of the buy and hold portfolios. We fmd that the managed portfolios are mean-variance dominant over the buy-and-hold strategies when no or low transaction costs are assumed. When high transaction costs are assumed instead, the mean-variance dominance diminishes However,the Sharpe index of risk-adjusted portfolio performanceindicates that the managed portfolios significantly outperform the buy-and-hold strategies even for the high-transaction cost scenario. We show that the difference in performance between the managed portfolios and the buy-and-hold strategies can be partially explained by the January effect or the small firm effect. In sum, this paper demonstrates the merits of using a nonparametric approach for predicting stock returns and testing market efficiency.     

中国教育收益率的长期变动趋势分析

本研究采用中国健康与营养调查1989-2009年的追踪数据,使用多层线性交互分类模型估计了全国教育收益率的总体变化趋势,并通过引入宏观经济发展指标来考察教育收益率变化的影响因素.结果表明,21世纪以来全国教育收益率并没有延续以往快速稳定增长的势头,中国市场转型过程中的制度变革和经济结构变化对教育收益率的变动都有显著影响.由于我国城乡分割的二元经济结构,本研究还比较了城镇和农村地区教育收益率变动趋势的差异,农村地区教育收益率先高后低反映了我国先农村后城市的改革开放进程.     

A wavelet-based approach to the analysis and modelling of financial time series exhibiting strong long-range dependence: the case of Southeast Europe

This paper demonstrates the utilization of wavelet-based tools for the analysis and prediction of financial time series exhibiting strong long-range dependence (LRD). Commonly emerging markets' stock returns are characterized by LRD. Therefore, we track the LRD evolvement for the return series of six Southeast European stock indices through the application of a wavelet-based semi-parametric method. We further engage the á trous wavelet transform in order to extract deeper knowledge on the returns term structure and utilize it for prediction purposes. In particular, a multiscale autoregressive (MAR) model is fitted and its out-of-sample forecast performance is benchmarked to that of ARMA. Additionally, a data-driven MAR feature selection procedure is outlined. We find that the wavelet-based method captures adequately LRD dynamics both in calm as well as in turmoil periods detecting the presence of transitional changes. At the same time, the MAR model handles with the complicated autocorrelation structure implied by the LRD in a parsimonious way achieving better performance.     

资产配置模型的选择:回报、风险抑或二者兼具

近年来以风险平价为代表的基于风险的配置模型广为流行。这些模型的一大特点是放弃回报信息。而以均值方差模型代表的基于回报的配置模型则认为回报很重要而且默认对回报的预测是准确的。这两种做法都有问题。考虑到回报的可预测性得到了大量经验研究的支持,那么对于基于风险的配置模型而言,完全放弃回报则意味着有关回报的有用信息得不到充分利用。对于基于回报的配置模型而言,不考虑参数估计误差而且对输入参数敏感的缺点也大大抵消了它们利用回报信息带来的好处。那么,回报是否重要以及应该如何使用回报成了资产配置研究所面临的一个重大问题。为此,本文提出以风险平价为配置基准,以贝叶斯VAR回报预测为主观观点的Black-Litterman(贝叶斯BL)模型回答这一命题。利用1952-2016年的美国股票和债券季度数据,本文将贝叶斯BL模型与现有配置模型进行比较研究。实证结果表明,相比基于回报的配置模型,贝叶斯BL模型降低了组合风险;相比基于风险的配置模型,贝叶斯BL模型增强了组合回报。这些特性来自于它既能利用回报可预测性带来的有用信息,又能够发挥基于风险的配置模型在控制风险方面的优势。因此该模型表现出增强回报和控制风险兼具的特点,是一条具有潜力的资产配置新方案。     

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.     

Multivariate Stochastic Volatility Models with Correlated Errors

We develop a Bayesian approach for parsimoniously estimating the correlation structure of the errors in a multivariate stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors is potentially very large, we impose a prior that allows the off-diagonal elements of the inverse of the correlation matrix to be identically zero. The model is estimated using a Markov chain simulation method that samples from the posterior distribution of the volatilities and parameters. We illustrate the approach using both simulated and real examples. In the real examples, the method is applied to equities at three levels of aggregation: returns for firms within the same industry, returns for different industries, and returns aggregated at the index level. We find pronounced correlation effects only at the highest level of aggregation.     

Multivariate Stochastic Volatility Models with Correlated Errors

We develop a Bayesian approach for parsimoniously estimating the correlation structure of the errors in a multivariate stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors is potentially very large, we impose a prior that allows the off-diagonal elements of the inverse of the correlation matrix to be identically zero. The model is estimated using a Markov chain simulation method that samples from the posterior distribution of the volatilities and parameters. We illustrate the approach using both simulated and real examples. In the real examples, the method is applied to equities at three levels of aggregation: returns for firms within the same industry, returns for different industries, and returns aggregated at the index level. We find pronounced correlation effects only at the highest level of aggregation.     

2003-2008年问中国教育收益变动趋势研究

人力资本理论认为个人收入分配在一定程度上取决于人力资本的分配和人力资本的回报,提高人力资本的最主要途径为教育。教育对个人收入的积极促进作用已得到许多研究的证实。教育收益率的估算及其变动趋势的研究具有重要的现实意义。运用CGSS2003、2005、2006与2008年数据对中国教育明瑟收益率的回归结果显示2003-2008年间中国的平均教育收益率得到了明显提高,与20世纪八、九十年代相比呈现出进一步提升的态势。     

国有企业改革与工资支付结构变革的面板数据分析

本文拟考察在1988-2002年期间国企改革对中国城镇工资支付结构变革的影响、特别是工资决定因素的变化。我们发现对教育的回报在增长,而对工作经验的回报在下降。2002年的数据表明：不断扩大的性别工资差距以及不断增加的非市场因素工资溢价可能均已不再上升。国企改革以及重工业在总体产业结构中比重的不断降低对这些部门职工的工资均已产生影响。我们使用1998-2002年回顾性面板数据提供了关于部门所有制结构、是否是党员以及失业等变量对工资影响的固定效应估计。     

Testing and Valuing Dynamic Correlations for Asset Allocation

We evaluate alternative models of variances and correlations with an economic loss function. We construct portfolios to minimize predicted variance subject to a required return. It is shown that the realized volatility is smallest for the correctly specified covariance matrix for any vector of expected returns. A test of relative performance of two covariance matrices is based on work of Diebold and Mariano. The method is applied to stocks and bonds and then to highly correlated assets. On average, dynamically correct correlations are worth around 60 basis points in annualized terms, but on some days they may be worth hundreds.     

Measurement of Abnormal Returns From Small Firms

Correction for heteroscedasticity in returns from portfolios long in small firms and short in large firms listed on the New York Stock Exchange reduces the estimate of market risk and increases the estimated abnormal return. Greatly improved diagnostic test statistics are obtained, strengthening the evidence for the existence of positive average abnormal returns from small firms. Periodicity of order 6 and 12 months is identified. The estimation procedure operates by exploiting the autoregressive pattern of heteroscedasticity in the return data.     

Best subset selection of autoregressive models with exogenous variables and generalized autoregressive conditional heteroscedasticity errors

Summary.  We develop an efficient way to select the best subset autoregressive model with exogenous variables and generalized autoregressive conditional heteroscedasticity errors. One main feature of our method is to select important autoregressive and exogenous variables, and at the same time to estimate the unknown parameters. The method proposed uses the stochastic search idea. By adopting Markov chain Monte Carlo techniques, we can identify the best subset model from a large of number of possible choices. A simulation experiment shows that the method is very effective. Misspecification in the mean equation can also be detected by our model selection method. In the application to the stock-market data of seven countries, the lagged 1 US return is found to have a strong influence on the other stock-market returns.     

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.