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.     

CCI与中国证券市场走势的实证分析

美英等国的消费者信心指数(CCI)对股市指数收益有很重要的影响力。为探讨CCI与中国证券市场走势的相关关系,通过两类模型对1999年10月~2004年8月间我国股市收益与CCI之间的关系进行了实证分析。结果表明:CCI对深沪五个指数的走势有显著的解释与预测效果,但对我国的B股市场似乎没有显著性。     

现代国际贸易理论中的国内和国际规模收益

在二战后的国际贸易中,工业化国家之间的制造业产品的交易量巨大,这种产业内贸易现象的出现,在一定程度上对传统的赫克歇尔-俄林-萨缪尔逊贸易模型产生了冲击和质疑。经济学家从多个角度寻求对这种现象的理论解释,并开始注意产品差别化及其对规模经济的影响,但是,这些研究对贸易理论的影响很小,并且解释现实的能力也很弱。因此,把差别化的生产资料作为研究的重点,在国际收益的框架下,建立模型以推出国际规模收益,并且运用这一模型得出了国际规模收益、传统的国内规模收益和要素禀赋论之间的关系。     

中国股市收益率分布特征研究

应用修正Weibull分布对上海综合指数收益率和深圳成分指数收益率的分布状况进行研究。结果表明:经过简单的移位变换后,上证综指收益率和深成指收益率可完全用修正Weibull分布来刻画;大收益率服从次指数分布,小收益率服从超指数分布;两股指收益率的概率分布存在一些差异,上证综指的波动性大于深成指的波动性;沪深股市收益率的分布在1996年以后发生了较大的变化,其中沪市变化更大。     

Bayesian analysis of multivariate stochastic volatility with skew return distribution

Multivariate stochastic volatility models with skew distributions are proposed. Exploiting Cholesky stochastic volatility modeling, univariate stochastic volatility processes with leverage effect and generalized hyperbolic skew t-distributions are embedded to multivariate analysis with time-varying correlations. Bayesian modeling allows this approach to provide parsimonious skew structure and to easily scale up for high-dimensional problem. Analyses of daily stock returns are illustrated. Empirical results show that the time-varying correlations and the sparse skew structure contribute to improved prediction performance and Value-at-Risk forecasts.     

Time series models with asymmetric Laplace innovations

We propose autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedastic (GARCH) models driven by asymmetric Laplace (AL) noise. The AL distribution plays, in the geometric-stable class, the analogous role played by the normal in the alpha-stable class, and has shown promise in the modelling of certain types of financial and engineering data. In the case of an ARMA model we derive the marginal distribution of the process, as well as its bivariate distribution when separated by a finite number of lags. The calculation of exact confidence bands for minimum mean-squared error linear predictors is shown to be straightforward. Conditional maximum likelihood-based inference is advocated, and corresponding asymptotic results are discussed. The models are particularly suited for processes that are skewed, peaked, and leptokurtic, but which appear to have some higher order moments. A case study of a fund of real estate returns reveals that AL noise models tend to deliver a superior fit with substantially less parameters than normal noise counterparts, and provide both a competitive fit and a greater degree of numerical stability with respect to other skewed distributions.     

Tail asymptotic of discounted aggregate claims with compound dependence under risky investment

This paper considers the tail asymptotic of discounted aggregate claims with compound dependence under risky investment. The price of risky investment is modeled by a geometric Lévy process, while claims are modeled by a one-sided linear process whose innovations further obeying a so-called upper tail asymptotic independence. When the innovations are heavy tailed, we derive some uniform asymptotic formulas. The results show that the linear dependence has significant impact on the tail asymptotic of discounted aggregate claims but the upper tail asymptotic independence is negligible.     

A semi-parametric method for estimating the beta coefficients of the hidden two-sided asset return jumps

ABSTRACT

We introduce a new methodology for estimating the parameters of a two-sided jump model, which aims at decomposing the daily stock return evolution into (unobservable) positive and negative jumps as well as Brownian noise. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. For this purpose, at first we use the Variance Gamma (VG) distribution which is frequently used in modeling financial time series and leads to the revelation of the hidden market jumps' distributions. Then, our method is based on the central moments of the stock returns for estimating the parameters of the model. It is proved that the proposed method provides always a solution in terms of the jump beta coefficients. We thus achieve a semi-parametric fit to the empirical data. The methodology itself serves as a criterion to test the fit of any sets of parameters to the empirical returns. The analysis is applied to NASDAQ and Google returns during the 2006–2008 period.     

A股上市首日超额收益影响因素的实证研究

在股权分置改革的背景下,运用多元线性回归模型的加权最小二乘法,对2004年1月1日至2007年12月31日期间发行上市的206只A股的实证研究,发现上市首日股票换手率、筹资规模和市场指数对上市首日超额涨幅具有较显著的影响;而发行价格、发行市盈率、发行前每股净资产、发行后实际流通股比例、发行方式及股权分置改革对A股上市首日超额收益影响不显著。     

技术投资、人力资本积累与我国技术进步缓慢现象研究

通过建立技术进步投资本与人力资本积累投资的分析模型,对我国企业技术进步缓慢现象进行理论与经验分析。结果表明,人力资本与技术进步之间互补形态将依据短边规则决定技术进步速度,但我国相对落后的教育回报影响了人力资本积累的速度,导致企业技术进步缓慢。因此,通过增加投入及深化教育改革等方式为工人提供良好的教育,是促进我国企业技术水平提高的保证。