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.     

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.     

浅谈玉柴重工的精益营销模式

广西玉柴重工有限公司是中国最大的中小型工程机械生产基地、出口基地,全球工程机械50强,排名43位。玉柴重工近年来高速发展,并在精益营销、公司管控、产品扩张等方面取得了令人瞩目的成绩。特别是在2010年,玉柴重工分别在常州、泸州建立了生产基地,在天津设立了全球营销结算中心、全球服务中心。天津玉柴重工的成立,标志着玉柴重工对营销资源进行了合理而有限的配置,迈出了精益营销的步伐。本文探讨进行精益营销的前因后果及必要性。     

SHIFTED HILL'S ESTIMATOR FOR HEAVY TAILS

Hill's estimator is a popular method for estimating the thickness of heavy tails. In this paper we modify Hill's estimator to make it shift-invariant as well as scale-invariant. The resulting shifted Hill's estimator is a more robust method of estimating tail thickness.

     

广州伊斯兰古迹二题

本文通过文献记载的新发现,将怀圣寺光塔存在的时间提前到北宋年间,并推断明人文献记载中的"金天教法"似与光塔塔顶之上的金鸡有关.     

明代重赋出于政治原因说

明代江南苏州、松江等地的田赋负担十分沉重,这是明史和中国经济史中的一大问题。明清以来,对明代苏松重赋问题的原因论述极多,但众说纷坛,莫衷一是。其实,明代除江南地区外,明代江西、陕西等地也出现了重赋现象。通盘考察明代的几个重赋区可以发现,这些地区虽自然、经济社会条件大相径庭,但共同的一点是元末明初皆为朱元璋的敌对势力所占领,他们曾与明军相抗衡,这正是这些地区遭到朱元璋重赋政策惩罚的原因。     

重新重工业化不等于粗放增长和走旧型工业化道路——对吴敬琏研究员相关论述的质疑

霍夫曼定理是已被历史证明的工业化发展的客观规律。重工业并不必然是资源消耗型、环境污染型产业。重工业的发展并不必然是粗放增长、外廷扩大,也不就是坚持走已经过时的旧型工业化道路。中国重新重工业化绕不开,也跨不过。反对重新重工业化,只会延误中国工业化、城镇化、现代化的进程。中国现在还不可能以服务业为主导,而且发展重工业并不排斥服务业特别是生产性服务业的发展。从总体和长期来看,重新开始重工业化有利于就业问题的解决。中国的比较优势已经发生了变化,具备了发展重工业的资本条件。重新重工业化,主要不是政府调节的产物,而是市场调节的结果。现在如果不再次大力发展重工业,到2020年就不可能完成基本实现工业化和城市化的任务。     

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.     

Monitoring Change in Persistence Against the Null of Difference-Stationarity in Infinite Variance Observations

In this article, we propose a moving kernel-weighted variance ratio statistic to monitor persistence change in infinite variance observations. We focus on I(1) to I(0) persistence change for sequences in the domain of attraction of a stable law and local-to-finite variance sequences. The null distribution of the monitoring statistic and its consistency are proved. In particular, a bootstrap procedure is proposed to determine the critical values for the derived asymptotic distribution depends on unknown tail index. The small sample performances of proposed monitoring procedure are illustrated by both simulation and application to a high frequency financial data.     

The Extremal Dependence Measure and Asymptotic Independence

Abstract

Extremal dependence analysis assesses the tendency of large values of components of a random vector to occur simultaneously. This kind of dependence information can be qualitatively different than what is given by correlation which averages over the total body of the joint distribution. Also, correlation may be completely inappropriate for heavy tailed data. We study the extremal dependence measure (EDM), a measure of the tendency of large values of components of a random vector to occur simultaneously and show consistency of an estimator of the EDM. We also show asymptotic normality of an idealized estimator in a restricted case of multivariate regular variation where scaling functions do not have to be estimated.