Influential observations in GARCH models

This paper examines local influence assessment in generalized autoregressive conditional heteroscesdasticity models with Gaussian and Student-t errors, where influence is examined via the likelihood displacement. The analysis of local influence is discussed under three perturbation schemes: data perturbation, innovative model perturbation and additive model perturbation. For each case, expressions for slope and curvature diagnostics are derived. Monte Carlo experiments are presented to determine the threshold values for locating influential observations. The empirical study of daily returns of the New York Stock Exchange composite index shows that local influence analysis is a useful technique for detecting influential observations; most of the observations detected as influential are associated with historical shocks in the market. Finally, based on this empirical study and the analysis of simulated data, some advice is given on how to use the discussed methodology.     

Simulating random variables using moment-generating functions and the saddlepoint approximation

When we are given only a transform such as the moment-generating function of a distribution, it is rare that we can efficiently simulate random variables. Possible approaches such as the inverse transform using numerical inversion of the transform are computationally very expensive. However, the saddlepoint approximation is known to be exact for the Normal, Gamma, and inverse Gaussian distribution and remarkably accurate for a large number of others. We explore the efficient use of the saddlepoint approximation for simulating distributions and provide three examples of the accuracy of these simulations.     

Pricing arithmetic Asian option under a two-factor stochastic volatility model with jumps

This paper presents an efficient Monte Carlo simulation scheme based on the variance reduction methods to evaluate arithmetic average Asian options in the context of the double Heston's stochastic volatility model with jumps. This paper consists of two essential parts. The first part presents a new flexible stochastic volatility model, namely, the double Heston model with jumps. In the second part, by combining two variance reduction procedures via Monte Carlo simulation, we propose an efficient Monte Carlo simulation scheme for pricing arithmetic average Asian options under the double Heston model with jumps. Numerical results illustrate the efficiency of our method.     

Estimation of parameters of SDE driven by fractional Brownian motion with polynomial drift

ABSTRACT

Strongly consistent and asymptotically normal estimators of the Hurst index and volatility parameters of solutions of stochastic differential equations with polynomial drift are proposed. The estimators are based on discrete observations of the underlying processes.     

华岗“政治大课”简论

为了贯彻《共同纲领》的文教政策，使山东大学顺利实现向新民主主义大学的转变，在建国初期山东大学思想政治教育中，校长华岗为全校师生所授政治大课，采取理论联系实际和以理服人的方针，系统地给山大师生讲授了党的政策和马克思主义基本原理，提高了山大师生的觉悟，为20世纪50年代山大的辉煌奠定了基础。     

马克思主义视阈下的生产力解读

迄今,国内外对生产力的含义众说纷纭,有"劳动生产率与因素综合说";有"人类的能力说";有"人对自然的作用说";有"关系系统说". 正确地把握马克思主义生产力含义,对于我们坚持"解放生产力,发展生产力"的思想和科学发展观有重大的理论意义和实践意义.重新审视马克思的原著,应该把生产力定义为:劳动者通过具体劳动生产出满足人们需要的使用价值时所表征出来的力量.按照这一观点,决定生产力的因素应该包括实体性因素、附着性因素或渗透性因素和运行性因素.     

影响蓖麻种子萌发因子的研究

对蓖麻种子萌发进行温度、水分、播种深度、种皮等处理试验，结果表明，温度和播种深度显著影响蓖麻种子萌发，其中土壤温度低和覆土厚是导致蓖麻种子萌发缓慢的主要因子;种皮的存在有利于菌麻种子萌发;土壤水分含量在10％以上能满足蓖麻种子萌发。     

试论城市化进程中的民族关系--以对临夏市的调查为视点

临夏市作为西北民族地区的一个小城市,在城市化进程中,其民族关系也会受到影响.针对临夏市的实际情况及学术界的研究现状,我们选取临夏市城市化进程中变化较大的几个变量,即民族居住格局的变迁、人口流动的影响、民族通婚的增减以及宗教因素,作为研究重点来衡量城市化进程中的民族关系.在此基础上得出临夏市各民族之间的关系总体上是平等、团结、互助、合作的社会主义新型民族关系,和谐平等的民族关系占据了主导地位的结论,并归纳出城市化进程中临夏市的民族关系具有主从性、敏感性、易发性、历史性、隐蔽性、复杂性等特点,最后提出临夏市各族群众必须解决自身的定位问题,建立临夏市和谐民族关系的良性运转机制.     

论回汉民族关系亲密的四大共因

本文重点对人们司空见惯而又习焉不察的回汉亲密的民族关系的共因作论述和分析。全文依次从回汉血缘关系的共同性,回汉同仇敌忾、反侵略、反压迫的共同性,回汉价值取向和性格的共同性,回汉两族语言使用的共同性四个方面有理有据地证明回汉民族从相互接触到相互学习再到相互认同的过程,正是整个中华民族由分散走向团结再走向和谐凝聚的一个生动缩影。这对了解中华民族多元一体的整合过程,对当今构建和谐社会的和谐民族关系有重要的现实意义。     

Time‐Varying Risk Premium in Large Cross‐Sectional Equity Data Sets

We develop an econometric methodology to infer the path of risk premia from a large unbalanced panel of individual stock returns. We estimate the time‐varying risk premia implied by conditional linear asset pricing models where the conditioning includes both instruments common to all assets and asset‐specific instruments. The estimator uses simple weighted two‐pass cross‐sectional regressions, and we show its consistency and asymptotic normality under increasing cross‐sectional and time series dimensions. We address consistent estimation of the asymptotic variance by hard thresholding, and testing for asset pricing restrictions induced by the no‐arbitrage assumption. We derive the restrictions given by a continuum of assets in a multi‐period economy under an approximate factor structure robust to asset repackaging. The empirical analysis on returns for about ten thousand U.S. stocks from July 1964 to December 2009 shows that risk premia are large and volatile in crisis periods. They exhibit large positive and negative strays from time‐invariant estimates, follow the macroeconomic cycles, and do not match risk premia estimates on standard sets of portfolios. The asset pricing restrictions are rejected for a conditional four‐factor model capturing market, size, value, and momentum effects.