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.     

中国燃油期货市场的VaR与ES风险度量

近年来,我国燃油期货市场取得快速发展,但有关该市场波动特征和风险状况的研究却非常缺乏。以上海期货交易所燃油期货价格指数为例,分别在多头和空头两种头寸状况以及5种不同分位数水平下,运用条件覆盖检验、非条件覆盖检验等后验分析方法,实证对比了不同风险测度模型对VaR和ES两种不同风险指标估计的精度差异。研究结果表明:在我国燃油期货市场的风险测度估计中考虑国际燃油价格波动因素有助于获得更为精准的风险测度精度;在综合考虑了模型对价格变化动力学的刻画效果以及对极端风险的测度精度等因素后,FIGARCH_CST-SST模型是一个相对合理的风险测度模型选择。     

我国农产品期货市场的风险测度模型及其后验分析

近年来,我国农产品期货市场取得快速发展,但有关该市场波动特征和风险状况的研究却非常缺乏.以我国农产品期货市场中的4种代表性价格指数为例,首先对其价格变化统计特征及波动模式进行了全面深入的检验,然后运用严谨系统的后验分析(Backtesting analysis)方法,分别在多头和空头两种头寸状况以及5种不同分位数水平下,实证对比了8种风险测度模型对VaR (Value at Risk)和ES (Excepted shortfall)两种不同风险指标估计的精度差异.研究结果表明,我国农产品期货市场的价格波动不存在显著的杠杆效应(Leverage effect),但却具有明显的有偏(Skewed)和条件厚尾(Conditional fat-tail)特征.另外,在综合考虑了模型估计效率和风险测度精度后,基于有偏学生t分布的普通GARCH模型是一个相对合理的风险测度模型选择.     

Implications of uncorrelatedness and independence in linear regression models

The assumption of normally distributed disturbances in the linear regression model implies that the disturbances are both uncorrelated and independent. Recently however, attention has focussed on possibly nonnonnally distributed disturbances, and in this case a distinction needs to be made between only uncorrelated disturbances and independently distributed disturbances. In this paper, general specification errors associated with misspecifying uncorrelatedness and independence for student - t distributed disturbances is examined. This class of distributions is a reasonable way of modelling tails that are fatter than those of the normal distribution which has applications to the modelling of series such as prices in financial and commodity markets, growth -curve models and astronomical data. Specification tests which test for only uncorrelatedness versus independence are also discussed.     

The empirical Bayes estimators of the mean and variance parameters of the normal distribution with a conjugate normal-inverse-gamma prior by the moment method and the MLE method

Most of the samples in the real world are from the normal distributions with unknown mean and variance, for which it is common to assume a conjugate normal-inverse-gamma prior. We calculate the empirical Bayes estimators of the mean and variance parameters of the normal distribution with a conjugate normal-inverse-gamma prior by the moment method and the Maximum Likelihood Estimation (MLE) method in two theorems. After that, we illustrate the two theorems for the monthly simple returns of the Shanghai Stock Exchange Composite Index.     

Regression analysis of hierarchical poisson-like event rate data: superpopulation model effect on predictions

This paper studies prediction of future failure (rates) by hierarchical empirical Bayes (EB) Poisson regression methodologies. Both a gamma distributed superpopulation as well as a more robust (long-tailed) log student-t superpopulation are considered. Simulation results are reported concerning predicted Poisson rates. The results tentatively suggest that a hierarchical model with gamma superpopulation can effectively adapt to data coming from a log-Student-t superpopulation particularly if the additional computation involved with estimation for the log-Student-t hierarchical model is burdensome.