题目：基于区间时间序列的自回归条件模型
Autoregressive Conditional Models for Interval-Valued Time Series Data
时间：2012年5月28日（周一）15:00-16:30
地点：8797威尼斯老品牌219
演讲者：中国科学院数学与系统科学研究院 韩艾 博士
摘要：An interval-valued observation in a time period contains more information than a point-valued observation in the same time period. Examples of interval data include the maximum and minimum temperatures in a day, the maximum and minimum GDP growth rates in a year, the maximum and minimum stock prices in a trading day, the ask and bid prices in a trading period, the long term and short term interest, and the 90%-tile and 10%- tile incomes of a cohort in a year, etc. Interval forecasts may be of direct interest in practice, as it contains information on the range of variation and the level of economic variables. Moreover, the informational advantage of interval data can be exploited for more efficient econometric estimation and inference.
We propose a new class of autoregressive conditional interval (ACI) models for interval-valued time series data. A minimum distance estimation method is developed to estimate the parameters of an ACI model, and the consistency and asymptotic normality of the proposed estimator are established. Simulation studies show that the use of interval time series data can provide more accurate estimation for model parameters in terms of mean squared error criterion. In an empirical study on asset pricing, we find that when return interval data is used, some bond market factors, particularly the default risk factor, are significant in explaining excess stock returns, even after the stock market factors are controlled in regressions. This differs from the previous finding in the literature.
Key Words: Asymptotic normality, Asset Pricing, Autoregressive conditional interval models, Interval time series, Mean squared error, Minimum distance estimation
（注：韩艾是8797威尼斯老品牌金融系本科毕业，2011年中科院博士毕业）
金融学系一年级研究生、所有博士生要求签到。
