Autoregressive Conditional Duration, Arch, Common Features and Cointegration

罗伯特·恩格尔（Robert Engle） 随着计算机功率和内存的增加，以越来越高的频率收集和分析数据变得可行。 记录每一笔交易的数据集-可能的最高频率-现在存在于许多金融数据集以及微观经济交易中，如电话和信用卡购买，这些交易由计算机记录。 对这些数据集的分析提出了新的和有趣的计量经济学挑战，其中之一是选择适当的时间间隔来汇总数据，以便生成一个具有均匀间隔的观测数据集。 固定区间分析的问题在于，它可能会给研究者留下许多无意义的数据点，或者掩盖最感兴趣的时期。 PI提出了一种固定区间分析的替代方法，他称之为自回归条件久期。 该方法不选择固定的时间间隔进行数据分析，而是将交易间隔作为随机变量进行分析。 因此，数据集成为每个事务的持续时间和特征的列表。 该过程直接对时间间隔进行建模，而不使用辅助数据或对时间流的原因进行假设。 ACD模型是用来分析IBM股票交易的价格，数量和持续时间的过程。 这一研究可以帮助机构预测市场的流动性和波动性的高频基础上。 它还可能对政府通过交易或断路器干预金融市场产生影响。 该研究还包括一些其他项目。 其中一个项目研究非线性商业周期。 非线性的本质总是很难提取，因为在近代历史中周期相对较少。 然而，如果一个国家的部门和最终产品类别地区都参与商业周期，那么非线性应该是常见的，更容易检测。 一个经济模型被开发来激发这些想法。 另一个项目涉及使用波动率的长期预测模型来产生波动率预测的期限结构。 这些预测可以用来构建一个投资组合，其价值不受投资组合冲击的影响。 这样的投资组合提供了一个新的维度来检验波动率预测的准确性：波动率的期限结构是否足以减少这种多期限投资组合的方差？

SBR-9422575 Robert Engle As computers increase in power and memory it becomes feasible to collect and analyze data at higher and higher frequencies. Data sets that record every transaction-- the highest frequency possible-- now exist for many financial data sets as well as microeconomic transactions such as telephone calls and credit card purchases that are recorded by computers. The analysis of such data sets poses new and interesting econometric challenges, one of them being the choice of the proper interval of time within which to aggregate the data so as to generate a data set with observations spaced evenly apart. The problem with fixed interval analysis is that it can leave the investigator with many uninformative data points or disguise the periods of most interest. The PI proposes an alternative to fixed interval analysis which he calls autoregressive conditional duration. Instead of selection a fixed interval for analyzing the data, it is proposed to let the interval between transactions be the random variable to be analyzed. Thus the data set becomes a list of durations and characteristics of each transaction. This procedure models the time intervals directly without using auxiliary data or imposing assumptions on the causes of the time flow. The ACD model is used to analyze the price, volume and duration process for IBM stock transactions. This research can help institutions forecast market liquidity and volatility on a high frequency basis. It can also have implications for government interventions in financial markets either through transactions or through circuit breakers. The research also includes a number of other projects. One project examines non-linear business cycles. The nature of the non-linearities is always difficult to extract because there are relatively few cycles in recent history. However, if sectors and final goods categories regions of the country all participate in the business cycle, then the non-linearities ought to be common and easier to detect. An economic model is developed to motive these ideas. Another project involves using ARCH models of volatility to produce a term structure of volatility forecasts. These forecasts can be used to construct a portfolio whose value is unaffected by portfolio shocks. Such portfolios provide a new dimension in which to examine the accuracy of volatility forecasts: Is the term structure of volatility adequate to reduce the variance of such multiple maturity portfolios?