Accomplishment Based Renewal of: Autoregressive Conditional Duration, Arch, Common Features, and Cointegration

基于成就的更新：自回归条件持续时间、拱形、共同特征和协整

• 批准号: 9730062
• 负责人: Robert Engle
• 金额: $ 22.91万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-05-15 至 2003-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9730062&HistoricalAwards=false
• 关键词: Accomplishment; Based; Renewal; Autoregressive; Conditional

9730062 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 economic 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 part. 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. This Accomplishment Based Renewal continues the study and application to financial microdata of an alternative to fixed interval analysis developed by the investigator. This approach is called Autoregressive Conditional Duration (ACD). Instead of selecting a fixed interval for analyzing the data, the interval between transactions becomes a 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 previous grant used the ACD model to analyze the price, volume and duration process of stock transactions. The timing of transactions, order submissions and quote revisions are the focus of new proposed econometric studies. From these analyses it is possible to infer key behavioral aspects of financial markets. The term liquidity is often used to describe effectively functioning markets. With more precise empirical measures of market quality such as the bid ask spread, the depth of the market and the price impact of trade, it is possible to examine the ebb and flow of market liquidity. This is clearly important for assessing the s tability of markets and for institutions seeking to trade large volumes. Using TORQ, TAQ, QEX options, S&P Futures and potentially other data sets, this project measures various dimensions of market liquidity, examines the time series behavior, forecastability, and economic determinants of these measures. ??

小行星9730062 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经济挑战，其中之一是选择适当的时间间隔，在此时间间隔内汇总数据，以生成一个数据集，其中观察间隔均匀。 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。 这种基于成就的更新继续研究和应用于金融微观数据的替代固定间隔分析的研究者开发的。 这种方法被称为自回归条件持续时间（ACD）。 而不是选择一个固定的时间间隔来分析数据，交易之间的时间间隔成为一个随机变量进行分析。 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。 上一次授予使用ACD模型来分析股票交易的价格、数量和持续时间过程。 交易的时间，订单提交和报价修改是新提出的计量经济学研究的重点。 从这些分析中可以推断出金融市场的关键行为方面。 流动性一词通常用来描述有效运作的市场。 有了更精确的市场质量实证指标，如买卖价差、市场深度和交易对价格的影响，就有可能考察市场流动性的涨落。 这对于评估市场的稳定性和寻求大量交易的机构显然很重要。 使用TORQ，TAQ，QEX期权，S P期货和其他潜在的数据集，该项目测量市场流动性的各个维度，研究这些措施的时间序列行为，可预测性和经济决定因素。 ??