论文标题
财务持续时间建模的计量经济学
The Econometrics of Financial Duration Modeling
论文作者
论文摘要
我们为财务持续时间模型的估计和推断建立了新的结果，在给定时间范围内（例如交易日或一周）观察到事件。对于Engle and Russell（1998，Conementrica 66，1127-1162）的经典自回旋有条件持续时间（ACD）模型，我们表明，可能性估计器的较大样本行为对财务持续时间的尾巴行为非常敏感。特别是，即使在平稳性下，渐近正态性对于小于一个小于一个的尾巴索引分解，或等效地，当观察到的事件的聚类行为使得持续时间的无条件分布没有有限的平均值。取而代之的是，我们发现估计器是混合的高斯，并且具有非标准的收敛速率。结果是基于利用这样一个关键事实，即在持续时间数据中，任何给定时间跨度内的观测值都是随机的。我们的结果适用于一般计量经济学模型，其中观察到的事件是随机的。
We establish new results for estimation and inference in financial durations models, where events are observed over a given time span, such as a trading day, or a week. For the classical autoregressive conditional duration (ACD) models by Engle and Russell (1998, Econometrica 66, 1127-1162), we show that the large sample behavior of likelihood estimators is highly sensitive to the tail behavior of the financial durations. In particular, even under stationarity, asymptotic normality breaks down for tail indices smaller than one or, equivalently, when the clustering behaviour of the observed events is such that the unconditional distribution of the durations has no finite mean. Instead, we find that estimators are mixed Gaussian and have non-standard rates of convergence. The results are based on exploiting the crucial fact that for duration data the number of observations within any given time span is random. Our results apply to general econometric models where the number of observed events is random.
