Umeå Economic Studies, Department of Economics, Umeå University
No 656:
Modelling High Frequency Financial Count Data
Shahiduzzaman Quoreshi ()
Abstract: This thesis comprises two papers concerning modelling of
financial count data. The papers advance the integer-valued moving average
model (INMA), a special case of integer-valued autoregressive moving
average (INARMA) model class, and apply the models to the number of stock
transactions in intra-day data.
Paper [1] advances the INMA model to
model the number of transactions in stocks in intra-day data. The
conditional mean and variance properties are discussed and model extensions
to include, e.g., explanatory variables are offered. Least squares and
generalized method of moment estimators are presented. In a small Monte
Carlo study a feasible least squares estimator comes out as the best
choice. Empirically we find support for the use of long-lag moving average
models in a Swedish stock series. There is evidence of asymmetric effects
of news about prices on the number of transactions.
Paper [2]
introduces a bivariate integer-valued moving average model (BINMA) and
applies the BINMA model to the number of stock transactions in intra-day
data. The BINMA model allows for both positive and negative correlations
between the count data series. The study shows that the correlation between
series in the BINMA model is always smaller than 1 in an absolute sense.
The conditional mean, variance and covariance are given. Model extensions
to include explanatory variables are suggested. Using the BINMA model for
AstraZeneca and Ericsson B it is found that there is positive correlation
between the stock transactions series. Empirically, we find support for the
use of long-lag bivariate moving average models for the two series.
Keywords: Count data; Intra-day; High frequency; Time series; Estimation; Long memory; Finance; (follow links to similar papers)
JEL-Codes: C13; C22; C25; C51; G12; G14; (follow links to similar papers)
13 pages, April 20, 2005
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