| 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. <p>
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. <p>
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. |