Empirical Study of Intraday Option Price Changes using extended Count Regression Models

In this paper we model absolute price changes of an option on the
XETRA DAX index based on quote-by-quote data from the EUREX
exchange. In contrast to other authors, we focus on a
parameter-driven model for this purpose and use a Poisson
Generalized Linear Model (GLM) with a latent AR(1) process in the
mean, which accounts for autocorrelation and overdispersion in the
data. Parameter estimation is carried out by Markov Chain Monte
Carlo methods using the WinBUGS software. In a Bayesian context, we
prove the superiority of this modelling approach compared to an
ordinary Poisson-GLM and to a complex Poisson-GLM with
heterogeneous variance structure (but without taking into account
any autocorrelations) by using the deviance information criterion
(DIC) as proposed by Spiegelhalter et al. (2002). We include a
broad range of explanatory variables into our regression modelling
for which we also consider interaction effects: While, according to
our modelling results, the price development of the underlying, the
intrinsic value of the option at the time of the trade, the number
of new quotations between two price changes, the time between two
price changes and the Bid-Ask spread have significant effects on
the size of the price changes, this is not the case for the
remaining time to maturity of the option. By giving possible
interpretations of our modelling results we also provide an
empirical contribution to the understanding of the microstructure
of option markets.