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138<br />

An important determ<strong>in</strong>ant <strong>of</strong> liquidity is the <strong>in</strong>side spread between the best ask and<br />

bid price. The bid-ask spread determ<strong>in</strong>es the cost <strong>of</strong> cross<strong>in</strong>g the market and thus<br />

the cost <strong>of</strong> utiliz<strong>in</strong>g market orders. Cohen et al. (1981) show that the existence <strong>of</strong> a<br />

bid-ask spread is a result <strong>of</strong> the “gravitational pull” <strong>of</strong> a limit order and is an<br />

equilibrium property <strong>of</strong> the market. Handa et al. (2003) demonstrate that the size <strong>of</strong><br />

the spread <strong>in</strong>creases with the degree <strong>of</strong> adverse selection and the difference <strong>in</strong><br />

valuation between low and <strong>high</strong> valuation <strong>in</strong>vestors. However, while these studies<br />

focus on the existence and properties <strong>of</strong> the spread, Foucault’s model provides<br />

testable implications regard<strong>in</strong>g the impact <strong>of</strong> the spread on the aggressiveness <strong>in</strong><br />

market trad<strong>in</strong>g and limit order trad<strong>in</strong>g, and this is formulated as Hypothesis (4):<br />

(4)The <strong>high</strong>er the bid-ask spread, the lower the aggressiveness <strong>in</strong> market trad<strong>in</strong>g<br />

and the <strong>high</strong>er the aggressiveness <strong>in</strong> limit order trad<strong>in</strong>g.<br />

These formulated hypotheses underp<strong>in</strong> the rationale for the construction <strong>of</strong><br />

appropriate explanatory variables <strong>in</strong> Sect. 5.<br />

3 The econometric approach<br />

A. D. Hall, N. Hautsch<br />

The arrival <strong>of</strong> aggressive market orders, limit orders and cancellations is modelled<br />

as a multivariate (f<strong>in</strong>ancial) po<strong>in</strong>t process. The econometric literature on the<br />

modell<strong>in</strong>g <strong>of</strong> f<strong>in</strong>ancial po<strong>in</strong>t processes was orig<strong>in</strong>ated by the sem<strong>in</strong>al paper by<br />

Engle and Russell (1998) who <strong>in</strong>troduced the class <strong>of</strong> autoregressive conditional<br />

duration (ACD) models. While this model was successfully applied to univariate<br />

duration processes, 5 it is not easily extended to a multivariate framework. The<br />

reason is that <strong>in</strong> a multivariate context the <strong>in</strong>dividual processes occur<br />

asynchronously, which is difficult to address <strong>in</strong> a discrete time duration model.<br />

A natural way to model multivariate po<strong>in</strong>t processes is to specify the<br />

(multivariate) <strong>in</strong>tensity function lead<strong>in</strong>g to a cont<strong>in</strong>uous-time framework. In this<br />

paper, we apply a six-dimensional version <strong>of</strong> the autoregressive conditional<br />

<strong>in</strong>tensity (ACI) model proposed by Russell (1999). 6 Follow<strong>in</strong>g the notation <strong>of</strong> Hall<br />

k<br />

and Hautsch (2004), let t denote the calendar time and def<strong>in</strong>e ti , k =1, ..., K, as the<br />

arrival times <strong>of</strong> a K-dimensional po<strong>in</strong>t process. Let N kðÞ¼ t<br />

P<br />

i 1 1 tk f i tg<br />

and<br />

M kðÞ¼ t<br />

P<br />

i 1 1 tk i

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