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Semiparametric estimation for f<strong>in</strong>ancial durations 233<br />

family that we use. For the exponential distribution the l<strong>in</strong>k function is<br />

1<br />

θ =−<br />

ϕ � ψ(¯di−1, ¯yi−1; ϑ1), φ(t ′ �.<br />

i−1 ; ϑ2)<br />

S<strong>in</strong>ce under this distribution E � di| ¯di−1, ¯yi−1,t ′ � �<br />

i−1 =−θ−1 and V di| ¯di−1, ¯yi−1,<br />

t ′ �<br />

i−1 = θ 2 , Eq. (18) specifies the first order conditions for the maximization <strong>of</strong> the<br />

log-likelihood function for exponentially distributed random variables.<br />

In order to estimate the parameters <strong>of</strong> <strong>in</strong>terest we maximize the quasi-log-likelihood<br />

function<br />

Qn (d,ϕ) =<br />

n�<br />

i=1<br />

log q � di,ϕ � ψ(¯di−1, ¯yi−1; ϑ1), φ(t ′ ��<br />

i−1 ; ϑ2)<br />

with respect to ϑ1 and ϑ2. As already <strong>in</strong>dicated <strong>in</strong> the likelihood context, if the seasonal<br />

component is assumed to fall with<strong>in</strong> the class <strong>of</strong> known parametric function, φ(ti−1; ϑ2),<br />

the properties <strong>of</strong> the QML estimators <strong>of</strong> ϑ1 and ϑ2 are well known (see Engle and Russell<br />

(1998) and Engle (2000)). But if the seasonal component is approximated nonparametrically,<br />

then standard quasi-likelihood arguments do not hold. For the standard i.i.d. data<br />

case, Sever<strong>in</strong>i and Staniswalis (1994) and Fan et al. (1995) propose consistent estimators<br />

<strong>of</strong> both parametric and nonparametric parts. The statistical results presented <strong>in</strong> these<br />

papers do not apply directly <strong>in</strong> our case s<strong>in</strong>ce they assume <strong>in</strong>dependence <strong>of</strong> observations<br />

but at the end <strong>of</strong> the subsection equivalent statistical results are shown for the dependent<br />

case.<br />

As <strong>in</strong> the likelihood case, let us def<strong>in</strong>e ˆφϑ1 (t′ 0 ), for fixed values <strong>of</strong> ϑ1, as the solution<br />

to the follow<strong>in</strong>g (smoothed) optimization problem<br />

ˆφϑ1 (t′ 1<br />

0 ) = arg sup<br />

φ∈� nh<br />

n�<br />

K<br />

i=1<br />

�<br />

t ′<br />

0 − t ′ �<br />

i<br />

log q<br />

h<br />

� di,ϕ � ψ(¯di−1, ¯yi−1; ϑ1), φ ��<br />

for t ′ 0 ∈[to,tc]. Then ˆφϑ1 (t′ 0 ) must fulfill the follow<strong>in</strong>g first order condition<br />

1<br />

nh<br />

n�<br />

K<br />

i=1<br />

�<br />

t ′<br />

0 − t ′ �<br />

i ∂<br />

� �<br />

log q di,ϕ ψ(¯di−1, ¯yi−1; ϑ1), ˆφϑ1<br />

h ∂φ (t′ 0 )<br />

��<br />

= 0.<br />

The estimator <strong>of</strong>ϑ1 is obta<strong>in</strong>ed as the solution to the follow<strong>in</strong>g (unsmoothed) optimization<br />

problem<br />

ˆϑ1n = arg sup<br />

n�<br />

ϑ1∈�<br />

i=1<br />

� �<br />

log q di,ϕ ψ(¯di−1, ¯yi−1; ϑ1), ˆφϑ1 (t′ i−1 )<br />

��<br />

,<br />

and ˆϑ1n must fulfill the follow<strong>in</strong>g first order condition<br />

n�<br />

i=1<br />

∂<br />

∂ϑ1<br />

� �<br />

log q di,ϕ ψ(¯di−1, ¯yi−1; ˆϑ1n), ˆφ ˆϑ1n (t′ i−1 )<br />

��<br />

= 0.<br />

(19)

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