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Multifractality of US Dollar/Deutsche Mark Exchange Rates - Studies2

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their distribution is usually specified Student t rather than Gaussian. In simulation, we assume<br />

independence for lack <strong>of</strong> an explicit alternative, and use the Gaussian for ut. These assumptions<br />

should not qualitatively affect our conclusions.<br />

rewrite<br />

where<br />

For FIGARCH(1,d,0), (12) can be written more intuitively. Starting with<br />

(1 − L) d ε 2 t = ω +[1− β1L] vt,<br />

σ 2 t =<br />

<br />

ω − β1vt−1 + 1 − (1 − L) d<br />

ε 2 t<br />

=<br />

∞<br />

ω − β1vt−1 − ψkε 2 t−k .<br />

ψk =<br />

k=1<br />

Γ(k − d)<br />

Γ(k +1)Γ(−d)<br />

The ARMA representation in volatility clarifies how simulation proceeds, and how long memory<br />

arises. (Plotting ψk will help readers gain further intuition for the process.) Because the Gamma<br />

function is smooth, the path <strong>of</strong> volatility is also smooth. This is the property that leads to clumping<br />

<strong>of</strong> like-sized returns.<br />

Like ARFIMA, FIGARCH is a hybrid from the scaling point <strong>of</strong> view. For ARFIMA, intuition<br />

is clear since it is asymptotically equivalent in increments to Fractional Gaussian Noise. Short<br />

memory components are non-scaling, but become negligible over long sampling increments. This<br />

intuition disappears for FIGARCH since squared increments are asymptotically scaling, not incre-<br />

ments themselves.<br />

An alternative intuition is the literature on diffusion limits <strong>of</strong> ARCH sequences. The continuous<br />

time limit <strong>of</strong> discrete GARCH sequences is an Itô diffusion or jump-diffusion in certain cases.<br />

(Nelson, 1990; Drost and Nijman, 1996). Intuitively, GARCH is not scaling since Itô diffusions are<br />

not scaling. 37 Continuous time limits <strong>of</strong> FIGARCH, on the other hand, are not known. 38<br />

Twenty simulated partition functions, appearing in Figure 19, are used to investigate the scaling<br />

properties <strong>of</strong> FIGARCH. Each simulations consists <strong>of</strong> 100,000 observations, allowing like comparison<br />

37<br />

A reference is not available, but intuitively, it results from smoothness <strong>of</strong> volatility.<br />

38<br />

Nelson (1990) notes that his method <strong>of</strong> demonstrating convergence to a continuous time diffusion does not apply<br />

to long memory processes.<br />

27

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