LARCH, Leverage, and Long Memory
We consider the long-memory and leverage properties of a model for the conditional variance V-sub-t-super-2 of an observable stationary sequence X-sub-t, where V-sub-t-super-2 is the square of an inhomogeneous linear combination of X-sub-s, s < t, with square summable weights b-sub-j. This model, which we call linear autoregressive conditionally heteroskedastic (LARCH), specializes, when V-sub-t-super-2 depends only on X-sub-t - 1, to the asymmetric ARCH model of Engle (1990, Review of Financial Studies 3, 103--106), and, when V-sub-t-super-2 depends only on finitely many X-sub-s, to a version of the quadratic ARCH model of Sentana (1995, Review of Economic Studies 62, 639--661), these authors having discussed leverage potential in such models. The model that we consider was suggested by Robinson (1991, Journal of Econometrics 47, 67--84), for use as a possibly long-memory conditionally heteroskedastic alternative to i.i.d. behavior, and further studied by Giraitis, Robinson and Surgailis (2000, Annals of Applied Probability 10, 1002--1004), who showed that integer powers X-sub-t-super-ℓ, ℓ ≥ 2 can have long-memory autocorrelations. We establish conditions under which the cross-autocovariance function between volatility and levels, h-sub-t = cov
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Volume (Year): 2 (2004)
Issue (Month): 2 ()
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