Journal of Financial Econometrics Vol. 2, No. 2, pp. 177-210
© 2004 Oxford University Press; all rights reserved.
LARCH, Leverage, and Long Memory
London School of Economics
Vilnius University and Institute of Mathematics and Informatics
London School of Economics
Institute of Mathematics and Informatics
Address correspondence to Donatas Surgailis, Institute of Mathematics and Informatics, Akademijos 4, 08663 Vilnius, Lithuania, or e-mail: sdonatas{at}ktl.mii.lt.
We consider the long-memory and leverage properties of a model for the conditional variance
of an observable stationary sequence Xt, where
is the square of an inhomogeneous linear combination of Xs, s < t, with square summable weights bj. This model, which we call linear autoregressive conditionally heteroskedastic (LARCH), specializes, when
depends only on Xt1, to the asymmetric ARCH model of Engle (1990, Review of Financial Studies 3, 103106), and, when
depends only on finitely many Xs, to a version of the quadratic ARCH model of Sentana (1995, Review of Economic Studies 62, 639661), these authors having discussed leverage potential in such models. The model that we consider was suggested by Robinson (1991, Journal of Econometrics 47, 6784), 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, 10021004), who showed that integer powers
,
2 can have long-memory autocorrelations. We establish conditions under which the cross-autocovariance function between volatility and levels,
, decays in the manner of moving average weights of long-memory processes on suitable choice of the bj. We also establish the leverage property that ht < 0 for 0 < t
k, where the value of k (which may be infinite) again depends on the bj. Conditions for finiteness of third and higher moments of Xt are also established.
KEYWORDS: leverage, linear ARCH, long-memory
Received April 1, 2002; revised August 25, 2003; accepted September 3, 2003
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