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Temporal aggregation of volatility models
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DOI：10.1016/S0304-4076(03)00200-8.png)
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In this paper, we consider temporal aggregation of volatility models. We introduce semi-parametric volatility models, termed square-root stochastic autoregressive volatility (SR-SARV), which are characterized by autoregressive dynamics of the stochastic variance. Our class encompasses the usual GARCH models and various asymmetric GARCH models. Moreover, our stochastic volatility models arc characterized by multiperiod conditional moment restrictions in terms of observables. The SR-SARV class is a natural extension of the class of weak GARCH models. This extension has four advantages: (i) we do not assume that fourth moments are finite; (ii) we allow for asymmetries (skewness, leverage effect) that are excluded from weak GARCH models; (iii) we derive conditional moment restrictions and (iv) our framework allows us to Study temporal aggregation of GARCH models. (C) 2003 Elsevier B.V. All rights reserved.
Keyword:
GARCH
stochastic volatility
state-space
SR-SARV
temporal aggregation
asset returns
diffusion processes
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