QMLE of periodic time-varying bilinear– GARCH models

Abstract

In the current paper, we explore some necessary probabilistic properties for the asymptotic inference of a broad class of periodic bilinear– GARCH processes (P – BLGARCH) obtained by adding to the standard periodic GARCH models one or more interaction components between the observed series and its volatility process. In these models, the parameters of conditional variance are allowed to switch periodically between different regimes. This specification lead us to obtain a new model which is able to capture the asymmetry and hence leverage effect characterized by the negativity of the correlation between returns shocks and subsequent shocks in volatility patterns for seasonal financial time series. So, the goal here is to give in first part some basic structural properties of P – BLGARCH necessary for the remainder of the paper. In the second part, we study the consistency and the asymptotic normality of the quasi-maximum likelihood estimator (QMLE) illustrated by a Monte Carlo study and applied to model the exchange rate of the Algerian Dinar against the US-dollar.

Keywords:

• Periodic bilinear– GARCH model
• stationarity
• strong consistency
• asymptotic normality

JEL CLASSIFICATION:

• G10
• G11
• G12

Acknowledgements

We would like to thank Prof. N. Balakrishnan editor of the journal, for his attention, encouragement and valuable advice. Special thanks go to the referee for his useful suggestions and comments.