Continuous-time GARCH processes

Abstract

A family of continuous-time generalized autoregressive conditionally heteroscedastic processes, generalizing the $\mathrm{COGARCH}(1,1)$ process of Klüppelberg, Lindner and Maller [J. Appl. Probab. 41 (2004) 601--622], is introduced and studied. The resulting $\mathrm{COGARCH}(p,q)$ processes, $q\ge p\ge 1$, exhibit many of the characteristic features of observed financial time series, while their corresponding volatility and squared increment processes display a broader range of autocorrelation structures than those of the $\mathrm{COGARCH}(1,1)$ process. We establish sufficient conditions for the existence of a strictly stationary nonnegative solution of the equations for the volatility process and, under conditions which ensure the finiteness of the required moments, determine the autocorrelation functions of both the volatility and the squared increment processes. The volatility process is found to have the autocorrelation function of a continuous-time autoregressive moving average process.

Published at http://dx.doi.org/10.1214/105051606000000150 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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