A Note on Non‐Negative Arma Processes

Henghsiu Tsai () and K. S. Chan

Journal of Time Series Analysis, 2007, vol. 28, issue 3, 350-360

Abstract: Abstract. Recently, there has been much research on developing models suitable for analysing the volatility of a discrete‐time process. Since the volatility process, like many others, is necessarily non‐negative, there is a need to construct models for stationary processes which are non‐negative with probability one. Such models can be obtained by driving autoregressive moving average (ARMA) processes with non‐negative kernel by non‐negative white noise. This raises the problem of finding simple conditions under which an ARMA process with given coefficients has a non‐negative kernel. In this article, we derive a necessary and sufficient condition. This condition is in terms of the generating function of the ARMA kernel which has a simple form. Moreover, we derive some readily verifiable necessary and sufficient conditions for some ARMA processes to be non‐negative almost surely.

Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:28:y:2007:i:3:p:350-360

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