A One Line Derivation of EGARCH

Michael McAleer and Christian Hafner

Econometrics, 2014, vol. 2, issue 2, 1-6

Abstract: One of the most popular univariate asymmetric conditional volatility models is the exponential GARCH (or EGARCH) specification. In addition to asymmetry, which captures the different effects on conditional volatility of positive and negative effects of equal magnitude, EGARCH can also accommodate leverage, which is the negative correlation between returns shocks and subsequent shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator of the EGARCH parameters are not available under general conditions, but rather only for special cases under highly restrictive and unverifiable conditions. It is often argued heuristically that the reason for the lack of general statistical properties arises from the presence in the model of an absolute value of a function of the parameters, which does not permit analytical derivatives, and hence does not permit (quasi-) maximum likelihood estimation. It is shown in this paper for the non-leverage case that: (1) the EGARCH model can be derived from a random coefficient complex nonlinear moving average (RCCNMA) process; and (2) the reason for the lack of statistical properties of the estimators of EGARCH under general conditions is that the stationarity and invertibility conditions for the RCCNMA process are not known.

Keywords: leverage; asymmetry; existence; random coefficient models; complex non-linear moving average process (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (74)

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Related works:
Working Paper: A One Line Derivation of EGARCH (2014)
Working Paper: A One Line Derivation of EGARCH (2014)
Working Paper: A One Line Derivation of EGARCH (2014)
Working Paper: A One Line Derivation of EGARCH (2014)
Working Paper: A One Line Derivation of EGARCH (2014)
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