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A simple test for causality in volatility

Chang, Chia-Lin; McAleer, Michael - In: Econometrics 5 (2017) 1, pp. 1-5

An early development in testing for causality (technically, Granger non-causality) in the conditional variance (or volatility) associated with financial returns was the portmanteau statistic for non-causality in the variance of Cheng and Ng (1996). A subsequent development was the Lagrange...

Persistent link: https://www.econbiz.de/10011755368

Asymmetry and leverage in conditional volatility models

McAleer, Michael - In: Econometrics 2 (2014) 3, pp. 145-150

The three most popular univariate conditional volatility models are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or...

Persistent link: https://www.econbiz.de/10010421299

A one line derivation of EGARCH

McAleer, Michael; Hafner, Christian M. - In: Econometrics 2 (2014) 2, pp. 92-97

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 … (2) the reason for the lack of statistical properties of the estimators of EGARCH under general conditions is that the …

Persistent link: https://www.econbiz.de/10010421302

Ten things you should know about the dynamic conditional correlation representation

Caporin, Massimiliano; McAleer, Michael - In: Econometrics 1 (2013) 1, pp. 115-126

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the...

Persistent link: https://www.econbiz.de/10010421297