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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
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Ranking Leading Econometrics Journals Using Citations Data from ISI and RePEc
Chang, Chia-Lin; McAleer, Michael - In: Econometrics 1 (2013) 3, pp. 217-235
The paper focuses on the robustness of rankings of academic journal quality and research impact of 10 leading econometrics journals taken from the Thomson Reuters ISI Web of Science (ISI) Category of Economics, using citations data from ISI and the highly accessible Research Papers in Economics...
Persistent link: https://www.econbiz.de/10010711829
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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/10011031443
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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/10011031447
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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/10010785679
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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
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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
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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