Search Results - subject:"moving average process"

The Correct Regularity Condition and Interpretation of Asymmetry in EGARCH

Chang, Chia-Lin; McAleer, Michael - 2017

of which was used by McAleer (2004) to obtain GJR. A random coefficient complex nonlinear moving average process was used …

Persistent link: https://www.econbiz.de/10011819449

The correct regularity condition and interpretation of asymmetry in EGARCH

Chang, Chia-Lin; McAleer, Michael - 2017

of which was used by McAleer (2004) to obtain GJR. A random coefficient complex nonlinear moving average process was used …

Persistent link: https://www.econbiz.de/10011688332

Forecasting Aggregated Time Series Variables: A Survey

Luetkepohl, Helmut - Department of Economics, European University Institute - 2009

Aggregated times series variables can be forecasted in different ways. For example, they may be forecasted on the basis of the aggregate series or forecasts of disaggregated variables may be obtained first and then these forecasts may be aggregated. A number of forecasts are presented and...

Persistent link: https://www.econbiz.de/10004980231

A One Line Derivation of EGARCH

McAleer, Michael; Hafner, Christian M. - 2014

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...

Persistent link: https://www.econbiz.de/10010377212

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