Quasi-maximum exponential likelihood estimators for a double AR(p) model

Ke Zhu

32 citations0/0 files storedStatistica Sinica

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Financial Risk and Volatility Modeling Advanced Statistical Methods and Models Statistical and numerical algorithms

Abstract

The paper studies the quasi-maximum exponential likelihood estimator (QMELE) for the double AR(p) (DAR(p)) model: yt = ∑i=1p φiyt-1 + ηt√w+∑i=1pα iy2t-i, where {ηt} is a white noise sequence. Under a fractional moment of yt with Eη2t < ∞, strong consistency and asymptotic normality of the global QMELE are established. A formal comparison is given with the QMLE in Ling (2007) and WLADE in Chan and Peng (2005). A simulation study is carried out to compare the performance of these estimators in finite samples. An example on the exchange rate is given.

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References

1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1992ARCH modeling in finance 2003ASYMPTOTIC THEORY FOR A VECTOR ARMA-GARCH MODEL 2004Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes 1986Asymptotic Theory for ARCH Models: Estimation and Testing 2003GARCH processes: structure and estimation 2019GARCH Models: Structure, Statistical Inference and Financial Applications 2003Least absolute deviations estimation for ARCH and GARCH models

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2018Linear double autoregression 2015On Mixture Double Autoregressive Time Series Models 2019Strict stationarity testing and GLAD estimation of double autoregressive models

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