M-ESTIMATION IN GARCH MODELS

K. Mukherjee

30 citations0/0 files storedEconometric Theory

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

Abstract

This paper derives asymptotic normality of a class ofM-estimators in the generalized autoregressive conditional heteroskedastic (GARCH) model. The class of estimators includes least absolute deviation and Huber's estimator in addition to the well-known quasi maximum likelihood estimator. For some estimators, the asymptotic normality results are obtained only under the existence of fractional unconditional moment assumption on the error distribution and some mild smoothness and moment assumptions on the score function.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 2006Analysis of Financial Time Series 2005Analysis of Financial Time Series 1994Asymptotic Theory for the Garch(1,1) Quasi-Maximum Likelihood Estimator 1992Stationarity of Garch processes and of some nonnegative time series 1986Asymptotic Theory for ARCH Models: Estimation and Testing

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