07350015%2E2012%2E707582
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To this end, we allow the individual un- conditional variances in conditional correlation generalized autoregressive conditional heteroscedasticity (CC-GARCH) models to change
The Gaussian pseudo-maximum likelihood (PML) estimators advocated by Bollerslev and Wooldridge ( 1992 ) among many others remain root- T consistent for the conditional variance
The Gaussian pseudo-maximum likelihood (PML) estimators advocated by Bollerslev and Wooldridge ( 1992 ) among many others remain root- T consistent for the conditional variance
The standard Gaussian QML estimator for generalized autoregressive conditional het- eroscedasticity (GARCH) is well known to be consistent and asymptotically Gaussian under
As a consequence, we propose bootstrap test for testing the instantaneous causality hypothesis when the unconditional covariance structure is time- varying. Evolution of the
This article proposes a novel stochastic volatility (SV) model that draws from the existing literature on autoregressive SV models, aggregation of autoregressive processes, and
We use both large- T asymptotic analysis and exact finite sample results to propose a procedure to reduce the bias of k -class estimators that works particularly well when the
“Studies of stock price volatility changes, Proceedings of the American Statistical Association.” Business and Economic Statistics Section p.. “Generalized Autoregressive Conditional
