Autoregressive Lag Length Selection Criteria in the Presence of ARCH Errors
AbstractWe study the effects of ARCH errors on the performance of the commonly used lag length selection criteria. The most important finding of this study is that SIC, FPE, HQC and BIC perform considerably well in estimating the true autoregressive lag length, even in the presence of ARCH errors. Thus, we conclude that these criteria are applicable to empirical data such as stock market returns and exchange rate volatility that exhibit ARCH effects.
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Bibliographic InfoArticle provided by AccessEcon in its journal Economics Bulletin.
Volume (Year): 3 (2005)
Issue (Month): 19 ()
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- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
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- Ahmed Bensaida, 2012. "Improving the Forecasting Power of Volatility Models," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 2(3), pages 51-64, July.
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