Previous research indicates that the maximum likelihood estimates of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models on foreign exchange rates, under various distributional assumptions, are sensitive to the presence of outliers. The advantage of the proposed Bounded Influence Estimator (BIE) is that it limits the influence of a small subset of data and is asymptotically normal. The BIE provides more consistent and robust estimates than Maximum Likelihood Estimator (MLE) and semi-parametric estimator, both of which tend to underestimate volatility persistence due to outliers. It is thus robust to outliers and model misspecification. Results of BIE estimates of GARCH models on the exchange rate series of five major currencies indicate that BIE offers an efficient mechanism for down-weighting outlying observations and is a competitive alternative to MLE.
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Document Type: Research Article
School of Economics and Management, Tsinghua University, Beijing 100084, ROC
Center for Policy Research and Department of Economics, Syracuse University, Syracuse, NY 13244-1090, USA
School of Global Management and Leadership, Arizona State University, Phoenix, Arizona, 85069-7100, USA
Publication date: 2010-04-01
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