Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/107806
Authors: 
Lucas, André
Zhang, Xin
Year of Publication: 
2014
Series/Report no.: 
Tinbergen Institute Discussion Paper 14-092/IV/DSF77
Abstract: 
We present a simple new methodology to allow for time variation in volatilities using a recursive updating scheme similar to the familiar RiskMetrics approach. We update parameters using the score of the forecasting distribution rather than squared lagged observations. This allows the parameter dynamics to adapt automatically to any non-normal data features and robustifies the subsequent volatility estimates. Our new approach nests several extensions to the exponentially weighted moving average (EWMA) scheme as proposed earlier. Our approach also easily handles extensions to dynamic higher-order moments or other choices of the preferred forecasting distribution. We apply our method to Value-at-Risk forecasting with Student's t distributions and a time varying degrees of freedom parameter and show that the new method is competitive to or better than earlier methods for volatility forecasting of individual stock returns and exchange rates.
Subjects: 
dynamic volatilities
time varying higher order moments
integrated generalized autoregressive score models
Exponential Weighted Moving Average (EWMA)
Value-at-Risk (VaR)
JEL: 
C51
C52
C53
G15
Document Type: 
Working Paper

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