Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/129725
Authors: 
Lucas, André
Zhang, Xin
Year of Publication: 
2015
Series/Report no.: 
Sveriges Riksbank Working Paper Series 309
Abstract: 
A simple methodology is presented for modeling time variation in volatilities and other higher-order moments using a recursive updating scheme similar to the familiar RiskMetricsTM approach. We update parameters using the score of the forecasting distribution. This allows the parameter dynamics to adapt automatically to any nonnormal data features and robusties the subsequent estimates. The new approach nests several of the earlier extensions to the exponentially weighted moving average (EWMA) scheme. In addition, it can easily be extended to higher dimensions and alternative forecasting distributions. The method is applied to Value-at-Risk forecasting with (skewed) Student's t distributions and a time-varying degrees of freedom and/or skewness parameter. We show that the new method is competitive to or better than earlier methods in forecasting volatility of individual stock returns and exchange rate returns.
Subjects: 
dynamic volatilities
dynamic higher-order moments
integrated generalized autoregressive score models
Exponentially Weighted Moving Average (EWMA)
Value-at-Risk (VaR)
JEL: 
C51
C52
C53
G15
Document Type: 
Working Paper

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