Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/129725 
Erscheinungsjahr: 
2015
Schriftenreihe/Nr.: 
Sveriges Riksbank Working Paper Series No. 309
Verlag: 
Sveriges Riksbank, Stockholm
Zusammenfassung: 
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.
Schlagwörter: 
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
Dokumentart: 
Working Paper
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