Score driven exponentially weighted moving averages and value-at-risk forecasting

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Sveriges Riksbank Working Paper Series ; No. 309

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Model Construction and Estimation ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods ; International Financial Markets
• Thema: dynamic volatilities ; dynamic higher-order moments ; integrated generalized autoregressive score models ; Exponentially Weighted Moving Average (EWMA) ; Value-at-Risk (VaR)

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Lucas, André ; Zhang, Xin
• Ereignis: Veröffentlichung
• (wer): Sveriges Riksbank
• (wo): Stockholm
• (wann): 2015

Weitere Informationen

• Handle: http://hdl.handle.net/10419/129725
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ