Score Driven exponentially Weighted Moving Average and Value-at-Risk Forecasting

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Tinbergen Institute Discussion Paper ; No. 14-092/IV/DSF77

Klassifikation und Themen

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

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Lucas, André ; Zhang, Xin
• Ereignis: Veröffentlichung
• (wer): Tinbergen Institute
• (wo): Amsterdam and Rotterdam
• (wann): 2014

Weitere Informationen

• Handle: http://hdl.handle.net/10419/107806
• Letzte Aktualisierung: 10.03.2025, 11:45 MEZ