Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/322158 
Year of Publication: 
2025
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2025-036/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
Point forecasts of score-driven models have been shown to behave at par with those of state-space models under a variety of circumstances. We show, however, that density rather than point forecasts of plain-vanilla score-driven models substantially underperform their state-space counterparts in a factor model context. We uncover the origins of this phenomenon and show how a simple adjustment of the measurement density of the score-driven model can put score-driven and state-space models approximately back on an equal footing again. The score-driven models can subsequently easily be extended with non-Gaussian features to fit the data even better without complicating parameter estimation. We illustrate our findings using a factor model for the implied volatility surface of S&P500 index options data.
Subjects: 
implied volatility surface dynamics
score-driven model
state-space model
dynamic factor model
density forecasting
JEL: 
C32
C38
Document Type: 
Working Paper

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