Model Uncertainty, Thick Modelling and the Predictability of Stock Returns

By Marco Aiolfi, Carlo A. Favero. 2005

98 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2010, unknown): “8 In a variation of Pesaran and Timmermann (1995), Aiolfi and Favero (2005) use a type of combining approach, "thick" modeling, in forecasting the equity premium.”
Efficient market hypothesis and forecasting (2004, unknown): “In a very promising application of this approach adopted to monthly returns on US stocks, Aiolfi and Favero (2002) find that accounting for model uncertainty using this type of approach leads to substantial improvement in the asset allocation performance based on recursive predictions.”
Complete subset regressions (2013, methodology): “Papers such as Aiolfi and Favero (2003) consider equal-weighted combinations of forecasts of stock returns from all possible 2K models.”
Forecasting global stock market implied volatility indices (2018, unknown): “Forecasting literature states (i.e. Favero and Aiolfi, 2005, Samuels and Sekkel, 2013, Timmermann, 2006) that model-averaged forecasts provide incremental predictive gains compared to single models.”
Persistence in forecasting performance and conditional combination strategies (2006, background): “OW : ŷct+h,t = PK k=1 ω̂kt £ (ι0Nk/Nk)ŷ k t+h,t ¤ , where ω̂kt are least-squares estimates of the 10See also Aiolfi and Favero (2003) and Granger and Jeon (2004) who argue in favor of trimming the worst models followed by computation of a simple equal-weighted average of the remaining forecasts.”