A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?

By P. R. Hansen, Asger Lunde. 2005

1,831 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b629dd83c52dae717b9f2519732de8177b685de1

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Citations in the corpus

Data-based ranking of realised volatility estimators (2011, unknown): “L Yt; RV (daily) t L Yt; RV (5min) t = 33:54 (6:69) + et (32) L Yt; RV (daily) t L Yt; RV (5min) t = 19:93 (5:75) + 27:76 (3:45) Zt 1 + et (33)”
Forecasting volatility of SSEC in Chinese stock market using multifractal analysis (2008, methodology): “[ 7 ,10,15]. In summary, the p-value of a SPA test indicates the relative performance for a base model (M0) to an alternative model (Mk).”
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models (2016, unknown): “As a result, large-scale comparative analyses such as the one in Hansen and Lunde (2005) often exclude parameter-driven models.”
Forecasting Stock Market Volatility with Regime-Switching GARCH Models (2005, methodology): “Hansen and Lunde (2005) obtain similar results with stock market data, finding that the GARCH(1,1) specification is not the best model (in terms of SPA) when compared to other single-regime specifications.”
Energy markets volatility modelling using GARCH (2014, unknown): “We choose a GARCH (1,1) formulation for all univariate models, because it has been found to yield the best performance compared to other GARCH lag configurations, under the most general conditions [see Hansen and Lunde (2005)].”
Shipping markets in turmoil: An analysis of the Covid-19 outbreak and its implications (2020, unknown): “As in other studies in the literature, we limit the scope of the estimation to the GARCH (1,1) which has been shown to perform well in previous applications (see Andersen and Bollerslev, 1998; Javaheri et al., 2004; Hansen and Lunde, 2005; Gazola et al., 2008; Gazola et al., 2008; Michail, 2019).”
Evaluating Volatility and Correlation Forecasts (2009, background): “Hansen and Lunde use this type of test to determine whether a GARCH(1,1) model was out-performed by any one of over three hundred competing volatility forecasts, for IBM equity return volatility and for the volatility of the log-difference of the DM-USD exchange rate [35].”