Publication
Jan 2010
A Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under alternative data generating processes (DGPs). Forecasts are evaluated by means of Mean Squared Errors (MSE), Mean Absolute Errors (MAE) and Value-at-Risk (VaR) diagnostics. Furthermore, complementarities between models are explored via forecast combinations. The results show that (i) the MSM model best forecasts volatility under any other alternative characterization of the latent volatility process and (ii) forecast combinations provide a systematic improvement upon forecasts of single models.
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English (PDF, 37 pages, 1.0 MB) |
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Author | Thomas Lux, Leonardo Morales-Arias |
Series | Kiel Institute Working Papers |
Issue | 1582 |
Publisher | Kiel Institute for the World Economy |
Copyright | © 2010 Kiel Institute for the World Economy |