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dc.contributor.authorLux, Thomasen_US
dc.contributor.authorMorales-Arias, Leonardoen_US
dc.date.accessioned2010-02-17en_US
dc.date.accessioned2010-02-26T13:28:09Z-
dc.date.available2010-02-26T13:28:09Z-
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/10419/30039-
dc.description.abstractA Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under alternative data generating processes (DGPs). The models included in the MC study are the (Fractionally Integrated) Generalized Autoregressive Conditional Heteroskedasticity models ((FI)GARCH), the Stochastic Volatility model (SV) and the Markov-switching Multifractal model (MSM). The MC study enables to compare the relative forecasting performance of models, which account for different characterizations of the latent volatility process: specifications which incorporate short/long memory, autoregressive components, stochastic shocks, Markov-switching and multifractality. 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.en_US
dc.language.isoengen_US
dc.publisherKiel Institute for the World Economy (IfW) Kielen_US
dc.relation.ispartofseriesKiel working paper 1582en_US
dc.subject.jelC22en_US
dc.subject.jelC53en_US
dc.subject.ddc330en_US
dc.subject.keywordMonte Carlo simulationsen_US
dc.subject.keywordvolatility forecastingen_US
dc.subject.keywordlong memoryen_US
dc.subject.keywordmultifractalityen_US
dc.subject.keywordstochastic volatilityen_US
dc.subject.keywordforecast combinationsen_US
dc.subject.keywordValue-at-Risken_US
dc.titleRelative forecasting performance of volatility models: Monte Carlo evidenceen_US
dc.typeWorking Paperen_US
dc.identifier.ppn618751858en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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