Relative forecasting performance of volatility models: Monte Carlo evidence

Thomas Lux and Leonardo Morales-Arias

No 1582, Kiel Working Papers from Kiel Institute for the World Economy (IfW Kiel)

Abstract: A 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.

Keywords: Monte Carlo simulations; volatility forecasting; long memory; multifractality; stochastic volatility; forecast combinations; Value-at-Risk (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifwkwp:1582

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