Arbeitspapier
Relative forecasting performance of volatility models: Monte Carlo evidence
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Kiel Working Paper ; No. 1582
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Forecasting Models; Simulation Methods
- Thema
-
Monte Carlo simulations
volatility forecasting
long memory
multifractality
stochastic volatility
forecast combinations
Value-at-Risk
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Lux, Thomas
Morales-Arias, Leonardo
- Ereignis
-
Veröffentlichung
- (wer)
-
Kiel Institute for the World Economy (IfW)
- (wo)
-
Kiel
- (wann)
-
2010
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Lux, Thomas
- Morales-Arias, Leonardo
- Kiel Institute for the World Economy (IfW)
Entstanden
- 2010
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