- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
We use a Markov switching multifractal (MSM) volatility model to forecast crude oil return volatility. Not only can the model capture stylized facts of multiscaling, long memory, and structural breaks in volatility, it is also more parsimonious in parameterization, after allowing for hundreds of regimes in the volatility. Our in-sample results suggest that MSM models fit oil return data better than the traditional GARCH-class models. The out-ofsample results show that MSM models generate more accurate volatility forecasts than either popular GARCH-class models or the historical volatility model. © 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Multifractality (or multiscaling) is a well-known stylized fact in financial data. However, traditional volatility models such as GARCH-type models do not consider this stylized fact. In this paper, we use a newly developed multifractal Markov switching (MSM) volatility model to capture and forecast the dynamics of the crude oil return volatility. Based on Vuong’s (1989) closeness test, we find that the log-likelihoods of MSM models are significantly greater than those of the GARCH-class ones, implying that MSM models fit the oil returns data better.