Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets

C. Emre Alper (), Salih Fendoglu () and Burak Saltoğlu

MPRA Paper from University Library of Munich, Germany

Abstract: We explore the relative weekly stock market volatility forecasting performance of the linear univariate MIDAS regression model based on squared daily returns vis-a-vis the benchmark model of GARCH(1,1) for a set of four developed and ten emerging market economies. We first estimate the two models for the 2002-2007 period and compare their in-sample properties. Next we estimate the two models using the data on 2002-2005 period and then compare their out-of-sample forecasting performance for the 2006-2007 period, based on the corresponding mean squared prediction errors following the testing procedure suggested by West (2006). Our findings show that the MIDAS squared daily return regression model outperforms the GARCH model significantly in four of the emerging markets. Moreover, the GARCH model fails to outperform the MIDAS regression model in any of the emerging markets significantly. The results are slightly less conclusive for the developed economies. These results may imply superior performance of MIDAS in relatively more volatile environments.

Keywords: Mixed Data Sampling regression model; Conditional volatility forecasting; Emerging Markets (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 G10 (search for similar items in EconPapers)
Date: 2008-03
New Economics Papers: this item is included in nep-ecm, nep-fmk, nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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