The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach

Journal of Forecasting - Tập 32 Số 7 - Trang 600-612 - 2013
Hossein Asgharian1, Ai Jun Hou2, Farrukh Javed3
1Department of Economics Lund University and Knut Wicksell Center for Financial Studies Box 7082 S‐22007 Lund Sweden
2Department of Business and Economics; Southern Denmark University; Odense Denmark
3Department of Statistics, Lund University, Sweden

Tóm tắt

ABSTRACTThis paper applies the GARCH‐MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short‐term and long‐term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low‐frequency macroeconomic information in the GARCH‐MIDAS model improves the prediction ability of the model, particularly for the long‐term variance component. Moreover, the GARCH‐MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright © 2013 John Wiley & Sons, Ltd.

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Tài liệu tham khảo

AlperCE FendogluS SaltogluB.2008.Forecasting stock market volatilities using MIDAS regression: An application to the emerging markets.MPRA PaperNo. 7460.

10.2307/2527343

10.1162/rest.89.4.701

10.1016/j.jeconom.2010.01.004

BaiJ GhyselsE WrightJ.2009.State space models and MIDAS regression. Working paper NY Fed UNC and Johns Hopkins.

BrandtMW KangQ.2002.On the relationship between the conditional mean and volatility of stock returns: a latent VAR approach. Wharton School Philadelphia PA.

10.1108/eb022959

ChenX GhyselsE.2007.News – good or bad – and its impact on multiple horizons. Working paper University of North Carolina Chapel Hill NC.

10.1093/rfs/hhq071

10.1016/S0304-4076(03)00108-8

ClementsMP GalavaoAB.2006.Macroeconomic forecasting with mixed frequency data: forecasting US output growth and inflation.Warwick Economic Research PaperNo. 773 University of Warwick UK.

10.2307/1392185

10.1016/0304-4076(95)01737-2

Engle R, 1999, Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W. J. Granger, 475, 10.1093/oso/9780198296836.003.0020

EngleR GhyselsE SohnB.2012.On the economic sources of stock market volatility Review of Economics and Statistics. (forthcoming).

10.1093/jjfinec/nbl010

GhyselsE Santa‐ClaraP ValkanovR.2004.The MIDAS touch: mixed data sampling regression. Discussion paper University of North Carolina Chapel Hill NC and University of California Los Angeles CA.

10.1080/07474930600972467

GhyselsE SinkoA ValkanovR.2009.Granger causality tests with mixed data frequencies. Discussion paper University of North Carolina Chapel Hill NC.

10.1111/j.1540-6261.1993.tb05128.x

10.1016/0304-4076(94)90038-8

10.1002/(SICI)1099-1255(199609)11:5<573::AID-JAE413>3.0.CO;2-T

10.1198/073500105000000063

KotzeGL.2007.Forecasting inflation with high frequency asset price data. Working paper University of Stellenbosch.

10.1111/0022-1082.00246

10.1111/j.1540-6261.1989.tb02647.x

10.1198/016214502388618960

10.1198/073500102317351921

TayAS.2007.Mixed frequencies: Stock returns as a predictor of real output growth. Discussion paper Singapore Management University.

10.1111/j.1540-6261.1994.tb05150.x

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Journal of Forecasting

Tập 32 Số 7

600-612

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