Forecasting growth during the Great Recession: is financial volatility the missing ingredient?

Ahn, 2006, Volatility relationship between stock performance and real output, Appl. Financ. Econ., 16, 777, 10.1080/09603100500424775 Andersen, 1998, Answering the skeptics: yes, standard volatility models do provide accurate forecasts, Int. Econ. Rev., 39, 885, 10.2307/2527343 Andreou, 2013, Should macroeconomic forecasters use daily financial data and how?, J. Bus. Econ. Stat., 31, 240, 10.1080/07350015.2013.767199 Angelini, 2011, Short-term forecasts of euro area GDP growth, Econ. J., 14, C25 Barhoumi, 2012, Monthly GDP forecasting using bridge models: application for the French economy, Bull. Econ. Res., 64, s53, 10.1111/j.1467-8586.2010.00359.x Barndorff-Nielsen, 2009, Realized kernels in practice: trades and quotes, Econ. J., 12, C1 Bellégo, 2012, Macro-financial linkages and business cycles: a factor-augmented probit approach, Econ. Model., 29, 1793, 10.1016/j.econmod.2012.05.033 Bollerslev, 1986, Generalized autoregressive conditional heteroskedasticity, J. Econ., 31, 307, 10.1016/0304-4076(86)90063-1 Carnero, 2012, Estimating GARCH volatility in the presence of outliers, Econ. Lett., 114, 86, 10.1016/j.econlet.2011.09.023 Charles, 2005, Outliers and GARCH models in financial data, Econ. Lett., 86, 347, 10.1016/j.econlet.2004.07.019 Chauvet, 2012 Chevillon, 2007, Direct multi-step estimation and forecasting, J. Econ. Surv., 21, 746, 10.1111/j.1467-6419.2007.00518.x Claessens, 2012, How do business and financial cycles interact?, J. Int. Econ., 87, 178, 10.1016/j.jinteco.2011.11.008 Clements, 2008, Macroeconomic forecasting with mixed-frequency data, J. Bus. Econ. Stat., 26, 546, 10.1198/073500108000000015 Creti, 2013, On the links between stock and commodity markets' volatility, Energy Econ., 37, 16, 10.1016/j.eneco.2013.01.005 Diron, 2008, Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data, J. Forecast., 27, 371, 10.1002/for.1067 Engle, 1982, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987, 10.2307/1912773 Ferrara, 2013, Financial variables as leading indicators of GDP growth: evidence from a MIDAS approach during the Great Recession, Appl. Econ. Lett., 20, 233, 10.1080/13504851.2012.689099 Ghysels, 2004 Ghysels, 2007, MIDAS regressions: further results and new directions, Econ. Rev., 26, 53, 10.1080/07474930600972467 Giannone, 2008, Nowcasting: the real-time informational content of macroeconomic data, J. Monet. Econ., 55, 665, 10.1016/j.jmoneco.2008.05.010 Hamilton, 2003, What is an oil shock?, J. Econ., 113, 363, 10.1016/S0304-4076(02)00207-5 Hamilton, 1996, Stock market volatility and the business cycle, J. Appl. Econ., 11, 573, 10.1002/(SICI)1099-1255(199609)11:5<573::AID-JAE413>3.0.CO;2-T Hotta, 1993, The effect of aggregation on prediction in autoregressive integrated moving-average models, J. Time Ser. Anal., 14, 261, 10.1111/j.1467-9892.1993.tb00143.x Kilian, 2008, The economic effects of energy price shocks, J. Econ. Lit., 46, 871, 10.1257/jel.46.4.871 Lütkepohl, 2010, Forecasting aggregated time series variables, OECD J.: J. Bus. Cycle Meas. Anal., 2, 1 Marcellino, 2010, Factor MIDAS for nowcasting and forecasting with ragged-edge data: a model comparison for German GDP, Oxf. Bull. Econ. Stat., 72, 518, 10.1111/j.1468-0084.2010.00591.x Simpson, 2001, Forecasting UK industrial production over the business cycle, J. Forecast., 20, 405, 10.1002/for.797 Stock, 2003, Forecasting output and inflation: the role of asset prices, J. Econ. Lit., 41, 788, 10.1257/jel.41.3.788 Timmermann, 2006, Forecast combinations, 10.1016/S1574-0706(05)01004-9