Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility

Lindblad, Annika (2017): Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility.

Abstract

I use the GARCH-MIDAS framework of Engle et al. (2013) to examine the relationship between the macro economy and stock market volatility, focusing on the role played by survey-based sentiment indicators compared to macroeconomic variables. I find that once the information in sentiment indicators is controlled for, backward-looking macroeconomic data does not include useful information for predicting stock return volatility. On the other hand, forward-looking macroeconomic variables remain useful for forecasting stock market volatility after sentiment data is taken into account. The term spread is the best predictor for stock return volatility over long horizons.

• Item Type: MPRA Paper
• Language: English
• Keywords: stock market volatility, volatility components, MIDAS, survey data, macro finance link
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation

References

Andersen, Torben G., Tim Bollerslev, Peter F. Christoffersen, and Francis X. Diebold (2006) ‘Volatility and correlation forecasting.’ Handbook of Economic Forecasting 1, 777–878.

Andreou, Elena, Eric Ghysels, and Andros Kourtellos (2010) ‘Regression models with mixed sampling frequencies.’ Journal of Econometrics 158, 246–261.

Arnold, Ivo J., and Evert B. Vrugt (2008) ‘Fundamental uncertainty and stock market volatility.’Applied Financial Economics 18, 1425–1440.

Asgharian, Hossein, Ai Jun Hou, and Farrukh Javed (2013) ‘The importance of the macroeconomic variables in forecasting stock return variance: a garch-midas approach.’ Journal of Forecasting 32, 600–612.

Asgharian, Hossein, Charlotte Christiansen, and Ai Jun Hou (2015) ‘Effects of macroeconomic uncertainty on the stock and bond markets.’ Finance Research Letters 13, 10–16.

Awartani, Basel M. A., and Valentina Corradi (2005) ‘Predicting the volatility of the s&p-500 stock index via garch models: the role of asymmetries.’ International Journal of Forecasting 21, 167–183.

Bansal, Ravi, and Amir Yaron (2004) ‘Risks for the long run: a potential resolution of asset pricing puzzles.’ Journal of Finance 59, 1481–1509.

Barsky, Robert B., and Eric R. Sims (2012) ‘Information, animal spirits, and the meaning of innovations in consumer confidence.’ American Economic Review 102(4), 1343–1377.

Black, Fischer (1976) ‘Studies of stock market volatility changes.’ Proceedings of the Business and Economic Statistics Section, Americal Statistical Association pp. 177–181.

Campbell, John Y. (1991) ‘A variance decomposition for stock returns.’ Economic Journal 101, 157–179.

Campbell, John Y., and Robert J. Shiller (1988) ‘The dividend-price ratio and expectations of future dividends and discount factors.’ Review of Financial Studies 1, 195–228.

Campbell, Sean D., and Francis X. Diebold (2009) ‘Stock returns and expected business conditions: Half a century of direct evidence.’ Journal of Business & Economic Statistics 27(2), 266–278.

Christiansen, Charlotte, Maik Schmeling, and Andreas Schrimpf (2012) ‘A comprehensive look at financial volatility prediction by economic variables.’ Journal of Applied Econometrics 27(6), 956–977.

Conrad, Christian, and Karen Loch (2014) ‘Anticipating long-term stock market volatility.’Journal of Applied Econometrics 30(7), 1090–1114.

Davis, Nicole, and Ali M. Kutan (2003) ‘Inflation and output as predictors of stock returns and volatility: international evidence.’ Applied Financial Economics 13(9), 693–700.

Diebold, Francis X., and Kamil Yilmaz (2008) ‘Macroeconomic volatility and stock market volatility, world-wide.’ NBER Working Paper

Ding, Zhuanxin, Clive W.J. Granger, and Robert F. Engle (1993) ‘A long memory property of stock market returns and a new model.’ Journal of empirical finance 1, 83–106.

Engle, Robert F., and Jose G. Rangel (2008) ‘The spline-garch model for low frequency volatility and its global macroeconomic causes.’ Review of Financial Studies 21, 1187–1222.

Engle, Robert F., Eric Ghysels, and Bumjean Sohn (2013) ‘Stock market volatility and macroeconomic fundamentals.’ The Review of Economics and Statistics 95(3), 776–797.

Errunza, Vihang, and Ked Hogan (1998) ‘Macroeconomic determinants of European stock market volatility.’ European Financial Management 4(3), 361–377.

Fama, Eugene F., and Kenneth R. French (1989) ‘Business conditions and expected returns on stock and bonds.’ Journal of Financial Economics 25, 23–49.

Ghysels, Eric, Arthur Sinko, and Rossen Valkanov (2007) ‘MIDAS regressions: Further results and new directions.’ Econometric Reviews 26(1), 53–90.

Ghysels, Eric, Pedro Santa-Clara, and Rossen Valkanov (2004) ‘The midas touch: Mixed data sampling regression models.’ Finance.

Ghysels, Eric, Pedro Santa-Clara, and Rossen Valkanov (2005) ‘There is a risk-return tradeoff after all.’ Journal of Financial Economics (76), 509–548.

Ghysels, Eric, Pedro Santa-Clara, and Rossen Valkanov (2006) ‘Predicting volatility: getting the most out of return data sampled at different frequencies.’ Journal of Econometrics 131(1-2), 59–95.

Giacomini, Raffaella, and Halbert White (2006) ‘Tests of conditional predictive ability.’Econometrica 74, 1545–1578.

Glosten, Lawrence R., Ravi Jagannathan, and David E. Runkle (1993) ‘On the relation between the expected value and the volatility of the nominal excess return on stocks.’ The Journal of Finance 48(5), 1779–1801.

Hamilton, James D., and Gang Lin (1996) ‘Stock market volatility and the business cycle.’Journal of Applied Econometrics 11(5), 573–593.

Hansen, Peter R., Asger Lunde, and James M. Nason (2011) ‘The model confidence set.’Econometrica 79(2), 453–497.

Mele, Antonio (2007) ‘Asymmetric stock market volatility and the cyclical behavior of expected returns.’ Journal of Financial Economics 86, 446–478.

Officer, R.R. (1973) ‘The variability of the market factor of the new york stock exchange.’ Journal of Business 46, 434–453.

Patton, Andrew J. (2011) ‘Volatility forecast comparison using imperfect volatility proxies.’Journal of Econometrics 160, 246–256.

Paye, Bradley S. (2012) ‘Deja vol: Predictive regressions for aggregate stock market volatility using macroeconomic variables.’ Journal of Financial Economics 106, 527–546.

Pierdzioch, Christian, Jorg Dopke, and Daniel Hartmann (2008) ‘Forecasting stock market volatility with macroeconomic variables in real time.’ Journal of Economics and Business 60, 256–276.

Poon, Ser-Huang, and Clive W.J. Granger (2003) ‘Forecasting volatility in financial markets: A review.’ Journal of Economic Literature 41, 478–539.

Romer, C. (1990) ‘The great crash and the onset of the great depression.’ The Quarterly Journal of Economics 105, 597–624.

Schwert, William G. (1989a) ‘Business cycles, financial crises, and stock volatility.’ Carnegie-Rochester Conference Series on Public Policy 31, 83–126.

Schwert, William G. (1989b) ‘Why does stock market volatility change over time?’ Journal of Finance 44, 1115–1153.

Taylor, Stephen J. (1986) Modelling financial time series (New York: John Wiley & Sons)

Veronesi, P. (1999) ‘Stock market overreaction to bad news in good times: a rational expectations equilibrium model.’ Review of Financial Studies 12, 975–1007.

Wang, Fangfang, and Eric Ghysels (2015) ‘Econometric analysis of volatility component models.’ Econometric Theory 31, 362–393.