What does financial volatility tell us about macroeconomic fluctuations?

Chauvet, Marcelle and Senyuz, Zeynep and Yoldas, Emre (2010): What does financial volatility tell us about macroeconomic fluctuations?

Abstract

This paper provides an extensive analysis of the predictive ability of financial volatility measures for economic activity. We construct monthly measures of aggregated and industry-level stock volatility, and bond market volatility from daily returns. We model log financial volatility as composed of a long-run component that is common across all series, and a short-run component. If volatility has components, volatility proxies are characterized by large measurement error, which veils analysis of their fundamental information and relationship with the economy. We find that there are substantial gains from using the long term component of the volatility measures for linearly projecting future economic activity, as well as for forecasting business cycle turning points. When we allow for asymmetry in the long-run volatility component, we find that it provides early signals of upcoming recessions. In a real-time out-of-sample analysis of the last recession, we find that these signals are concomitant with the first signs of distress in the financial markets due to problems in the housing sector around mid-2007 and the implied chronology is consistent with the crisis timeline.

• Item Type: MPRA Paper
• Language: English
• Keywords: Realized Volatility, Business Cycles, Forecasting, Dynamic Factor Models, Markov Switching
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy

References

Adrian, T. and J. Rosenberg, 2008, “Stock Returns and Volatility: Pricing the Long-Run and Short-Run Components of Market Risk,” The Journal of Finance, 63, 2997-3030.

Alizadeh, S., M.W. Brandt, F.X. Diebold, 2002, “Range-Based Estimation of Stochastic Volatility Models,” The Journal of Finance, LVII(3), 1047-1091.

Andersen, T.G., T. Bollerslev, F.X. Diebold and H. Ebens, 2001a, “The Distribution of Realized Stock Return Volatility,” Journal of Financial Economics, 61, 43-76.

Andersen, T.G., T. Bollerslev, F.X. Diebold and P. Labys, 2001b, “The Distribution of Realized Exchange Rate Volatility,” Journal of the American Statistical Association, 96, 42-55.

Andersen, T. G., T. Bollerslev, F.X. Diebold and P. Labys, 2003, “Modeling and Forecasting Realized Volatility,” Econometrica, 71, 579-625.

Andersen, T., T. Bollerslev, F.X. Diebold, and P. Labys, 2000, “Great Realizations,” Risk, March, 105-108.

Andersen, T.G., T. Bollerslev, F.X. Diebold, and J. Wu, 2005, “A Framework for Exploring the Macroeconomic Determinants of Systematic Risk,” American Economic Review, 95, 398-404.

Andersen, T.G., T. Bollerslev, and F.X. Diebold, 2010, “Parametric and Nonparametric Volatility Measurement,” in Y. Ait-Sahalia and L.P. Hansen (Eds.): Handbook of Financial Econometrics, 67-138. Amsterdam: North Holland.

Andreou, E., E. Ghysels, and A. Kourtellos, 2010, “Should Macroeconomic Forecasters Use Daily Financial Data and How?,” Working Paper., Duke University.

Andreou, E., D.R. Osborn, and M. Sensier, 2000, “A Comparison of the Statistical Properties of Financial Variables in the USA, UK and Germany over the Business Cycle,” The Manchester School, 68, 396-418.

Basak, S. and D. Cuoco, 1998, “An Equilibrium Model with Restricted Stock Market Participation,” Review of Financial Studies, 11, 309-341.

Barndorff-Nielsen, O.E. and N. Shephard, 2002a, “Econometric Analysis of Realised Volatility and its Use in Estimating Stochastic Volatility Models”, Journal of the Royal Statistical Society, Series B, 64, 253-280.

Barndorff-Nielsen, O.E. and N. Shephard, 2002b, “Estimating Quadratic Variation Using Realised Variance”, Journal of Applied Econometrics, 17, 457-477.

Bernanke, B.S., M. Gertler, and S. Gilchrist, 1999, “The Financial Accelerator in a Quantitative Business Cycle Framework," in J.B. Taylor and M. Woodford (Eds.): Handbook of Macroeconomics, Vol. 1C, 1341-1393. Elsevier.

Bloom, N., 2009, “The Impact of Uncertainty Shocks,” Econometrica, 77(3), 623-685. Bollerslev, T., and H. Zhou, 2002, “Estimating Stochastic Volatility Diffusion using Conditional Moments of Integrated Volatility,” Journal of Econometrics, 109, 33-65.

Campbell., J., M. Lettau, B.G. Malkiel, and Y. Xu, 2001, “Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk,” The Journal of Finance, 56, 1-44.

Campbell, J.Y. and J. Cochrane, 1999, “By Force of Habit: A Consumption-based Explanation of Aggregate Stock Market behavior,” Journal of Political Economy, 107, 205–251.

Chacko, G., and L. Viceira, 2003, “Spectral GMM estimation of Continuous-Time Processes,” Journal of Econometrics 116, pp. 259-292.

Chauvet, M. 1998, “An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching,” International Economic Review 39, 969-996.

Chauvet, M., 1998/1999, “Stock Market Fluctuations and the Business Cycle,” Journal of Economic and Social Measurement, 25, 235-258.

Chauvet, M. and S. Potter, 2000, “Coincident and Leading Indicators of the Stock Market,” Journal of Empirical Finance, 7(1), 87-111.

Chauvet, M. and S. Potter, 2001, “Nonlinear Risk,” Macroeconomic Dynamics, 5(4), 621-646.

Chauvet, M. and J.D. Hamilton, 2006, “Dating Business Cycle Turning Points in Real Time,” “Nonlinear Time Series Analysis of Business Cycles,” ed. Van Dijk, Milas, and Rothman, Elsevier’s Contributions to Economic Analysis series, 1-54.

Chauvet, M. and J. Piger, 2008, “A Comparison of the Real-Time Performance of Business Cycle Dating Methods,” Journal of Business Economics and Statistics, 26(1), 42-49.

Chernov, M., A.R. Gallant, E. Ghysels, and G. Tauchen, 2003, Alternative Models for Stock Price Dynamics, Journal of Econometrics, 116, 225-257.

David, A., and P. Veronesi, 2009, Inflation and Earnings Uncertainty and Volatility Forecasts, Working Paper. Ding, Z. and C.W. Granger, 1996, Volatility Persistence of Speculative Returns: A new approach, Journal of Econometrics, 73, 185-215.

Engle, R.F., E. Ghysels, and B. Sohn, 2008, “On the Economic Sources of Stock Market Volatility,” mimeo, New York University.

Engle, R.F. and J.G. Rangel, 2008, “The Spline GARCH Model for Low Frequency Volatility and its Macroeconomic Causes,” Review of Financial Studies, 21, 1187-1222.

Engle, R.F. and G.G.J. Lee, 1999, “A Permanent and Transitory Component Model of Stock Return Volatility,” in R.F. Engle and H. White (eds.), Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W.J. Granger, 475-497. Oxford, UK: Oxford University Press.

Estrella, A. and F.S. Mishkin, 1998, “Predicting U.S. recessions: Financial Variables as Leading Indicators,” The Review of Economics and Statistics, 80, 45-61.

Estrella, A. and G. Hardouvelis, 1991, “The Term Structure as a Predictor of Real Economic Activity,” The Journal of Finance, 46, 555-576.

Fama, E.F. and K.R. French, 1989, “Business Conditions and Expected Returns on Stock and Bonds,” Journal of Financial Economics, 25, 23-49.

Fornari, F. and A. Mele, 2009, “Financial Volatility and Economic Activity,” mimeo, London School of Economics. French, K.R., G.W. Schwert, and R.F. Stambaugh, 1987, “Expected Stock Returns and Volatility,” Journal of Financial Economics, 19, 3-29.

Gallant, R. C.-T. Hsu, and G. Tauchen, 1999, ‘‘Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance,’’ Review of Economics and Statistics, 81, 617-631.

Granger, C.W.J., 1969, “Prediction with a Generalized Cost of Error Function,” Operational Research Quarterly, 20, 199–207.

Gurkaynak, R.S., B. Sack, and J.H. Wright, 2007, “The U.S. Treasury Yield Curve: 1961 to the Present,” Journal of Monetary Economics, 24(8), 2291-2304.

Hamilton, J.D., 1989, “A New Approach to the Economic Analysis of Nonstationary Time Series and Business Cycles,” Econometrica, 57, 357-384.

Hamilton, J.D., and G. Lin, 1996, “Stock Market Volatility and the Business Cycle,” Journal of Applied Econometrics, 11, 574-593.

Hansen, P.R., 2005, “A Test for Superior Predictive Ability,” Journal of Business and Economic Statistics, 23(4), 365-380.

Kim, C.-J., 1994, “Dynamic Linear Models with Markov-Switching,” Journal of Econometrics, 60, 1-22. Maheu, J.M. and T.H. McCurdy, 2000, “Identifying Bull and Bear Markets in Stock Returns,” Journal of Business and Economic Statistics, 18(1), 100-112.

Mele, A., 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 NYSE,” Journal of Business, 46(3), 434-453.

Perez-Quiros, G., Timmermann A., 1995, “Variations in the Mean and Volatility of Stock Returns around Turning Points of the Business Cycle,” In: Forecasting Volatility in the Financial Markets, Knight J., Satchell S. (eds.). Butterworth-Heinemann: Oxford.

Poterba, J.M. and L. Summers, 1986, “The Persistence of Volatility and Stock Market Fluctuations,” The American Economic Review, 76, 1142-1151.

Senyuz, Z., 2010, “Factor Analysis of Permanent and Transitory Components of U.S. Economy and the Stock Market,” Journal of Applied Econometrics, forthcoming.

Schwert, G.W., 1989a, “Business Cycles, Financial Crises and Stock Volatility,” Carnegie-Rochester Conference Series on Public Policy, 31, 83-125.

Schwert, G.W., 1989b, “Why Does Stock Market Volatility Change Over Time?”, The Journal of Finance, 44, 1115-1153.

Whitelaw, R., 1994, “Time Variations and Covariations in the Expectation and Volatility of Stock Market Return,” The Journal of Finance, 49, 515-541.