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Identifying long-run risks: a bayesian mixed-frequency approach

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  • Frank Schorfheide
  • Dongho Song
  • Amir Yaron

Abstract

We develop a nonlinear state-space model that captures the joint dynamics of consumption, dividend growth, and asset returns. Building on Bansal and Yaron (2004), our model consists of an economy containing a common predictable component for consumption and dividend growth and multiple stochastic volatility processes. The estimation is based on annual consumption data from 1929 to 1959, monthly consumption data after 1959, and monthly asset return data throughout. We maximize the span of the sample to recover the predictable component and use high-frequency data, whenever available, to efficiently identify the volatility processes. Our Bayesian estimation provides strong evidence for a small predictable component in consumption growth (even if asset return data are omitted from the estimation). Three independent volatility processes capture different frequency dynamics; our measurement error specification implies that consumption is measured much more precisely at an annual than monthly frequency; and the estimated model is able to capture key asset-pricing facts of the data.

Suggested Citation

  • Frank Schorfheide & Dongho Song & Amir Yaron, 2013. "Identifying long-run risks: a bayesian mixed-frequency approach," Working Papers 13-39, Federal Reserve Bank of Philadelphia.
  • Handle: RePEc:fip:fedpwp:13-39
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    References listed on IDEAS

    as
    1. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    2. Ravi Bansal & Amir Yaron, 2004. "Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles," Journal of Finance, American Finance Association, vol. 59(4), pages 1481-1509, August.
    3. Beeler, Jason & Campbell, John Y., 2012. "The Long-Run Risks Model and Aggregate Asset Prices: An Empirical Assessment," Critical Finance Review, now publishers, vol. 1(1), pages 141-182, January.
    4. Hall, Robert E, 1988. "Intertemporal Substitution in Consumption," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 339-357, April.
    5. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    6. Wilcox, David W, 1992. "The Construction of U.S. Consumption Data: Some Facts and Their Implications for Empirical Work," American Economic Review, American Economic Association, vol. 82(4), pages 922-941, September.
    7. Rui Albuquerque & Martin Eichenbaum & Victor Xi Luo & Sergio Rebelo, 2016. "Valuation Risk and Asset Pricing," Journal of Finance, American Finance Association, vol. 71(6), pages 2861-2904, December.
    8. Robert J. Barro, 2009. "Rare Disasters, Asset Prices, and Welfare Costs," American Economic Review, American Economic Association, vol. 99(1), pages 243-264, March.
    9. Chernov, Mikhail & Ronald Gallant, A. & Ghysels, Eric & Tauchen, George, 2003. "Alternative models for stock price dynamics," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 225-257.
    10. Stambaugh, Robert F., 1999. "Predictive regressions," Journal of Financial Economics, Elsevier, vol. 54(3), pages 375-421, December.
    11. Andreasen, Martin M., 2010. "Stochastic volatility and DSGE models," Economics Letters, Elsevier, vol. 108(1), pages 7-9, July.
    12. Ravi Bansal & Robert F. Dittmar & Christian T. Lundblad, 2005. "Consumption, Dividends, and the Cross Section of Equity Returns," Journal of Finance, American Finance Association, vol. 60(4), pages 1639-1672, August.
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    Cited by:

    1. Isoré, Marlène & Szczerbowicz, Urszula, 2017. "Disaster risk and preference shifts in a New Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 97-125.
    2. repec:spr:annopr:v:262:y:2018:i:2:d:10.1007_s10479-015-1972-8 is not listed on IDEAS
    3. Binder, Michael & Lieberknecht, Philipp & Quintana, Jorge & Wieland, Volker, 2017. "Model Uncertainty in Macroeconomics: On the Implications of Financial Frictions," CEPR Discussion Papers 12013, C.E.P.R. Discussion Papers.
    4. Pettenuzzo, Davide & Timmermann, Allan G & Valkanov, Rossen, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," CEPR Discussion Papers 10160, C.E.P.R. Discussion Papers.
    5. Doh, Taeyoung & Wu, Shu, 2015. "Cash flow and risk premium dynamics in an equilibrium asset-pricing model with recursive preferences," Research Working Paper RWP 15-12, Federal Reserve Bank of Kansas City.
    6. Shaliastovich, Ivan, 2015. "Learning, confidence, and option prices," Journal of Econometrics, Elsevier, vol. 187(1), pages 18-42.
    7. Gill Segal & Ivan Shaliastovich & Amir Yaron, 2014. "Good and Bad Uncertainty: Macroeconomic and Financial Market Implications," 2014 Meeting Papers 488, Society for Economic Dynamics.
    8. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    9. Pooyan Amir‐Ahmadi & Christian Matthes & Mu‐Chun Wang, 2016. "Drifts and volatilities under measurement error: Assessing monetary policy shocks over the last century," Quantitative Economics, Econometric Society, vol. 7(2), pages 591-611, July.
    10. Grammig, Joachim & Küchlin, Eva-Maria, 2017. "A two-step indirect inference approach to estimate the long-run risk asset pricing model," CFR Working Papers 17-01, University of Cologne, Centre for Financial Research (CFR).
    11. Dongho Song, 2014. "Bond Market Exposures to Macroeconomic and Monetary Policy Risks," PIER Working Paper Archive 14-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    12. Amir-Ahmadi, Pooyan & Matthes, Christian & Wang, Mu-Chun, 2016. "Choosing Prior Hyperparameters," Working Paper 16-9, Federal Reserve Bank of Richmond.
    13. Aruoba, S. Boragan, 2016. "Term structures of inflation expectations and real interest rates," Working Papers 16-9, Federal Reserve Bank of Philadelphia, revised 20 Sep 2016.
    14. GUERRON-QUINTANA, Pablo A. & JINNAI, Ryo, 2015. "Financial Frictions, Trends, and the Great Recession," Discussion paper series HIAS-E-14, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    15. Elmar Mertens & James M Nason, 2015. "Inflation and Professional Forecast Dynamics: An Evaluation of Stickiness, Persistence, and Volatility," CAMA Working Papers 2015-06, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    16. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    17. Ole Wilms & Karl Schmedders & Walt Pohl, 2016. "Higher-Order Effects in Asset-Pricing Models with Long-Run Risks," 2016 Meeting Papers 306, Society for Economic Dynamics.
    18. Drew D. Creal & Jing Cynthia Wu, 2016. "Bond Risk Premia in Consumption-based Models," NBER Working Papers 22183, National Bureau of Economic Research, Inc.
    19. Grammig, Joachim & Küchlin, Eva-Maria, 2017. "A two-step indirect inference approach to estimate the long-run risk asset pricing model," CFS Working Paper Series 572, Center for Financial Studies (CFS).
    20. Martin M. Andreasen & Kasper Jørgensen, 2016. "Explaining Asset Prices with Low Risk Aversion and Low Intertemporal Substitution," CREATES Research Papers 2016-16, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    Nonlinear theories ; Dividends ; Consumption (Economics) ; Bayesian statistical decision theory;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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