Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce


IMPACT FACTOR 2018: 0.448
5-years IMPACT FACTOR: 0.877

CiteScore 2018: 0.85

SCImago Journal Rank (SJR) 2018: 0.552
Source Normalized Impact per Paper (SNIP) 2018: 0.561

Mathematical Citation Quotient (MCQ) 2018: 0.07

Online
ISSN
1558-3708
See all formats and pricing
More options …
Ahead of print

Issues

Volume 24 (2020)

Computational Methods for Production-Based Asset Pricing Models with Recursive Utility

Eric Mark Aldrich / Howard Kung
  • London Business School, Department of Finance, London, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2019-12-14 | DOI: https://doi.org/10.1515/snde-2017-0003

Abstract

We compare local and global polynomial solution methods for DSGE models with Epstein- Zin-Weil utility. We show that model implications for macroeconomic quantities are relatively invariant to choice of solution method but that a global method can yield substantial improvements for asset prices and welfare costs. The divergence in solution quality is highly dependent on parameters which affect value function sensitivity to TFP volatility, as well as the magnitude of TFP volatility itself. This problem is pronounced for calibrations at the extreme of those accepted in the asset pricing literature and disappears for more traditional macroeconomic parameterizations.

Keywords: asset pricing; DSGE models; nonlinear solution methods; numerical dynamic programming; recursive utility

References

  • Aruoba, S. B., J. Fernández-Villaverde, and J. F. Rubio-Ramírez. 2006. “Comparing Solution Methods for Dynamic Equilibrium Economies.” Journal of Economic Dynamics and Control 30: 2477–2508.CrossrefGoogle Scholar

  • Bansal, R., and A. Yaron. 2004. “Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles.” The Journal of Finance LIX: 1481–1509.Google Scholar

  • Bansal, R., D. Kiku, and A. Yaron. 2007. “Risks For the Long Run: Estimation and Inference.” Working Paper.Google Scholar

  • Brumm, J., and S. Scheidegger. 2015. “Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models.” Working Paper, 1–39.Google Scholar

  • Cai, Y., K. L. Judd, and J. Steinbuks. 2015. “A Nonlinear Certainty Equivalent Approximation Method for Dynamic Stochastic Problems.” Working Paper, 1–61.Google Scholar

  • Caldara, D., J. Fernández-Villaverde, J. F. Rubio-Ramírez, and W. Yao. 2012. “Computing DSGE Models with Recursive Preferences and Stochastic Volatility.” Review of Economic Dynamics 15: 188–206.CrossrefWeb of ScienceGoogle Scholar

  • Campanale, C., R. Castro, and G. L. Clementi. 2010. “Asset Pricing in a Production Economy with Chew-Dekel Preferences.” Review of Economic Dynamics 13: 379–402.CrossrefWeb of ScienceGoogle Scholar

  • Croce, M. M. 2013. “Welfare Costs in the Long Run.” Working Paper.Google Scholar

  • Den Haan, W. J., and A. Marcet. 1994. “Accuracy in Simulations.” The Review of Economics Studies 61: 3–17.CrossrefGoogle Scholar

  • Den Haan, W. J., and J. de Wind. 2009. “How Well-Behaved are Higher-Order Perturbation Solutions?” Working Paper.Google Scholar

  • Epstein, L. G., and S. E. Zin. 1989. “Substitution, Risk Aversion, and the Temporal Behavior of Consumption and Asset Returns: A Theoretical Framework.” Econometrica 57: 937–969.CrossrefGoogle Scholar

  • Fernández-Villaverde, J., and O. Levintal. 2016. “Solution Methods for Models with Rare Disasters.” Working Paper, 1–37, http://www.nber.org/papers/w21997.

  • Fernández-Villaverde, J., G. Gordon, P. Guerrón-Quintana, and J. F. Rubio-Ramírez. 2015. “Nonlinear Adventures at the Zero Lower Bound.” Journal of Economic Dynamics and Control 57: 182–204.Web of ScienceCrossrefGoogle Scholar

  • Irarrazabal, A., and J. C. Parra-Alvarez. 2015. “Time-Varying Disaster Risk Models: An Empirical Assessment of the Rietz-Barro Hypothesis.” Working Paper, 1–46.Google Scholar

  • Jermann, U. J. 1998. “Asset Pricing in Production Economies.” Journal of Monetary Economics 41: 257–275.CrossrefWeb of ScienceGoogle Scholar

  • Judd, K. L. 1998. Numerical Methods in Economics. Cambidge, MA: MIT Press.Google Scholar

  • Judd, K. L., and S.-M. Guu. 1997. “Asymptotic Methods for Aggregate Growth Models.” Journal of Economic Dynamics and Control 21: 1025–1042.CrossrefGoogle Scholar

  • Judd, K. L., L. Maliar, and S. Maliar. 2011. “Numerically Stable and Accurate Stochastic Simulation Approaches for Solving Dynamic Economic Models.” Quantitative Economics 2: 173–210.CrossrefWeb of ScienceGoogle Scholar

  • Judd, K. L., L. Maliar, S. Maliar, and R. Valero. 2014. “Smolyak Method for Solving Dynamic Economic Models: Lagrange Interpolation, Anisotropic Grid and Adaptive Domain.” Journal of Economic Dynamics and Control 44: 92–123.CrossrefWeb of ScienceGoogle Scholar

  • Kaltenbrunner, G., and L. Lochstoer. 2008. “Long-Run Risk through Consumption Smoothing.” Working Paper.Google Scholar

  • Levintal, O. 2016. “Taylor Projection: A New Solution Method to Dynamic General Equilibrium Models.” Working Paper, 1–62.Google Scholar

  • Maliar, L., and S. Maliar. 2015. “Merging Simulation and Projection Approaches to Solve High-Dimensional Problems with an Application to a New Keynesian Model.” Quantitative Economics 6: 1–47.Web of ScienceCrossrefGoogle Scholar

  • Mehra, R., and E. C. Prescott. 1985. “The Equity Premium: A Puzzle.” Journal of Monetary Economics 15: 145–161.CrossrefGoogle Scholar

  • Parra-Alvarez, J. C. 2017. “A Comparison of Numerical Methods for the Solution of Continuous-Time DSGE Models.” Macroeconomic Dynamics 22: 1555–1583.Web of ScienceGoogle Scholar

  • Pohl, W., K. Schmedders, and O. Wilms. 2015. “Higher-Order Effects in Asset-Pricing Models with Long-Run Risks.” Working Paper, 1–57.Google Scholar

  • Restoy, F., and G. M. Rockinger. 1994. “On Stock Market Returns and Returns on Investment.” The Journal of Finance XLIX: 543–556.Google Scholar

  • Rudebusch, G. D., and E. T. Swanson. 2012. “The Bond Premium in a DSGE Model with Long-Run Real and Nominal Risks.” American Economic Journal: Macroeconomics 4: 105–143.Web of ScienceGoogle Scholar

  • Schmitt-Grohé, S., and M. Uribe. 2004. “Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function.” Journal of Economic Dynamics and Control 28: 755–775.CrossrefGoogle Scholar

  • Weil, P. 1989. “The Equity Premium Puzzle and the Risk-Free Rate Puzzle.” Journal of Monetary Economics 24: 401–421.CrossrefGoogle Scholar

  • Weil, P. 1990. “Nonexpected Utility in Macroeconomics.” The Quarterly Journal of Economics 105: 29–42.CrossrefGoogle Scholar

About the article

Published Online: 2019-12-14


Citation Information: Studies in Nonlinear Dynamics & Econometrics, 20170003, ISSN (Online) 1558-3708, DOI: https://doi.org/10.1515/snde-2017-0003.

Export Citation

© 2019 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in