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Decomposing Risk in Dynamic Stochastic General Equilibrium

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  • Lan, Hong
  • Meyer-Gohde, Alexander

Abstract

We analyze the theoretical moments of a nonlinear approximation to real business cycle model with stochastic volatility and recursive preferences. We nd that the conditional heteroskedasticity of stochastic volatility operationalizes a time-varying risk adjustment channel that induces variability in conditional asset pricing measures and assigns a substantial portion of the variance of macroeconomic variables to variations in precautionary behavior, both while leaving its ability to match key macroeconomic and asset pricing facts untouched. We calculate the theoretical moments directly and decomposes these moments into contributions from shifts in the distribution of future shocks (i.e., risk) and from realized shocks and differing orders of approximation, enabling us to identify the common channel through which stochastic volatility in isolation operates and through which conditional asset pricing measures vary over time. Under frictional investment and varying capital utilization, output drops in response to an increase in risk, but the contributions to the variance of macroeconomic variables from risk becomes negligible.

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  • Lan, Hong & Meyer-Gohde, Alexander, 2014. "Decomposing Risk in Dynamic Stochastic General Equilibrium," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100523, Verein für Socialpolitik / German Economic Association.
    • Handle: RePEc:zbw:vfsc14:100523
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    Cited by:

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    4. Benjamin Born & Johannes Pfeifer, 2014. "Risk Matters: A Comment," CESifo Working Paper Series 4793, CESifo.
    5. Bonciani, Dario & Roye, Björn van, 2016. "Uncertainty shocks, banking frictions and economic activity," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 200-219.
    6. Benjamin Born & Johannes Pfeifer, 2014. "Risk Matters: The Real Effects of Volatility Shocks: Comment," American Economic Review, American Economic Association, vol. 104(12), pages 4231-4239, December.
    7. Poeschel, Friedrich, 2012. "Assortative matching through signals," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62061, Verein für Socialpolitik / German Economic Association.
    8. Alexander Meyer-Gohde, 2014. "Risky Linear Approximations," SFB 649 Discussion Papers SFB649DP2014-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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