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Asset prices in affine real business cycle models

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  • Malkhozov, Aytek

Abstract

I describe a tractable way to study macroeconomic quantities and asset prices in a large class of dynamic stochastic general equilibrium models. The proposed approximate solution is analytical, log-linear, and adjusted for risk. Therefore, it is well suited to investigate economic mechanisms, describe the time series properties or estimate the model, and deal with stochastic volatility. I explain the pitfalls encountered by previous attempts to use simple approximation techniques, in particular with models featuring recursive preferences. Finally, I show the theoretical relationship between my solution and higher-order perturbation methods.

Suggested Citation

  • Malkhozov, Aytek, 2014. "Asset prices in affine real business cycle models," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 180-193.
    • Handle: RePEc:eee:dyncon:v:45:y:2014:i:c:p:180-193
    DOI: 10.1016/j.jedc.2014.05.011
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    Citations

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    Cited by:

    1. Segal, Gill & Shaliastovich, Ivan & Yaron, Amir, 2015. "Good and bad uncertainty: Macroeconomic and financial market implications," Journal of Financial Economics, Elsevier, vol. 117(2), pages 369-397.
    2. Kyle Jurado, 2016. "Advance Information and Distorted Beliefs in Macroeconomic and Financial Fluctuations," 2016 Meeting Papers 154, Society for Economic Dynamics.
    3. Pierlauro Lopez & J. David López-Salido & Francisco Vazquez-Grande, 2022. "Accounting for Risk in a Linearized Solution: How to Approximate the Risky Steady State and Around It," Working Papers 22-14, Federal Reserve Bank of Cleveland.
    4. Frank Hespeler & Marco M. Sorge, 2018. "Does Near†Rationality Matter In First†Order Approximate Solutions? A Perturbation Approach," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 97-113, January.
    5. Pierlauro Lopez & David Lopez-Salido & Francisco Vazquez-Grande, 2018. "Risk-Adjusted Linearizations of Dynamic Equilibrium Models," Working papers 702, Banque de France.
    6. Segal, Gill, 2019. "A tale of two volatilities: Sectoral uncertainty, growth, and asset prices," Journal of Financial Economics, Elsevier, vol. 134(1), pages 110-140.
    7. Chiu, Yen-Chen, 2020. "Macroeconomic uncertainty, information competition, and liquidity," Finance Research Letters, Elsevier, vol. 34(C).
    8. Francesco Bianchi & Howard Kung & Mikhail Tirskikh, 2023. "The origins and effects of macroeconomic uncertainty," Quantitative Economics, Econometric Society, vol. 14(3), pages 855-896, July.
    9. Tarek A. Hassan & Thomas M. Mertens & Jingye Wang, 2024. "A Currency Premium Puzzle," Working Paper Series 2024-32, Federal Reserve Bank of San Francisco.
    10. Backus, David & Ferriere, Axelle & Zin, Stanley, 2015. "Risk and ambiguity in models of business cycles," Journal of Monetary Economics, Elsevier, vol. 69(C), pages 42-63.
    11. Lorenzo Bretscher & Andrea Tamoni & Aytek Malkhozov, 2019. "News Shocks and Asset Prices," 2019 Meeting Papers 100, Society for Economic Dynamics.

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    More about this item

    Keywords

    Approximation methods; Asset prices; Stochastic volatility;
    All these keywords.

    JEL classification:

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

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