IDEAS home Printed from https://ideas.repec.org/p/fsu/wpaper/wp2019_09_01.html
   My bibliography  Save this paper

Default Recovery Rates and Aggregate Fluctuations

Author

Listed:
  • Giacomo Candian

    (HEC Montreal)

  • Mikhail Dmitriev

    (Department of Economics, Florida State University)

Abstract

Default recovery rates in the US are highly volatile and pro-cyclical. We show that state-of-the-art models with a Bernanke-Gertler-Gilchrist financial accelerator mechanism imply that recovery rates are flat over the cycle. We propose a model where financially constrained entrepreneurs face an idiosyncratic cost of redeploying liquidated capital. The resulting endogenous liquidation costs magnify the effect of the financial accelerator. We fit the model to US data and find that it explains a substantial amount of variation in recovery rates. Our mechanism alters the transmission of structural disturbances and leads to novel policy implications about the effectiveness of subsidies for liquidated assets.

Suggested Citation

  • Giacomo Candian & Mikhail Dmitriev, 2019. "Default Recovery Rates and Aggregate Fluctuations," Working Papers wp2019_09_01, Department of Economics, Florida State University.
    • Handle: RePEc:fsu:wpaper:wp2019_09_01
    as

    Download full text from publisher

    File URL: https://coss.fsu.edu/econpapers/wpaper/wp2019_09_01.pdf
    File Function: First version, 2019-09
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Simon Gilchrist & Raphael Schoenle & Jae Sim & Egon Zakrajšek, 2017. "Inflation Dynamics during the Financial Crisis," American Economic Review, American Economic Association, vol. 107(3), pages 785-823, March.
    2. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
    3. Shleifer, Andrei & Vishny, Robert W, 1992. "Liquidation Values and Debt Capacity: A Market Equilibrium Approach," Journal of Finance, American Finance Association, vol. 47(4), pages 1343-1366, September.
    4. Justiniano, Alejandro & Primiceri, Giorgio E. & Tambalotti, Andrea, 2010. "Investment shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 132-145, March.
    5. Bruche, Max & González-Aguado, Carlos, 2010. "Recovery rates, default probabilities, and the credit cycle," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 754-764, April.
    6. Marco Del Negro & Marc P. Giannoni & Frank Schorfheide, 2015. "Inflation in the Great Recession and New Keynesian Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 168-196, January.
    7. Jing Cynthia Wu & Fan Dora Xia, 2016. "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 253-291, March.
    8. Andrea Ajello, 2016. "Financial Intermediation, Investment Dynamics, and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 106(8), pages 2256-2303, August.
    9. Jonathan Hoddenbagh & Mikhail Dmitriev, 2017. "The Financial Accelerator and the Optimal State-Dependent Contract," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 43-65, March.
    10. Cristina Fuentes‐Albero, 2019. "Financial Frictions, Financial Shocks, and Aggregate Volatility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(6), pages 1581-1621, September.
    11. Choi, Woon Gyu & Cook, David, 2012. "Fire sales and the financial accelerator," Journal of Monetary Economics, Elsevier, vol. 59(4), pages 336-351.
    12. Lawrence J. Christiano & Martin S. Eichenbaum & Mathias Trabandt, 2015. "Understanding the Great Recession," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 110-167, January.
    13. Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393, Elsevier.
    14. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    15. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    16. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    17. Andrew T. Levin & Fabio M. Natalucci & Egon Zakrajšek, 2004. "The magnitude and cyclical behavior of financial market frictions," Finance and Economics Discussion Series 2004-70, Board of Governors of the Federal Reserve System (U.S.).
    18. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    19. Chris Woolston, 2014. "Rice," Nature, Nature, vol. 514(7524), pages 49-49, October.
    20. Erceg, Christopher J. & Henderson, Dale W. & Levin, Andrew T., 2000. "Optimal monetary policy with staggered wage and price contracts," Journal of Monetary Economics, Elsevier, vol. 46(2), pages 281-313, October.
    21. Lawrence J. Christiano & Roberto Motto & Massimo Rostagno, 2014. "Risk Shocks," American Economic Review, American Economic Association, vol. 104(1), pages 27-65, January.
    22. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    23. Jon Frye, 2000. "Collateral damage detected," Emerging Issues, Federal Reserve Bank of Chicago, issue Sep.
    24. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    25. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    26. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    27. Nada Mora, 2012. "What determines creditor recovery rates?," Economic Review, Federal Reserve Bank of Kansas City, vol. 97(Q II).
    28. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
    29. Smets, Frank & Villa, Stefania, 2016. "Slow recoveries: Any role for corporate leverage?," Journal of Economic Dynamics and Control, Elsevier, vol. 70(C), pages 54-85.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anna Belianska & Aurélien Eyquem & Céline Poilly, 2021. "The Transmission Channels of Government Spending Uncertainty," AMSE Working Papers 2115, Aix-Marseille School of Economics, France.
    2. Goodhart, Charles A.E. & Tsomocos, Dimitrios P. & Wang, Xuan, 2023. "Bank credit, inflation, and default risks over an infinite horizon," Journal of Financial Stability, Elsevier, vol. 67(C).
    3. Aicha Kharazi & Francesco Ravazzolo, 2023. "Regulatory Collateral Requirements and Delinquency Rate in a Two-Agent New Keynesian Model," Working Paper series 23-03, Rimini Centre for Economic Analysis.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lindé, J. & Smets, F. & Wouters, R., 2016. "Challenges for Central Banks’ Macro Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 2185-2262, Elsevier.
    2. Böhl, Gregor & Strobel, Felix, 2020. "US business cycle dynamics at the zero lower bound," IMFS Working Paper Series 143, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    3. Lindé, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks´ Macro Models," Working Paper Series 323, Sveriges Riksbank (Central Bank of Sweden).
    4. Giacomo Candian & Mikhail Dmitriev, 2019. "Implications of Default Recovery Rates for Aggregate Fluctuations," 2019 Meeting Papers 1185, Society for Economic Dynamics.
    5. Drago Bergholt, 2014. "Foreign shocks in an estimated multi-sector model," Working Papers No 4/2014, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Francesco Furlanetto & Paolo Gelain & Marzie Taheri Sanjani, 2021. "Output Gap, Monetary Policy Trade-offs, and Financial Frictions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 52-70, July.
    7. Brand, Thomas & Isoré, Marlène & Tripier, Fabien, 2019. "Uncertainty shocks and firm creation: Search and monitoring in the credit market," Journal of Economic Dynamics and Control, Elsevier, vol. 99(C), pages 19-53.
    8. Drago Bergholt, 2015. "Foreign Shocks," Working Papers No 11/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    9. Lieberknecht, Philipp, 2018. "Financial Frictions, the Phillips Curve and Monetary Policy," MPRA Paper 89429, University Library of Munich, Germany.
    10. Hürtgen, Patrick, 2014. "Consumer misperceptions, uncertain fundamentals, and the business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 279-292.
    11. Brand, Thomas & Isoré, Marlène & Tripier, Fabien, 2019. "Uncertainty shocks and firm creation: Search and monitoring in the credit market," Journal of Economic Dynamics and Control, Elsevier, vol. 99(C), pages 19-53.
    12. Bianchi, Francesco & Kung, Howard & Morales, Gonzalo, 2019. "Growth, slowdowns, and recoveries," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 47-63.
    13. Hashmat Khan & John Tsoukalas, 2012. "The Quantitative Importance of News Shocks in Estimated DSGE Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1535-1561, December.
    14. Shirota, Toyoichiro, 2018. "What is the major source of business cycles: Spillovers from land prices, investment shocks, or anything else?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 138-149.
    15. Andrea Ajello, 2016. "Financial Intermediation, Investment Dynamics, and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 106(8), pages 2256-2303, August.
    16. Khan, Hashmat & Tsoukalas, John, 2011. "Investment shocks and the comovement problem," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 115-130, January.
    17. repec:zbw:bofrdp:2017_034 is not listed on IDEAS
    18. Gunn Christopher M. & Johri Alok & Letendre Marc-André, 2023. "Charge-offs, Defaults and the Financial Accelerator," The B.E. Journal of Macroeconomics, De Gruyter, vol. 23(1), pages 427-471, January.
    19. Görtz, Christoph & Tsoukalas, John, 2011. "News and financial intermediation in aggregate and sectoral fluctuations," MPRA Paper 38986, University Library of Munich, Germany, revised Mar 2012.
    20. Afrin, Sadia, 2017. "The role of financial shocks in business cycles with a liability side financial friction," Economic Modelling, Elsevier, vol. 64(C), pages 249-269.
    21. Fève, Patrick & Moura, Alban & Pierrard, Olivier, 2022. "The fall in shadow banking and the slow U.S. recovery," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).

    More about this item

    Keywords

    Financial accelerator; financial frictions; recovery rates; liquidation costs;
    All these keywords.

    JEL classification:

    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fsu:wpaper:wp2019_09_01. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Luke Rodgers (email available below). General contact details of provider: https://edirc.repec.org/data/defsuus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.