IDEAS home Printed from https://ideas.repec.org/p/zbw/bubdps/372018.html
   My bibliography  Save this paper

Equilibrium asset pricing in directed networks

Author

Listed:
  • Branger, Nicole
  • Konermann, Patrick
  • Meinerding, Christoph
  • Schlag, Christian

Abstract

Directed links in cash flow networks affect the cross-section of price exposures and market prices of risk in equilibrium. In an asset pricing model featuring mutually exciting jumps, we measure directedness through an asset's shock propagation capacity (spc). In the model, we prove: (i) Cash flow shocks of high spc assets command high market prices of risk, (ii) the price reaction of an asset to its own cash flow shocks is less pronounced for high spc assets. To illustrate our theoretical findings, we estimate an empirical network from industry cash flows and find support for these predictions.

Suggested Citation

  • Branger, Nicole & Konermann, Patrick & Meinerding, Christoph & Schlag, Christian, 2018. "Equilibrium asset pricing in directed networks," Discussion Papers 37/2018, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdps:372018
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/182301/1/1031178988.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Vasco M Carvalho & Makoto Nirei & Yukiko U Saito & Alireza Tahbaz-Salehi, 2021. "Supply Chain Disruptions: Evidence from the Great East Japan Earthquake," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(2), pages 1255-1321.
    2. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2017. "Microeconomic Origins of Macroeconomic Tail Risks," American Economic Review, American Economic Association, vol. 107(1), pages 54-108, January.
    3. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    4. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    5. Vasco Carvalho & Nico Voigtländer, 2014. "Input diffusion and the evolution of production networks," Economics Working Papers 1418, Department of Economics and Business, Universitat Pompeu Fabra, revised Feb 2015.
    6. Vasco M. Carvalho, 2014. "From Micro to Macro via Production Networks," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 23-48, Fall.
    7. Michael Gofman & Gill Segal & Youchang Wu & Stijn Van Nieuwerburgh, 2020. "Production Networks and Stock Returns: The Role of Vertical Creative Destruction," The Review of Financial Studies, Society for Financial Studies, vol. 33(12), pages 5856-5905.
    8. Vasco M Carvalho & Makoto Nirei & Yukiko U Saito & Alireza Tahbaz-Salehi, 0. "Supply Chain Disruptions: Evidence from the Great East Japan Earthquake," The Quarterly Journal of Economics, Oxford University Press, vol. 136(2), pages 1255-1321.
    9. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    10. Lior Menzly & Oguzhan Ozbas, 2010. "Market Segmentation and Cross‐predictability of Returns," Journal of Finance, American Finance Association, vol. 65(4), pages 1555-1580, August.
    11. David Backus & Mikhail Chernov & Ian Martin, 2011. "Disasters Implied by Equity Index Options," Journal of Finance, American Finance Association, vol. 66(6), pages 1969-2012, December.
    12. Vasco M. Carvalho, 2014. "From Micro to Macro via Production Networks," Working Papers 793, Barcelona School of Economics.
    13. Daron Acemoglu & Ufuk Akcigit & William Kerr, 2016. "Networks and the Macroeconomy: An Empirical Exploration," NBER Macroeconomics Annual, University of Chicago Press, vol. 30(1), pages 273-335.
    14. Jean-Noël Barrot & Julien Sauvagnat, 2016. "Input Specificity and the Propagation of Idiosyncratic Shocks in Production Networks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(3), pages 1543-1592.
    15. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    16. Branger, Nicole & Kraft, Holger & Meinerding, Christoph, 2014. "Partial information about contagion risk, self-exciting processes and portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 18-36.
    17. Han N. Ozsoylev & Johan Walden & M. Deniz Yavuz & Recep Bildik, 2014. "Investor Networks in the Stock Market," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1323-1366.
    18. Jerry Tsai & Jessica A. Wachter, 2014. "Rare Booms and Disasters in a Multi-sector Endowment Economy," NBER Working Papers 20062, National Bureau of Economic Research, Inc.
    19. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    20. Luca Benzoni & Pierre Collin-Dufresne & Robert S. Goldstein & Jean Helwege, 2015. "Modeling Credit Contagion via the Updating of Fragile Beliefs," The Review of Financial Studies, Society for Financial Studies, vol. 28(7), pages 1960-2008.
    21. Hansen, Lars Peter & Sargent, Thomas J., 2007. "Recursive robust estimation and control without commitment," Journal of Economic Theory, Elsevier, vol. 136(1), pages 1-27, September.
    22. Bernard Herskovic, 2018. "Networks in Production: Asset Pricing Implications," Journal of Finance, American Finance Association, vol. 73(4), pages 1785-1818, August.
    23. James D. Hamilton, 2018. "Why You Should Never Use the Hodrick-Prescott Filter," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 831-843, December.
    24. Xavier Gabaix, 2012. "Variable Rare Disasters: An Exactly Solved Framework for Ten Puzzles in Macro-Finance," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(2), pages 645-700.
    25. Aït-Sahalia, Yacine & Laeven, Roger J.A. & Pelizzon, Loriana, 2014. "Mutual excitation in Eurozone sovereign CDS," Journal of Econometrics, Elsevier, vol. 183(2), pages 151-167.
    26. Paul J. Irvine & Jeffrey Pontiff, 2009. "Idiosyncratic Return Volatility, Cash Flows, and Product Market Competition," The Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1149-1177.
    27. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2013. "The Network Origins of Large Economic Downturns," NBER Working Papers 19230, National Bureau of Economic Research, Inc.
    28. Bernard Herskovic, 2015. "Networks in Production: Asset Pricing Implications," 2015 Meeting Papers 378, Society for Economic Dynamics.
    29. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    30. Jerry Tsai & Jessica A. Wachter, 2015. "Disaster Risk and its Implications for Asset Pricing," NBER Working Papers 20926, National Bureau of Economic Research, Inc.
    31. Jessica A. Wachter, 2013. "Can Time-Varying Risk of Rare Disasters Explain Aggregate Stock Market Volatility?," Journal of Finance, American Finance Association, vol. 68(3), pages 987-1035, June.
    32. Jerry Tsai & Jessica A. Wachter, 2015. "Disaster Risk and Its Implications for Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 219-252, December.
    33. Long, John B, Jr & Plosser, Charles I, 1983. "Real Business Cycles," Journal of Political Economy, University of Chicago Press, vol. 91(1), pages 39-69, February.
    34. Bjørn Eraker & Ivan Shaliastovich, 2008. "An Equilibrium Guide To Designing Affine Pricing Models," Mathematical Finance, Wiley Blackwell, vol. 18(4), pages 519-543, October.
    35. Paul J. Irvine & Jeffrey Pontiff, 2009. "Idiosyncratic Return Volatility, Cash Flows, and Product Market Competition," The Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1149-1177, March.
    36. repec:bla:jfinan:v:59:y:2004:i:4:p:1481-1509 is not listed on IDEAS
    37. Kenneth R. Ahern & Jarrad Harford, 2014. "The Importance of Industry Links in Merger Waves," Journal of Finance, American Finance Association, vol. 69(2), pages 527-576, April.
    38. Sang Byung Seo & Jessica A. Wachter, 2018. "Do Rare Events Explain CDX Tranche Spreads?," Journal of Finance, American Finance Association, vol. 73(5), pages 2343-2383, October.
    39. Steven Ho & Philip Howard & Mariano Croce & Riccardo Colacito, 2013. "BKK the EZ way. An International Production Economy with Recursive Preferences," 2013 Meeting Papers 112, Society for Economic Dynamics.
    40. Aït-Sahalia, Yacine & Cacho-Diaz, Julio & Laeven, Roger J.A., 2015. "Modeling financial contagion using mutually exciting jump processes," Journal of Financial Economics, Elsevier, vol. 117(3), pages 585-606.
    41. Robert J. Richmond, 2019. "Trade Network Centrality and Currency Risk Premia," Journal of Finance, American Finance Association, vol. 74(3), pages 1315-1361, June.
    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. Baruník, Jozef & Ellington, Michael, 2024. "Persistence in financial connectedness and systemic risk," European Journal of Operational Research, Elsevier, vol. 314(1), pages 393-407.
    2. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto & Pelizzon, Loriana, 2023. "The impact of network connectivity on factor exposures, asset pricing, and portfolio diversification," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 196-223.
    3. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.

    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. Ernesto Pasten & Raphael S. Schoenle & Michael Weber & Michael Weber, 2017. "Price Rigidities and the Granular Origins of Aggregate Fluctuations," CESifo Working Paper Series 6619, CESifo.
    2. Dong, Feng & Wen, Yi, 2019. "Long and Plosser meet Bewley and Lucas," Journal of Monetary Economics, Elsevier, vol. 102(C), pages 70-92.
    3. Ernesto Pasten & Raphael Schoenle & Michael Weber, 2017. "Price Rigidity and the Origins of Aggregate Fluctuations," NBER Working Papers 23750, National Bureau of Economic Research, Inc.
    4. repec:zbw:bofrdp:2018_003 is not listed on IDEAS
    5. Pasten, Ernesto & Schoenle, Raphael & Weber, Michael, 2017. "Price rigidities and the granular origins of aggregate fluctuations," Working Paper Series 2102, European Central Bank.
    6. Julian Di Giovanni & Galina Hale, 2022. "Stock Market Spillovers via the Global Production Network: Transmission of U.S. Monetary Policy," Journal of Finance, American Finance Association, vol. 77(6), pages 3373-3421, December.
    7. Brancaccio, Emiliano & Giammetti, Raffaele & Lopreite, Milena & Puliga, Michelangelo, 2019. "Monetary policy, crisis and capital centralization in corporate ownership and control networks: A B-Var analysis," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 55-66.
    8. Kristina Barauskaite & Anh D. M. Nguyen, 2022. "Intersectoral network‐based channel of aggregate TFP shocks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3897-3910, October.
    9. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    10. Yu, Miao & Hu, Xiaolu & Zhong, Angel, 2024. "Network centrality, information diffusion and asset pricing," International Review of Financial Analysis, Elsevier, vol. 93(C).
    11. Pasten, Ernesto & Schoenle, Raphael & Weber, Michael, 2020. "The propagation of monetary policy shocks in a heterogeneous production economy," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 1-22.
    12. Everett Grant & Julieta Yung, 2019. "Upstream, Downstream & Common Firm Shocks," Globalization Institute Working Papers 360, Federal Reserve Bank of Dallas.
    13. Everett Grant & Julieta Yung, 2021. "The double‐edged sword of global integration: Robustness, fragility, and contagion in the international firm network," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 760-783, September.
    14. Kristina Barauskaite & Anh Dinh Minh Nguyen, 2021. "Direct and network effects of idiosyncratic TFP shocks," Empirical Economics, Springer, vol. 60(6), pages 2765-2793, June.
    15. Matthew Elliott & Benjamin Golub & Matthew V. Leduc, 2022. "Supply Network Formation and Fragility," American Economic Review, American Economic Association, vol. 112(8), pages 2701-2747, August.
    16. Lee, Dongyeol, 2021. "Propagation of economic shocks through vertical and trade linkages in Korea: An empirical analysis," Japan and the World Economy, Elsevier, vol. 60(C).
    17. Raffaele Giammetti & Alberto Russo & Mauro Gallegati, 2020. "Key sectors in input–output production networks: An application to Brexit," The World Economy, Wiley Blackwell, vol. 43(4), pages 840-870, April.
    18. Goya, Daniel, 2021. "The network effect of Chinese competition on what domestic suppliers produce," Economic Modelling, Elsevier, vol. 102(C).
    19. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    20. Erik Frohm & Vanessa Gunnella, 2021. "Spillovers in global production networks," Review of International Economics, Wiley Blackwell, vol. 29(3), pages 663-680, August.
    21. Mundt, Philipp, 2021. "The formation of input–output architecture: Evidence from the European Union," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 89-104.

    More about this item

    Keywords

    directed cash flow networks; directed shocks; mutually exciting processes; recursive preferences;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

    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:zbw:bubdps:372018. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/dbbgvde.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.