Nothing Special   »   [go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v39y2017ipbp990-999.html
   My bibliography  Save this article

Characterizing investor expectations for assets with varying risk

Author

Listed:
  • Gaus, Eric
  • Sinha, Arunima
Abstract
How do financial market investors form expectations about assets with different risk characteristics? We examine this question using Euro-area yield curves for AAA-rated and AAA-with-other bonds. Investors’ conditional forecasts about the yield curves for different assets, at various forecasting horizons, are modeled using a VAR model with time-varying parameters. Two processes are assumed for the evolution of these parameters: a constant-gain learning model and a new endogenous learning technique proposed here. Both these algorithms allow investors to account for structural changes in the data. The endogenous learning mechanism also allows investors to compensate for large deviations in observed coefficients used for forecasting, relative to past data. Daily data is used to estimate the gain parameters for the learning algorithms, and we find that these gains vary across asset types, implying investors form conditional expectations differently for assets with differential risks. For 2005–2015, the investors’ conditional forecasts for the AAA-rated bonds are better described using the endogenous learning mechanism, implying that investors with lower risk preferences are more sensitive to large deviations in the data.

Suggested Citation

  • Gaus, Eric & Sinha, Arunima, 2017. "Characterizing investor expectations for assets with varying risk," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 990-999.
  • Handle: RePEc:eee:riibaf:v:39:y:2017:i:pb:p:990-999
    DOI: 10.1016/j.ribaf.2016.01.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531916300198
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ribaf.2016.01.019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. William A. Branch & George W. Evans, 2010. "Asset Return Dynamics and Learning," The Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1651-1680, April.
    2. Bacchetta, Philippe & Mertens, Elmar & van Wincoop, Eric, 2009. "Predictability in financial markets: What do survey expectations tell us?," Journal of International Money and Finance, Elsevier, vol. 28(3), pages 406-426, April.
    3. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    4. Gourinchas, Pierre-Olivier & Tornell, Aaron, 2004. "Exchange rate puzzles and distorted beliefs," Journal of International Economics, Elsevier, vol. 64(2), pages 303-333, December.
    5. Verrecchia, Robert E, 1982. "Information Acquisition in a Noisy Rational Expectations Economy," Econometrica, Econometric Society, vol. 50(6), pages 1415-1430, November.
    6. Stefano Eusepi & Bruce Preston, 2011. "Expectations, Learning, and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 101(6), pages 2844-2872, October.
    7. William A. Branch & George W. Evans, 2011. "Learning about Risk and Return: A Simple Model of Bubbles and Crashes," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(3), pages 159-191, July.
    8. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    9. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-2678, August.
    10. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    11. Albert Marcet & Juan P. Nicolini, 2003. "Recurrent Hyperinflations and Learning," American Economic Review, American Economic Association, vol. 93(5), pages 1476-1498, December.
    12. Svensson, Lars E O, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992-4," CEPR Discussion Papers 1051, C.E.P.R. Discussion Papers.
    13. Dewachter, Hans & Lyrio, Marco, 2006. "Macro Factors and the Term Structure of Interest Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(1), pages 119-140, February.
    14. Glenn D. Rudebusch & Tao Wu, 2007. "Accounting for a Shift in Term Structure Behavior with No-Arbitrage and Macro-Finance Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2-3), pages 395-422, March.
    15. Hommes,Cars, 2015. "Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems," Cambridge Books, Cambridge University Press, number 9781107564978, October.
    16. Gurkaynak, Refet S. & Sack, Brian & Wright, Jonathan H., 2007. "The U.S. Treasury yield curve: 1961 to the present," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2291-2304, November.
    17. Thomas Laubach & Robert J. Tetlow & John C. Williams, 2007. "Learning and the Role of Macroeconomic Factors in the Term Structure of Interest Rates," 2007 Meeting Papers 476, Society for Economic Dynamics.
    18. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    19. Michiel De Pooter, 2007. "Examining the Nelson-Siegel Class of Term Structure Models," Tinbergen Institute Discussion Papers 07-043/4, Tinbergen Institute.
    20. Milani, Fabio, 2014. "Learning and time-varying macroeconomic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 47(C), pages 94-114.
    21. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
    22. Jeffrey C. Fuhrer, 1996. "Monetary Policy Shifts and Long-Term Interest Rates," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(4), pages 1183-1209.
    23. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, vol. 121(552), pages 379-401, May.
    24. Eric Gaus, 2013. "Time-Varying Parameters and Endogenous Learning Algorithms," Working Papers 13-02, Ursinus College, Department of Economics.
    25. Jacobs,Donald P. & Kalai,Ehud & Kamien,Morton I. & Schwartz,Nancy L. (ed.), 1998. "Frontiers of Research in Economic Theory," Cambridge Books, Cambridge University Press, number 9780521635387, October.
    26. Kozicki, Sharon & Tinsley, P. A., 2001. "Shifting endpoints in the term structure of interest rates," Journal of Monetary Economics, Elsevier, vol. 47(3), pages 613-652, June.
    27. Bianchi, Francesco & Mumtaz, Haroon & Surico, Paolo, 2009. "The great moderation of the term structure of UK interest rates," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 856-871, September.
    28. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    Full references (including those not matched with items on IDEAS)

    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. Gaus, Eric & Sinha, Arunima, 2018. "What does the yield curve imply about investor expectations?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 248-265.
    2. Faria, Adriano & Almeida, Caio, 2018. "A hybrid spline-based parametric model for the yield curve," Journal of Economic Dynamics and Control, Elsevier, vol. 86(C), pages 72-94.
    3. Laurini, Márcio P. & Caldeira, João F., 2016. "A macro-finance term structure model with multivariate stochastic volatility," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 68-90.
    4. Ioannidis, Christos & Ka, Kook, 2018. "The impact of oil price shocks on the term structure of interest rates," Energy Economics, Elsevier, vol. 72(C), pages 601-620.
    5. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2012. "The yield curve and the macro-economy across time and frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1950-1970.
    6. Refet S. Gürkaynak & Jonathan H. Wright, 2012. "Macroeconomics and the Term Structure," Journal of Economic Literature, American Economic Association, vol. 50(2), pages 331-367, June.
    7. Eder, Armin & Keiler, Sebastian & Pichl, Hannes, 2013. "Interest rate risk and the Swiss solvency test," Discussion Papers 41/2013, Deutsche Bundesbank.
    8. Kang, Kyu Ho, 2015. "The predictive density simulation of the yield curve with a zero lower bound," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 51-66.
    9. Gáti, Laura, 2023. "Monetary policy & anchored expectations—An endogenous gain learning model," Journal of Monetary Economics, Elsevier, vol. 140(S), pages 37-47.
    10. Luciano Vereda & Hélio Lopes & Jessica Kubrusly & Adrian Pizzinga & Taofik Mohammed Ibrahim, 2014. "Yield Curve Forecasts and the Predictive Power of Macro Variables in a VAR Framework," Journal of Reviews on Global Economics, Lifescience Global, vol. 3, pages 377-393.
    11. Rajnish Mehra & Arunima Sinha, 2016. "The Term Structure of Interest Rates in India," NBER Working Papers 22020, National Bureau of Economic Research, Inc.
    12. Michał Brzoza-Brzezina & Jacek Kotłowski, 2014. "Measuring the natural yield curve," Applied Economics, Taylor & Francis Journals, vol. 46(17), pages 2052-2065, June.
    13. Afonso, António & Martins, Manuel M.F., 2012. "Level, slope, curvature of the sovereign yield curve, and fiscal behaviour," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1789-1807.
    14. Hommes, Cars, 2018. "Behavioral & experimental macroeconomics and policy analysis: a complex systems approach," Working Paper Series 2201, European Central Bank.
    15. Stona, Filipe & Caldeira, João F., 2019. "Do U.S. factors impact the Brazilian yield curve? Evidence from a dynamic factor model," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 76-89.
    16. Michiel De Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," International Finance Discussion Papers 993, Board of Governors of the Federal Reserve System (U.S.).
    17. Vahidin Jeleskovic & Anastasios Demertzidis, 2018. "Comparing different methods for the estimation of interbank intraday yield curves," MAGKS Papers on Economics 201839, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. Anastasios Demertzidis & Vahidin Jeleskovic, 2021. "Empirical Estimation of Intraday Yield Curves on the Italian Interbank Credit Market e-MID," JRFM, MDPI, vol. 14(5), pages 1-23, May.
    19. Glenn D. Rudebusch, 2010. "Macro‐Finance Models Of Interest Rates And The Economy," Manchester School, University of Manchester, vol. 78(s1), pages 25-52, September.
    20. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.

    More about this item

    Keywords

    Adaptive learning; Investor beliefs; Risk;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:eee:riibaf:v:39:y:2017:i:pb:p:990-999. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .

    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.