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Nonlinear Modeling of Financial Stability Using Default Probabilities from the Capital Market

Author

Listed:
  • Lucian Liviu ALBU

    (Institute for Economic Forecasting, Romanian Academy)

  • Radu LUPU

    (Institute for Economic Forecasting, Romanian Academy)

  • Adrian Cantemir CĂLIN

    (Institute for Economic Forecasting, Romanian Academy)

  • Iulia LUPU

    ("Victor Slăvescu” Centre for Financial and Monetary Research)

Abstract

Our study relies on a general assumption that prices contain a rational component, which is consistent with the rational expectations theory, and an irrational one, better explained by behavioural economics. We decompose the probabilities of default computed by Bloomberg for the listed Romanian companies by filtering the irrational component with newly proposed gauges. To check for the relevance of the rationality component, we use MiDaS models to study the relation with sectoral GDP gap dynamics for the corresponding companies. Employing regression related methods, we further divide the irrational part of default probabilities into a measure for fear and a measure for habit. After each transformation, we check the connection with the corresponding sectoral GDP gap. Our objective is to investigate the extent to which there is a connection between the macroeconomic expected activity, measured by the sectoral GDP gap and the risk of companies listed at the Bucharest Stock Exchange, quantified by probabilities of default. We embark on this journey with the assumption that the irrational component obfuscates the above-mentioned connections.

Suggested Citation

  • Lucian Liviu ALBU & Radu LUPU & Adrian Cantemir CĂLIN & Iulia LUPU, 2019. "Nonlinear Modeling of Financial Stability Using Default Probabilities from the Capital Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 19-37, March.
  • Handle: RePEc:rjr:romjef:v::y:2019:i:1:p:19-37
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    References listed on IDEAS

    as
    1. Lee A. Smales & Jardee N. Kininmonth, 2016. "FX Market Returns and Their Relationship to Investor Fear," International Review of Finance, International Review of Finance Ltd., vol. 16(4), pages 659-675, December.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 15(2), pages 457-510.
    4. Thaler, Richard H & Shefrin, H M, 1981. "An Economic Theory of Self-Control," Journal of Political Economy, University of Chicago Press, vol. 89(2), pages 392-406, April.
    5. Kim, Jae H., 2017. "Stock returns and investors' mood: Good day sunshine or spurious correlation?," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 94-103.
    6. Barberis, Nicholas & Thaler, Richard, 2003. "A survey of behavioral finance," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 18, pages 1053-1128, Elsevier.
    7. Tim Bollerslev & Viktor Todorov, 2011. "Tails, Fears, and Risk Premia," Journal of Finance, American Finance Association, vol. 66(6), pages 2165-2211, December.
    8. Alexander Puetz & Stefan Ruenzi, 2011. "Overconfidence Among Professional Investors: Evidence from Mutual Fund Managers," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 38(5-6), pages 684-712, June.
    9. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    10. Nicholas Barberis & Ming Huang, 2001. "Mental Accounting, Loss Aversion, and Individual Stock Returns," NBER Working Papers 8190, National Bureau of Economic Research, Inc.
    11. Bin Zou, 2017. "Optimal Investment In Hedge Funds Under Loss Aversion," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(03), pages 1-32, May.
    12. Jose A. Scheinkman & Wei Xiong, 2003. "Overconfidence and Speculative Bubbles," Journal of Political Economy, University of Chicago Press, vol. 111(6), pages 1183-1219, December.
    13. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    14. Lee, Boram & Veld-Merkoulova, Yulia, 2016. "Myopic loss aversion and stock investments: An empirical study of private investors," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 235-246.
    15. Nicholas Barberis & Ming Huang, 2001. "Mental Accounting, Loss Aversion, and Individual Stock Returns," Journal of Finance, American Finance Association, vol. 56(4), pages 1247-1292, August.
    16. Smales, L.A., 2016. "Risk-on/Risk-off: Financial market response to investor fear," Finance Research Letters, Elsevier, vol. 17(C), pages 125-134.
    17. Blackburn, Douglas W. & Cakici, Nusret, 2017. "Overreaction and the cross-section of returns: International evidence," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 1-14.
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    Cited by:

    1. Dragos HURU & Ioana MANAFI & Ionut PANDELICA & Marilena Carmen UZLAU, 2022. "Nonlinear Dependencies between Green Bonds and General Financial Market Indices," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 169-181, December.
    2. Radu LUPU & Iulia LUPU & Tanase STAMULE & Mihai ROMAN, 2022. "Entropy as Leading Indicator for Extreme Systemic Risk Events," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 58-73, December.

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    More about this item

    Keywords

    probabilities of default; fear; loss aversion; asymmetric volatility; day-of-the-week-effect; MiDaS regressions;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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