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Predicting Systemic Risk with Entropic Indicators

Listed author(s):
  • Nikola Gradojevic

    ()

    (IÉSEG School of Management (LEM-CNRS), Lille Catholic University, France; Faculty of Technical Sciences, University of Novi Sad, Serbia; The Rimini Centre for Economic Analysis, Italy)

  • Marko Caric

    (Faculty of Economics and Engineering Management, Business Academy, Serbia)

This paper concentrates on quantifying the behavioral aspects of systemic risk by using a novel approach based on entropy. More specifically, we study aggregate market expectations and the predictability of the systemic risk before and during the financial crisis in 2008. Two underlying signals for estimating entropic risk measures are considered: 1) skewness premium of deepest out-of-the-money options, and 2) implied volatility ratio in regards to deepest out-of-the-money options. The findings confirm the predictive and contemporaneous usefulness of our entropy setting in market risk management. The degree of predictability is closely linked to both the type of entropy and the nature of the underlying signal.

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File URL: http://www.rcea.org/RePEc/pdf/wp15-14.pdf
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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 15-14.

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Date of creation: Mar 2015
Handle: RePEc:rim:rimwps:15-14
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  1. Scholes, Myron S, 1996. "Global Financial Markets, Derivative Securities, and Systemic Risks," Journal of Risk and Uncertainty, Springer, vol. 12(2-3), pages 271-286, May.
  2. Gradojevic, Nikola & Gencay, Ramazan, 2008. "Overnight interest rates and aggregate market expectations," Economics Letters, Elsevier, vol. 100(1), pages 27-30, July.
  3. Martin, M.T. & Plastino, A.R. & Plastino, A., 2000. "Tsallis-like information measures and the analysis of complex signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 275(1), pages 262-271.
  4. Lisa Borland, 2002. "A theory of non-Gaussian option pricing," Quantitative Finance, Taylor & Francis Journals, vol. 2(6), pages 415-431.
  5. Gamero, L.G. & Plastino, A. & Torres, M.E., 1997. "Wavelet analysis and nonlinear dynamics in a nonextensive setting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 246(3), pages 487-509.
  6. Kitamura, Yuichi & Stutzer, Michael, 2002. "Connections between entropic and linear projections in asset pricing estimation," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 159-174, March.
  7. Gençay, Ramazan & Gradojevic, Nikola, 2010. "Crash of '87 -- Was it expected?: Aggregate market fears and long-range dependence," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 270-282, March.
  8. Kaynar, Bahar & Ridder, Ad, 2010. "The cross-entropy method with patching for rare-event simulation of large Markov chains," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1380-1397, December.
  9. Tong, S. & Bezerianos, A. & Paul, J. & Zhu, Y. & Thakor, N., 2002. "Nonextensive entropy measure of EEG following brain injury from cardiac arrest," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 305(3), pages 619-628.
  10. Yang, Jiping & Qiu, Wanhua, 2005. "A measure of risk and a decision-making model based on expected utility and entropy," European Journal of Operational Research, Elsevier, vol. 164(3), pages 792-799, August.
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