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Predicting Systemic Risk with Entropic Indicators

Author

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  • 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)

Abstract

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.

Suggested Citation

  • Nikola Gradojevic & Marko Caric, 2015. "Predicting Systemic Risk with Entropic Indicators," Working Paper series 15-14, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:15-14
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    References listed on IDEAS

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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.
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    10. Namaki, A. & Koohi Lai, Z. & Jafari, G.R. & Raei, R. & Tehrani, R., 2013. "Comparing emerging and mature markets during times of crises: A non-extensive statistical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3039-3044.
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    Cited by:

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    2. Barbagli, Matteo & François, Pascal & Gauthier, Geneviève & Vrins, Frédéric, 2024. "The role of CDS spreads in explaining bond recovery rates," LIDAM Discussion Papers LFIN 2024002, Université catholique de Louvain, Louvain Finance (LFIN).
    3. Casarin, Roberto & Costola, Michele, 2019. "Structural changes in large economic datasets: A nonparametric homogeneity test," Economics Letters, Elsevier, vol. 176(C), pages 55-59.
    4. Nikola Gradojevic, 2021. "Brexit and foreign exchange market expectations: Could it have been predicted?," Annals of Operations Research, Springer, vol. 297(1), pages 167-189, February.
    5. Shternshis, Andrey & Mazzarisi, Piero & Marmi, Stefano, 2022. "Measuring market efficiency: The Shannon entropy of high-frequency financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    6. Li, Chuchu & Lin, Qin & Huang, Dong & Grifoll, Manel & Yang, Dong & Feng, Hongxiang, 2023. "Is entropy an indicator of port traffic predictability? The evidence from Chinese ports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    7. Radhika Prosad Datta, 2023. "Leveraging Sample Entropy for Enhanced Volatility Measurement and Prediction in International Oil Price Returns," Papers 2312.12788, arXiv.org.
    8. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    9. Harshit Mishra & Parama Barai, 2024. "Entropy Augmented Asset Pricing Model: Study on Indian Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(1), pages 81-99, March.

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