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Predicting Systemic Risk with Entropic Indicators

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  • Nikola Gradojevic
  • Marko Caric

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.
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Suggested Citation

  • Nikola Gradojevic & Marko Caric, 2017. "Predicting Systemic Risk with Entropic Indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 16-25, January.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:1:p:16-25
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    Cited by:

    1. Hidekazu Yoshioka, 2024. "Generalized Logit Dynamics Based on Rational Logit Functions," Dynamic Games and Applications, Springer, vol. 14(5), pages 1333-1358, November.
    2. Barbagli, Matteo & François, Pascal & Gauthier, Geneviève & Vrins, Frédéric, 2025. "The role of CDS spreads in explaining bond recovery rates," Journal of Banking & Finance, Elsevier, vol. 174(C).
    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. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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).
    9. Radhika Prosad Datta, 2023. "Leveraging Sample Entropy for Enhanced Volatility Measurement and Prediction in International Oil Price Returns," Papers 2312.12788, arXiv.org.

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