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Financial Risk Meter for The Romanian Stock Market

Author

Listed:
  • Daniel Traian PELE

    (Department of Statistics and Econometrics, Faculty of Cybernetics, Statistics and Economic Informatics, The Bucharest University of Economic Studies, Piata Romana nr. 6, sector 1, 010374, Bucharest, Romania. Institute for Economic Forecasting, Romanian Academy; Casa Academiei, Calea 13 Septembrie nr. 13, sector 5, 050711, Bucharest, Romania.)

  • Alexandra Ioana CONDA

    (Economic Cybernetics and Statistics Doctoral School, Faculty of Economic Cybernetics, Statistics and Informatics, Bucharest University of Economic Studies, Piata Romana nr. 6, sector 1, 010374, Bucharest, Romania)

  • Raul Cristian BAG

    (Economic Cybernetics and Statistics Doctoral School, Faculty of Economic Cybernetics, Statistics and Informatics, Bucharest University of Economic Studies, Piata Romana nr. 6, sector 1, 010374, Bucharest, Romania)

  • Miruna MAZURENCU-MARINESCU-PELE

    (Department of Statistics and Econometrics, Faculty of Cybernetics, Statistics and Economic Informatics, The Bucharest University of Economic Studies, Piata Romana nr. 6, sector 1, 010374, Bucharest, Romania.)

  • Vasile Alecsandru STRAT

    (Department of Statistics and Econometrics, Faculty of Cybernetics, Statistics and Economic Informatics, The Bucharest University of Economic Studies, Piata Romana nr. 6, sector 1, 010374, Bucharest, Romania.)

Abstract

This article aims to estimate the systemic risk of the Romanian stock market, using the FRM (Financial Risk Meter) methodology. This research contribution is about applying a novel systemic risk index to the Romanian financial system (FRM@RO), to identify potential sources of systemic risk, and to understand network interconnections, thus increasing risk awareness of both managers and regulators. By using data for companies listed at the Bucharest Stock Exchange (BSE), our article highlights several aspects of the systemic risk of the Romanian stock market. First, our study reveals that the main driver of systemic risk, especially during financial crises, is the volatility index, VIX. However, local factors, such as ROBOR interest rate and sectorial indices for financial investment companies, in general, and energy sector companies, in particular, are extremely important in triggering systemic risk. Second, the system risk indicator for the Romanian stock market, FRM@RO, may capture both investor sentiment, measured via the Google Trends Search Volume Index, and stock market volatility. Third, FRM@RO can act as an early warning indicator for economic turmoil, being able to predict periods of technical recession one quarter in advance. Fourth, by using network analysis, we can identify, daily, the level of market interconnectedness and highlight the main companies triggering tail co-movements. Fifth, we emphasize the need for an integrated early warning system for financial crises.

Suggested Citation

  • Daniel Traian PELE & Alexandra Ioana CONDA & Raul Cristian BAG & Miruna MAZURENCU-MARINESCU-PELE & Vasile Alecsandru STRAT, 2023. "Financial Risk Meter for The Romanian Stock Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 5-24, March.
  • Handle: RePEc:rjr:romjef:v::y:2023:i:1:p:5-24
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    References listed on IDEAS

    as
    1. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    2. Nicola Borri & Marianna Caccavaio & Giorgio Di Giorgio & Alberto Maria Sorrentino, 2014. "Systemic Risk in the Italian Banking Industry," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 43(1), pages 21-38, February.
    3. Markwat, Thijs & Kole, Erik & van Dijk, Dick, 2009. "Contagion as a domino effect in global stock markets," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 1996-2012, November.
    4. DRAGHIA, Andreea & STEFONI, Sorina Emanuela, 2020. "A Financial Systemic Stress Index For Romania," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 24(3), pages 41-50, September.
    5. Ben Amor, Souhir & Althof, Michael & Härdle, Wolfgang Karl, 2022. "Financial Risk Meter for emerging markets," Research in International Business and Finance, Elsevier, vol. 60(C).
    6. NAGY, Ágnes & DÉZSI-BENYOVSZKI, Annamária & SZÉKELY, Imre, 2016. "Measuring Financial Systemic Stress In Romania: A Composite Indicator Approach," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 20(3), pages 28-38.
    7. Ren, Rui & Lu, Meng-Jou & Li, Yingxing & Härdle, Wolfgang Karl, 2022. "Financial Risk Meter FRM based on Expectiles," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    8. Smaga, Pawel, 2014. "The concept of systemic risk," LSE Research Online Documents on Economics 61214, London School of Economics and Political Science, LSE Library.
    9. Yuan, Ming, 2006. "GACV for quantile smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 813-829, February.
    10. Cristiana Tudor, 2011. "Changes in Stock Markets Interdependencies as a Result of the Global Financial Crisis: Empirical Investigation on the CEE Region," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 58(4), pages 525-543, December.
    11. Brownlees, Christian & Chabot, Ben & Ghysels, Eric & Kurz, Christopher, 2020. "Back to the future: Backtesting systemic risk measures during historical bank runs and the great depression," Journal of Banking & Finance, Elsevier, vol. 113(C).
    12. Mariana Hatmanu & Cristina Cautisanu, 2021. "The Impact of COVID-19 Pandemic on Stock Market: Evidence from Romania," IJERPH, MDPI, vol. 18(17), pages 1-22, September.
    13. Blundell-Wignall, Adrian & Atkinson, Paul E. & Roulet, Caroline, 2012. "The Business Models of Large Interconnected Banks and the Lessons of the Financial Crisis," National Institute Economic Review, National Institute of Economic and Social Research, vol. 221, pages 31-43, July.
    14. Song, Jianhua & Zhang, Zhepei & So, Mike K.P., 2021. "On the predictive power of network statistics for financial risk indicators," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    15. Ștefan Cristian Gherghina & Daniel Ștefan Armeanu & Camelia Cătălina Joldeș, 2021. "COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach," JRFM, MDPI, vol. 14(8), pages 1-29, July.
    16. Cristiana Tudor, 2021. "Investors’ Trading Activity and Information Asymmetry: Evidence from the Romanian Stock Market," Risks, MDPI, vol. 9(8), pages 1-19, August.
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    More about this item

    Keywords

    systemic risk; spillover effect; Romania; FRM@RO; financial risk meter;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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