IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i5p834-d361026.html
   My bibliography  Save this article

Market Volatility of the Three Most Powerful Military Countries during Their Intervention in the Syrian War

Author

Listed:
  • Viviane Naimy

    (Faculty of Business Administration and Economics, Notre Dame University—Louaize, Zouk Mikayel, Zouk Mosbeh 72, Lebanon)

  • José-María Montero

    (Department of Political Economy and Public Finance, Economic and Business Statistics, and Economic Policy, Faculty of Law and Social Sciences, University of Castilla-La Mancha, 45071 Toledo, Spain)

  • Rim El Khoury

    (Faculty of Business Administration and Economics, Notre Dame University—Louaize, Zouk Mikayel, Zouk Mosbeh 72, Lebanon)

  • Nisrine Maalouf

    (Financial Risk Management—Faculty of Business Administration and Economics, Notre Dame University—Louaize, Zouk Mikayel, Zouk Mosbeh 72, Lebanon)

Abstract

This paper analyzes the volatility dynamics in the financial markets of the (three) most powerful countries from a military perspective, namely, the U.S., Russia, and China, during the period 2015–2018 that corresponds to their intervention in the Syrian war. As far as we know, there is no literature studying this topic during such an important distress period, which has had very serious economic, social, and humanitarian consequences. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH (1, 1)) model yielded the best volatility results for the in-sample period. The weighted historical simulation produced an accurate value at risk (VaR) for a period of one month at the three considered confidence levels. For the out-of-sample period, the Monte Carlo simulation method, based on student t-copula and peaks-over-threshold (POT) extreme value theory (EVT) under the Gaussian kernel and the generalized Pareto (GP) distribution, overstated the risk for the three countries. The comparison of the POT-EVT VaR of the three countries to a portfolio of stock indices pertaining to non-military countries, namely Finland, Sweden, and Ecuador, for the same out-of-sample period, revealed that the intervention in the Syrian war may be one of the pertinent reasons that significantly affected the volatility of the stock markets of the three most powerful military countries. This paper is of great interest for policy makers, central bank leaders, participants involved in these markets, and all practitioners given the economic and financial consequences derived from such dynamics.

Suggested Citation

  • Viviane Naimy & José-María Montero & Rim El Khoury & Nisrine Maalouf, 2020. "Market Volatility of the Three Most Powerful Military Countries during Their Intervention in the Syrian War," Mathematics, MDPI, vol. 8(5), pages 1-21, May.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:834-:d:361026
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/5/834/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/5/834/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hussain, Saiful Izzuan & Li, Steven, 2015. "Modeling the distribution of extreme returns in the Chinese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 263-276.
    2. Zongrun Wang & Weitao Wu & Chao Chen & Yanju Zhou, 2010. "The exchange rate risk of Chinese yuan: Using VaR and ES based on extreme value theory," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 265-282.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Dolores Furió & Francisco J. Climent, 2013. "Extreme value theory versus traditional GARCH approaches applied to financial data: a comparative evaluation," Quantitative Finance, Taylor & Francis Journals, vol. 13(1), pages 45-63, January.
    5. Trinidad Segovia, J.E. & Fernández-Martínez, M. & Sánchez-Granero, M.A., 2019. "A novel approach to detect volatility clusters in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    6. Qian Chen & David E. Giles & Hui Feng, 2012. "The extreme-value dependence between the Chinese and other international stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 22(14), pages 1147-1160, July.
    7. Weixian Wei, 2002. "Forecasting stock market volatility with non-linear GARCH models: a case for China," Applied Economics Letters, Taylor & Francis Journals, vol. 9(3), pages 163-166.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Wei, Yu & Chen, Wang & Lin, Yu, 2013. "Measuring daily Value-at-Risk of SSEC index: A new approach based on multifractal analysis and extreme value theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2163-2174.
    10. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
    11. Paul Embrechts & Sidney Resnick & Gennady Samorodnitsky, 1999. "Extreme Value Theory as a Risk Management Tool," North American Actuarial Journal, Taylor & Francis Journals, vol. 3(2), pages 30-41.
    12. Lin, Xiaoqiang & Fei, Fangyu, 2013. "Long memory revisit in Chinese stock markets: Based on GARCH-class models and multiscale analysis," Economic Modelling, Elsevier, vol. 31(C), pages 265-275.
    13. Hou, Yang & Li, Steven, 2016. "Information transmission between U.S. and China index futures markets: An asymmetric DCC GARCH approach," Economic Modelling, Elsevier, vol. 52(PB), pages 884-897.
    14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Piotr Fiszeder & Marta Ma³ecka, 2022. "Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(4), pages 939-967, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Hung-Chun & Chiang, Shu-Mei & Cheng, Nick Ying-Pin, 2012. "Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 78-91.
    2. Ñíguez, Trino-Manuel & Perote, Javier, 2017. "Moments expansion densities for quantifying financial risk," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 53-69.
    3. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    4. Kanungo, Rama Prasad, 2021. "Uncertainty of M&As under asymmetric estimation," Journal of Business Research, Elsevier, vol. 122(C), pages 774-793.
    5. Daglis, Theodoros & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Papadakis, Theodoulos Eleftherios, 2020. "The forecasting ability of solar and space weather data on NASDAQ’s finance sector price index volatility," Research in International Business and Finance, Elsevier, vol. 52(C).
    6. Kumar Arya & Sahoo Jyotirmayee & Sahoo Jyotsnarani & Nanda Subhashree & Debyani Devi, 2024. "Exploring Asymmetric GARCH Models for Predicting Indian Base Metal Price Volatility," Folia Oeconomica Stetinensia, Sciendo, vol. 24(1), pages 105-123.
    7. repec:agr:journl:v:4(621):y:2019:i:4(621):p:35-52 is not listed on IDEAS
    8. Tetsuya Takaishi, 2021. "Time-varying properties of asymmetric volatility and multifractality in Bitcoin," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-21, February.
    9. M. MALLIKARJUNA & R. Prabhakara RAO, 2019. "Volatility experience of major world stock markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(621), W), pages 35-52, Winter.
    10. Iorember, Paul & Sokpo, Joseph & Usar, Terzungwe, 2017. "Inflation and Stock Market Returns Volatility: Evidence from the Nigerian Stock Exchange 1995Q1-2016Q4: An E-GARCH Approach," MPRA Paper 85656, University Library of Munich, Germany.
    11. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(4), pages 445-463, October.
    12. Vanshu Mahajan & Sunil Thakan & Aashish Malik, 2022. "Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models," Economies, MDPI, vol. 10(5), pages 1-20, April.
    13. Ma, Feng & Wei, Yu & Huang, Dengshi & Chen, Yixiang, 2014. "Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 171-180.
    14. Ciccarelli, Nicola, 2016. "Semiparametric Efficient Adaptive Estimation of the PTTGARCH model," MPRA Paper 72021, University Library of Munich, Germany.
    15. Mohd Aminul Islam, 2014. "A Study on the Performance of Symmetric and Asymmetric GARCH Models in Estimating Stock Returns Volatility," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 2(4), pages 182-192.
    16. Trifonov, Juri & Potanin, Bogdan, 2024. "GARCH-M model with an asymmetric risk premium: Distinguishing between ‘good’ and ‘bad’ volatility periods," International Review of Financial Analysis, Elsevier, vol. 91(C).
    17. Nagaraj Naik & Biju R. Mohan, 2021. "Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market," Mathematics, MDPI, vol. 9(14), pages 1-18, July.
    18. Roni Bhowmik & Wu Chao & Jewel Roy Kumar & Wang Shouyang, 2017. "A Study on the Volatility of the Bangladesh Stock Market — Based on GARCH Type Models," Journal of Systems Science and Information, De Gruyter, vol. 5(3), pages 193-215, June.
    19. N’dri Konan Léon, 2015. "Forecasting Stock Return Volatility: Evidence from the West African Regional Stock Market," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 5(6), pages 1-2.
    20. Chebbi, Ali & Hedhli, Amel, 2022. "Revisiting the accuracy of standard VaR methods for risk assessment: Using the Copula–EVT multidimensional approach for stock markets in the MENA region," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 430-445.
    21. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020. "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, vol. 2(3), pages 241-258, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:834-:d:361026. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.