IDEAS home Printed from https://ideas.repec.org/a/wly/ijfiec/v26y2021i1p1459-1468.html
   My bibliography  Save this article

A kernel fuzzy twin SVM model for early warning systems of extreme financial risks

Author

Listed:
  • Xun Huang
  • Fanyong Guo

Abstract

It is an important component of risk management in financial markets to develop an early warning systems (EWS) for extreme financial risk. In this paper, we establish a novel EWS called kernel fuzzy twin support vector machine (KFT‐SVM). Unlike T‐SVM, KFT‐SVM can deal with the noises and outliners in dataset and the fuzzy dataset with a lot of potential uncertain but important factors in financial markets by introducing the fuzzy approach. More importantly, the introduced kernel method can aid the fuzzy approach to achieve more valuable fuzzy memberships by transporting dataset from the input space to the kernel space and further improve the generalization performance of T‐SVM. Computational comparisons of KFT‐SVM against SVM, T‐SVM and FT‐SVM indicate the significant superiority of our proposed KFT‐SVM. Furthermore, we have investigated the favourable ability of KFT‐SVM for overcoming the class imbalance problem by comparison with that combined with the resampling method of the synthetic minority over‐sampling technique (SMOTE). The experimental result shows that our proposed KFT‐SVM can effectively overcome the class imbalance problem.

Suggested Citation

  • Xun Huang & Fanyong Guo, 2021. "A kernel fuzzy twin SVM model for early warning systems of extreme financial risks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1459-1468, January.
  • Handle: RePEc:wly:ijfiec:v:26:y:2021:i:1:p:1459-1468
    DOI: 10.1002/ijfe.1858
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/ijfe.1858
    Download Restriction: no

    File URL: https://libkey.io/10.1002/ijfe.1858?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Schreiber, Irene & Müller, Gernot & Klüppelberg, Claudia & Wagner, Niklas, 2012. "Equities, credits and volatilities: A multivariate analysis of the European market during the subprime crisis," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 57-65.
    2. Tsay, Ruey S. & Ando, Tomohiro, 2012. "Bayesian panel data analysis for exploring the impact of subprime financial crisis on the US stock market," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3345-3365.
    3. Cumperayot, Phornchanok & Kouwenberg, Roy, 2013. "Early warning systems for currency crises: A multivariate extreme value approach," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 151-171.
    4. Pelizzon, Loriana & Sartore, Domenico, 2013. "Deciphering the Libor and Euribor Spreads during the subprime crisis," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 565-585.
    5. Wang, Ping & Moore, Tomoe, 2012. "The integration of the credit default swap markets during the US subprime crisis: Dynamic correlation analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(1), pages 1-15.
    6. Lang, Michael & Schmidt, Paul G., 2016. "The early warnings of banking crises: Interaction of broad liquidity and demand deposits," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 1-29.
    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. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    2. Yishuai Tian & Yifan Wu, 2024. "Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN," Mathematics, MDPI, vol. 12(11), pages 1-31, May.
    3. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

    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. Charles Ka Yui Leung & Edward Chi Ho Tang, 2023. "The dynamics of the house price‐to‐income ratio: Theory and evidence," Contemporary Economic Policy, Western Economic Association International, vol. 41(1), pages 61-78, January.
    2. Zhi-Qiang Jiang & Gang-Jin Wang & Askery Canabarro & Boris Podobnik & Chi Xie & H. Eugene Stanley & Wei-Xing Zhou, 2018. "Short term prediction of extreme returns based on the recurrence interval analysis," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 353-370, March.
    3. Miroslav Mateev, 2019. "Volatility relation between credit default swap and stock market: new empirical tests," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(4), pages 681-712, October.
    4. Guarin, Alexander & Lozano, Ignacio, 2017. "Credit funding and banking fragility: A forecasting model for emerging economies," Emerging Markets Review, Elsevier, vol. 32(C), pages 168-189.
    5. Benjamin Hippert & André Uhde & Sascha Tobias Wengerek, 2019. "Portfolio benefits of adding corporate credit default swap indices: evidence from North America and Europe," Review of Derivatives Research, Springer, vol. 22(2), pages 203-259, July.
    6. Suzanne Salloy & Irfan Akbar Kazi, 2013. "Contagion effect due to Lehman Brothers’ bankruptcy and the global financial crisis: From the perspective of the Credit Default Swaps’ G14 dealers," Erudite Working Paper 2013-02, Erudite.
    7. Yufeng Chen & Wenqi Li & Xi Jin, 2018. "Volatility Spillovers between Crude Oil Prices and New Energy Stock Price in China," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 43-62, December.
    8. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).
    9. Mohammad Karimi & Marcel‐Cristian Voia, 2019. "Empirics of currency crises: A duration analysis approach," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 428-449, July.
    10. Bai, Jushan & Ando, Tomohiro, 2013. "Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors," MPRA Paper 52785, University Library of Munich, Germany, revised Dec 2013.
    11. Dejan Šoškić, 2015. "Global Financial Reform Since 2008: Achievements and Shortcomings," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 62(3), pages 385-400, June.
    12. repec:zbw:rwirep:0243 is not listed on IDEAS
    13. Antunes, António & Bonfim, Diana & Monteiro, Nuno & Rodrigues, Paulo M.M., 2018. "Forecasting banking crises with dynamic panel probit models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 249-275.
    14. Amstad, Marlene & Remolona, Eli & Shek, Jimmy, 2016. "How do global investors differentiate between sovereign risks? The new normal versus the old," Journal of International Money and Finance, Elsevier, vol. 66(C), pages 32-48.
    15. Tomoe Moore & Ali Mirzaei, 2016. "The Impact of the Global Financial Crisis on Industry Growth," Manchester School, University of Manchester, vol. 84(2), pages 159-180, March.
    16. Riedel, Christoph & Thuraisamy, Kannan S. & Wagner, Niklas, 2013. "Credit cycle dependent spread determinants in emerging sovereign debt markets," Emerging Markets Review, Elsevier, vol. 17(C), pages 209-223.
    17. Phornchanok Cumperayot & Casper G. de Vries, 2006. "Large Swings in Currencies driven by Fundamentals," Tinbergen Institute Discussion Papers 06-086/2, Tinbergen Institute.
    18. Patrick Augustin, 2012. "Sovereign Credit Default Swap Premia," Working Papers 12-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    19. Tjeerd M. Boonman & Jan P. A. M. Jacobs & Gerard H. Kuper & Alberto Romero, 2019. "Early Warning Systems for Currency Crises with Real-Time Data," Open Economies Review, Springer, vol. 30(4), pages 813-835, September.
    20. Cheng, Xian & Zhao, Haichuan, 2019. "Modeling, analysis and mitigation of contagion in financial systems," Economic Modelling, Elsevier, vol. 76(C), pages 281-292.
    21. Jing, Zhongbo, 2015. "On the relation between currency and banking crises in developing countries, 1980–2010," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 267-291.

    More about this item

    Statistics

    Access and download statistics

    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:wly:ijfiec:v:26:y:2021:i:1:p:1459-1468. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1076-9307/ .

    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.