IDEAS home Printed from https://ideas.repec.org/a/bpj/ecqcon/v35y2020i1p1-15n3.html
   My bibliography  Save this article

Predicting Federal Funds Rate Using Extreme Value Theory

Author

Listed:
  • Dey Ashim Kumar

    (Department of Mathematics, Lamar University, Beaumont, TX 77710-0009, USA)

  • Das Kumer Pial

    (University of Louisiana, Lafayette, LA 70503-2014, USA)

Abstract

The extreme value theory (EVT) is used to assess the risk of extreme events caused by natural calamities or untoward circumstances in the social and economic sectors. The theory can be used to study the frequency of rare events and to build up a predictive model so that one can attempt to forecast the frequency of such future extreme events such as a financial collapse and the amount of damage from such a collapse. Even though many statistical techniques have been used to analyze the manner in which the Federal Reserve determines the level of the Federal Fund Rates, no known study has used EVT to analyze and predict the extreme fund rates. In this study, the US Federal Funds Rate, one of the most publicized and important economic indicators in the financial world, from 1954–2019 has been analyzed. The contributions of this study are: (1) to provide an appropriate model for the normalized Federal Funds Rate data; (2) to compare several estimation techniques in estimating parameters for two possible models; (3) to predict the maximum economic return rate from a Federal Funds Rate in the future by using the concept of the return period; and (4) to investigate the bias of estimated parameters applying a simulation study. Simulated data and real financial data are used for the study, and the outcome satisfies the efficiency of its application.

Suggested Citation

  • Dey Ashim Kumar & Das Kumer Pial, 2020. "Predicting Federal Funds Rate Using Extreme Value Theory," Stochastics and Quality Control, De Gruyter, vol. 35(1), pages 1-15, June.
    • Handle: RePEc:bpj:ecqcon:v:35:y:2020:i:1:p:1-15:n:3
    DOI: 10.1515/eqc-2020-0003
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/eqc-2020-0003
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/eqc-2020-0003?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Younes Bensalah, 2000. "Steps in Applying Extreme Value Theory to Finance: A Review," Staff Working Papers 00-20, Bank of Canada.
    2. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    3. Das, Kumer Pial & Dey, Asim Kumer, 2016. "Quantifying the risk of extreme aviation accidents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 345-355.
    4. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 117-137, May.
    Full references (including those not matched with items on IDEAS)

    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. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    2. Suarez, R, 2001. "Improving Modeling of Extreme Events using Generalized Extreme Value Distribution or Generalized Pareto Distribution with Mixing Unconditional Disturbances," MPRA Paper 17443, University Library of Munich, Germany.
    3. Muteba Mwamba, John & Mhlanga, Isaah, 2013. "Extreme conditional value at risk: a coherent scenario for risk management," MPRA Paper 64387, University Library of Munich, Germany.
    4. Suarez, Ronny, 2009. "Improving Modeling of Extreme Events using Generalized Extreme Value Distribution or Generalized Pareto Distribution with Mixing Unconditional Disturbances," MPRA Paper 17482, University Library of Munich, Germany.
    5. Tomáš Jeøábek, 2020. "The Efficiency of GARCH Models in Realizing Value at Risk Estimates," ACTA VSFS, University of Finance and Administration, vol. 14(1), pages 32-50.
    6. Zhi-Fu Mi & Yue-Jun Zhang, 2011. "Estimating the 'value at risk' of EUA futures prices based on the extreme value theory," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 35(2/3/4), pages 145-157.
    7. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
    8. Knowledge Chinhamu & Chun-Kai Huang & Chun-Sung Huang & Jahvaid Hammujuddy, 2015. "Empirical Analyses of Extreme Value Models for the South African Mining Index," South African Journal of Economics, Economic Society of South Africa, vol. 83(1), pages 41-55, March.
    9. Feng, Zhen-Hua & Wei, Yi-Ming & Wang, Kai, 2012. "Estimating risk for the carbon market via extreme value theory: An empirical analysis of the EU ETS," Applied Energy, Elsevier, vol. 99(C), pages 97-108.
    10. Ana-Maria Gavril, 2009. "Exchange Rate Risk: Heads or Tails," Advances in Economic and Financial Research - DOFIN Working Paper Series 35, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    11. Feng Ren & David E. Giles, 2007. "Extreme Value Analysis of Daily Canadian Crude Oil Prices," Econometrics Working Papers 0708, Department of Economics, University of Victoria.
    12. John Kwaku Mensah Mawutor & Kezia Bortey & Bernardine Ansah & Faustina Osei- Frimpong & Worlanyo Kumassah, 2015. "Credit Risk Management and Profitability of Banks Listed on the Ghana Stock Exchange," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(7), pages 396-406.
    13. Kokoszka Piotr & Miao Hong & Stoev Stilian & Zheng Ben, 2019. "Risk Analysis of Cumulative Intraday Return Curves," Journal of Time Series Econometrics, De Gruyter, vol. 11(2), pages 1-31, July.
    14. Gijbels, Irène & Sznajder, Dominik, 2013. "Testing tail monotonicity by constrained copula estimation," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 338-351.
    15. G. D. Gettinby & C. D. Sinclair & D. M. Power & R. A. Brown, 2004. "An Analysis of the Distribution of Extreme Share Returns in the UK from 1975 to 2000," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5‐6), pages 607-646, June.
    16. S. A. Abu Bakar & Saralees Nadarajah & Z. A. Absl Kamarul Adzhar, 2018. "Loss modeling using Burr mixtures," Empirical Economics, Springer, vol. 54(4), pages 1503-1516, June.
    17. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    18. Amira Dridi & Mohamed El Ghourabi & Mohamed Limam, 2012. "On monitoring financial stress index with extreme value theory," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 329-339, March.
    19. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    20. Cristina Sommacampagna, 2002. "Stima del Value-at-Risk con il Filtro di Kalman," Rivista di Politica Economica, SIPI Spa, vol. 92(6), pages 147-174, November-.

    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:bpj:ecqcon:v:35:y:2020:i:1:p:1-15:n:3. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.