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Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms

Author

Listed:
  • Özer Depren

    (Yapı Kredi Bank)

  • Mustafa Tevfik Kartal

    (Borsa İstanbul Financial Reporting and Subsidiaries Directorate)

  • Serpil Kılıç Depren

    (Yildiz Technical University)

Abstract

Some countries have announced national benchmark rates, while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of 2021. Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate (TLREF), this study examines the determinants of TLREF. In this context, three global determinants, five country-level macroeconomic determinants, and the COVID-19 pandemic are considered by using daily data between December 28, 2018, and December 31, 2020, by performing machine learning algorithms and Ordinary Least Square. The empirical results show that (1) the most significant determinant is the amount of securities bought by Central Banks; (2) country-level macroeconomic factors have a higher impact whereas global factors are less important, and the pandemic does not have a significant effect; (3) Random Forest is the most accurate prediction model. Taking action by considering the study’s findings can help support economic growth by achieving low-level benchmark rates.

Suggested Citation

  • Özer Depren & Mustafa Tevfik Kartal & Serpil Kılıç Depren, 2021. "Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
    • Handle: RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00245-1
    DOI: 10.1186/s40854-021-00245-1
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    Cited by:

    1. Hasan Murat Ertuğrul & Mustafa Tevfik Kartal & Serpil Kılıç Depren & Uğur Soytaş, 2022. "Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models," Energies, MDPI, vol. 15(20), pages 1-17, October.
    2. Alexey Mikhaylov & Hasan Dinçer & Serhat Yüksel, 2023. "Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.
    3. Mustafa Tevfik Kartal & Özer Depren, 2023. "Asymmetric relationship between global and national factors and domestic food prices: evidence from Turkey with novel nonlinear approaches," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
    4. Kılıç Depren, Serpil & Kartal, Mustafa Tevfik & Ertuğrul, Hasan Murat & Depren, Özer, 2022. "The role of data frequency and method selection in electricity price estimation: Comparative evidence from Turkey in pre-pandemic and pandemic periods," Renewable Energy, Elsevier, vol. 186(C), pages 217-225.
    5. Pontines, Victor & Rummel, Ole, 2023. "LIBOR meets machine learning: A Lasso regression approach to detecting data irregularities," Finance Research Letters, Elsevier, vol. 55(PA).

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    More about this item

    Keywords

    Benchmark rate; Determinants; Machine learning algorithms; Turkey;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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