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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
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195703/ https://www.ncbi.nlm.nih.gov/pubmed/35024284 http://dx.doi.org/10.1186/s40854-021-00245-1 |
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author | Depren, Özer Kartal, Mustafa Tevfik Kılıç Depren, Serpil |
author_facet | Depren, Özer Kartal, Mustafa Tevfik Kılıç Depren, Serpil |
author_sort | Depren, Özer |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8195703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-81957032021-06-15 Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms Depren, Özer Kartal, Mustafa Tevfik Kılıç Depren, Serpil Financ Innov Research 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. Springer Berlin Heidelberg 2021-06-12 2021 /pmc/articles/PMC8195703/ /pubmed/35024284 http://dx.doi.org/10.1186/s40854-021-00245-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Depren, Özer Kartal, Mustafa Tevfik Kılıç Depren, Serpil Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms |
title | Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms |
title_full | Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms |
title_fullStr | Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms |
title_full_unstemmed | Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms |
title_short | Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms |
title_sort | recent innovation in benchmark rates (bmr): evidence from influential factors on turkish lira overnight reference interest rate with machine learning algorithms |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195703/ https://www.ncbi.nlm.nih.gov/pubmed/35024284 http://dx.doi.org/10.1186/s40854-021-00245-1 |
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