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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

机译:最近在基准率的创新(BMR):来自机器学习算法的土耳其Lira过夜参考利率的影响因素的证据

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摘要

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.
机译:摘要有些国家已经宣布国家基准利率,而其他人一直在近期的走势中,伦敦银行同业拆借利率将在2021年的年底考虑到土耳其宣布土耳其里拉隔夜参考利率(TLREF)退役,这研究探讨TLREF的决定因素。在此背景下,全球三大因素,五国家层面的宏观因素,以及COVID-19大流行是使用2018年12月28日和2020年12月31日期间每天的数据,通过执行机器学习算法和普通最小二乘考虑。实证结果表明:(1)最显著的决定因素是各国央行购买证券的数额; (2)国家一级宏观经济因素有较高的影响,而全球性因素那么重要,而且这种流行病不具有显著效果; (3)随机森林是最准确的预测模型。考虑这项研究的结果采取行动,可以帮助实现低级别的基准利率,支持经济增长。

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