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首页> 外文期刊>American Journal of Mathematical and Computer Modelling >Hurst Exponent Analysis on the Ghana Stock Exchange
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Hurst Exponent Analysis on the Ghana Stock Exchange

机译:加纳证券交易所赫斯特指数分析

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This paper talks about the application of Hurst Index on the Ghana Stock Exchange (GSE). The aim of the paper was to find out, whether GSE daily returns have some autocorrelation (long-term dependency) and multifractality using the Hurst Index analysis. Hurst Index of daily returns of some selected stocks in the period of January 2018 to December 2018 constituting 247 trading days were computed using Rescale Range Method and the Periodogram Method. The findings show that, 91.7% of the stocks considered possess long-term dependency and only 8.3% shows multifractality. Besides, the average percentage error of the geometric fractional Brownian motion (GFBM) model was 16.68% with an efficiency accuracy of 83.32% whilst that of the geometric Brownian motion (GBM) model percentage error is 20.90% with an accuracy of 79.10%. This indicates that, the GFBM model yielded better predicting accuracy than GBM in the long-run and par predicting accuracy in the short-run.
机译:本文谈到了赫斯特指数在加纳证券交易所(GSE)的应用。本文的目的是找出,使用赫斯特指数分析,GSE日常返回是否具有一些自相关(长期依赖性)和多重性。 2018年1月至2018年12月期间的一些选定股票的仓鼠每日回报指数由rescale范围方法和期间地点方法计算构成247个交易日。调查结果表明,91.7%被认为具有长期依赖性,只有8.3%显示多重性。此外,几何分数褐色运动(GFBM)模型的平均百分比误差为16.68%,效率准确度为83.32%,而几何褐色运动(GBM)模型百分比误差为20.90%,精度为79.10%。这表明,GFBM模型在短期内的长期和PAR预测精度方面比GBM更好地预测精度。

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