Univariate and multivariate analyses of the asset returns using new statistical models and penalized regression techniques

Huda M. Alshanbari; Zubair Ahmad; Faridoon Khan; Saima K. Khosa; Muhammad Ilyas; Abd Al-Aziz Hosni El-Bagoury; Huda M. Alshanbari; Zubair Ahmad; Faridoon Khan; Saima K. Khosa; Muhammad Ilyas; Abd Al-Aziz Hosni El-Bagoury

doi:10.3934/math.2023994

AIMS Mathematics

2023, Volume 8, Issue 8: 19477-19503. doi: 10.3934/math.2023994

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Univariate and multivariate analyses of the asset returns using new statistical models and penalized regression techniques

1.
Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
2.
Department of Statistics, Quaid-i-Azam University, Islamabad 44000, Pakistan
3.
Pakistan Institute of Development Economics, Islamabad 44000, Pakistan
4.
Department of Mathematics and Statistics University of Saskatchewan, Saskatoon, SK, Canada
5.
Department of Statistics, University of Malakand, Dir (L), Chakdara, Khyber Pakhtunkhwa, Pakistan
6.
Higher Institute of Engineering and Technology at El-Mahala El-Kobra, Egypt

Received: 27 March 2023 Revised: 16 May 2023 Accepted: 22 May 2023 Published: 09 June 2023
MSC : 62F09, 62G34

The COVID-19 epidemic has had a profound effect on almost every aspect of daily life, including the financial sector, education, transportation, health care, and so on. Among these sectors, the financial and health sectors are the most affected areas by COVID-19. Modeling and predicting the impact of the COVID-19 epidemic on the financial and health care sectors is particularly important these days. Therefore, this paper has two aims, (i) to introduce a new probability distribution for modeling the financial data set (oil prices data), and (ii) to implement a machine learning approach to predict the oil prices. First, we introduce a new approach for developing new probability distributions for the univariate analysis of the oil price data. The proposed approach is called a new reduced exponential-$ X $ (NRE-$ X $) family. Based on this approach, two new statistical distributions are introduced for modeling the oil price data and its log returns. Based on certain statistical tools, we observe that the proposed probability distributions are the best competitors for modeling the prices' data sets. Second, we carry out a multivariate analysis while considering some covariates of oil price data. Dual well-known machine learning algorithms, namely, the least absolute shrinkage and absolute deviation (Lasso) and Elastic net (Enet) are utilized to achieve the important features for oil prices based on the best model. The best model is established through forecasting performance.
- logistic distribution,
- heavy-tailed distributions,
- T-X family,
- cryptocurrency,
- artificial neural networks,
- support vector regression
Citation: Huda M. Alshanbari, Zubair Ahmad, Faridoon Khan, Saima K. Khosa, Muhammad Ilyas, Abd Al-Aziz Hosni El-Bagoury. Univariate and multivariate analyses of the asset returns using new statistical models and penalized regression techniques[J]. AIMS Mathematics, 2023, 8(8): 19477-19503. doi: 10.3934/math.2023994

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Abstract

The COVID-19 epidemic has had a profound effect on almost every aspect of daily life, including the financial sector, education, transportation, health care, and so on. Among these sectors, the financial and health sectors are the most affected areas by COVID-19. Modeling and predicting the impact of the COVID-19 epidemic on the financial and health care sectors is particularly important these days. Therefore, this paper has two aims, (i) to introduce a new probability distribution for modeling the financial data set (oil prices data), and (ii) to implement a machine learning approach to predict the oil prices. First, we introduce a new approach for developing new probability distributions for the univariate analysis of the oil price data. The proposed approach is called a new reduced exponential-$ X $ (NRE-$ X $) family. Based on this approach, two new statistical distributions are introduced for modeling the oil price data and its log returns. Based on certain statistical tools, we observe that the proposed probability distributions are the best competitors for modeling the prices' data sets. Second, we carry out a multivariate analysis while considering some covariates of oil price data. Dual well-known machine learning algorithms, namely, the least absolute shrinkage and absolute deviation (Lasso) and Elastic net (Enet) are utilized to achieve the important features for oil prices based on the best model. The best model is established through forecasting performance.

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