International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 7 (July 2025), Pages: 134-143

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 Original Research Paper

Modeling stock price trends and volatility in emerging markets using ARIMA and GARCH approaches

 Author(s): 

 Kevin Macharia 1, *, Edwine Atitwa 1, David Mugo 2, Millien Kawira 3

 Affiliation(s):

  1Department of Mathematics and Statistics, University of Embu, Embu, Kenya
  2Department of Computing and Information Technology, University of Embu, Embu, Kenya
  3Department of Physical Sciences, University of Embu, Embu, Kenya

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0002-7990-0946

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.07.013

 Abstract

Stock price prediction and volatility modeling are important for making financial decisions, especially in emerging markets like the Nairobi Securities Exchange (NSE). This study examines how well the Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models perform in forecasting stock prices and modeling volatility. The ARIMA (2,1,0) model was selected as the best fit using the Akaike Information Criterion (AIC), showing strong performance in capturing long-term price trends. However, an analysis of the residuals showed signs of volatility clustering, meaning ARIMA alone could not capture short-term fluctuations. To solve this, the study added a GARCH (1,1) model, which effectively captured changing volatility and improved prediction accuracy. The combined ARIMA-GARCH model reduced the Root Mean Squared Error (RMSE) from 3.1211 to 2.5786, demonstrating the value of including volatility modeling in financial time series. The results highlight the need for strong statistical models in emerging markets, where stock prices are often affected by external shocks and market inefficiencies. This research offers useful insights for investors, policymakers, and financial analysts by supporting better risk management and more accurate forecasting. Future studies could expand the model to include more stocks, macroeconomic data, and machine learning techniques to further improve results.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Stock prediction, Volatility modeling, ARIMA model, GARCH model, Emerging markets

 Article history

 Received 21 February 2025, Received in revised form 18 May 2025, Accepted 17 June 2025

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Macharia K, Atitwa E, Mugo D, and Kawira M (2025). Modeling stock price trends and volatility in emerging markets using ARIMA and GARCH approaches. International Journal of Advanced and Applied Sciences, 12(7): 134-143

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 Figures

  Fig. 1  Fig. 2  Fig. 3 

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5  Table 6  Table 7  Table 8  Table 9  Table 10  Table 11  Table 12  Table 13 

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