Author(s)

YiLin Peng

Affiliation(s)

South China Normal University, School of Mathematical Sciences, Guangzhou 510631, Guangdong, China.

Corresponding Author

YiLin Peng

ABSTRACT

As the global market becomes more and more open and volatile, it is of great significance to grasp the financial temporal volatility and correlation and accurately predict stock price behavior. This paper takes the Nasdaq Composite Index in 2020-2023 as the research object, constructs the ARIMA(1,1,(1,5)) model with GARCH(1,1) conforming to t distribution disturbance term to fit the trend of the index in 2020-2022, and predicts the trend in the first half of 2023. The results show that the model with GARCH effect gives a wider forecast confidence interval and can indicate the potential risk, but can not accurately reflect the real trend of the index. To improve the prediction accuracy, this paper takes Nasdaq index as the response variable, introduces S&P 500 index as the input variable, constructs an effective dynamic regression model through Granger causality test and EG cointegration test, and improves the model through cross-correlation function analysis. The results show that the forecast trend of the model is closer to the actual series of fluctuations, indicating that the S&P 500 index plays a promoting role in predicting the Nasdaq index, which provides a more reliable reference for investors when weighing the benefits and risks.

KEYWORDS

Volatility and correlation; Financial timing; ARIMA; GARCH; Cointegration; Dynamic regression

CITE THIS PAPER

YiLin Peng. Nasdaq index prediction based on ARIMA-GARCH model and dynamic regression. World Journal of Information Technology. 2025, 3(2): 45-53. DOI: https://doi.org/10.61784/wjit3033.

REFERENCES

[1] Akin I, Akin M. Behavioral finance impacts on US stock market volatility: An analysis of market anomalies. Behavioural Public Policy, 2024, 1-25.

[2] Muliabanta N H. Peramalan Volatilitas Return IHSG Menggunakan GARCH-Genetic Algorithm-Support Vector Regression. Institut Teknologi Sepuluh Nopember, 2025. 

[3] Nugroho D B, Kurniawati D, Panjaitan L P, et al. Empirical performance of GARCH, GARCH-M, GJR-GARCH and log-GARCH models for returns volatility[C]//Journal of Physics: Conference Series. IOP Publishing, 2019, 1307(1): 012003.

[4] Marisetty N. Prediction of Popular Global Stock Indexes Volatility by Using ARCH/GARCH Models. GARCH Models, 2024.

[5] Raza S, Shreevastava A, Meher B K, et al. Predictive Analysis of Volatility for BSE S&P GREENEX index using GARCH Family Models: A case study for Indian stock market. Revista de Stiinte Politice. Revue des Sciences Politiques, 2024, 84, 54-67.

[6] Roszyk N, Slepaczuk R. The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models. arXiv preprint arXiv:2407.16780, 2024.

[7] Shojaie A, Fox E B. Granger causality: A review and recent advances. Annual Review of Statistics and Its Application, 2022, 9(1): 289-319.

[8] Wang P C, Vo T T H. Stock price prediction based on dual important indicators using ARIMAX: A case study in Vietnam. Journal of Intelligent Systems, 2025, 34(1): 20240101.

[9] Akusta A. Exploring ceemdan decomposition for improved financial market forecasting: A case study on dow Jones index. Pamukkale üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 2024, (62): 19-35.

[10] Aylar E, Smeekes S, Westerlund J. Lag truncation and the local asymptotic distribution of the ADF test for a unit root. Statistical Papers, 2019, 60: 2109-2118.

[11] Yasmin S, Moniruzzaman M. Forecasting of area, production, and yield of jute in Bangladesh using Box-Jenkins ARIMA model. Journal of Agriculture and Food Research, 2024, 16: 101203.