Forecasting Selected International Stock Indices returns by Using Arima Model

This article investigates the application of Autoregressive Integrated Moving Average (ARIMA) models in forecasting returns for five major international stock indices: FTSE 100, HANG SENG, NIKKEI 225, NIFTY 50, and S&P 500. Grounded in a comprehensive methodology, the study begins by tracing the evolution of ARIMA models and their pivotal role in analysing complex temporal patterns across diverse sectors, including finance and economics. Methodologically, the research encompasses data collection from financial databases, preprocessing to ensure data quality and stationarity, ARIMA model specification through Box-Jenkins methodology, parameter estimation, and thorough validation against historical data. Results highlight varying model performances across indices, with the FTSE 100 and S&P 500 exhibiting lower prediction errors compared to the HANG SENG and NIKKEI 225, indicative of differing levels of market volatility and predictability. The analysis integrates unit root tests, ARIMA model specifications (e.g., ARIMA(2,0,1) for FTSE 100 and ARIMA(3,0,3) for S&P 500), forecast accuracy assessments, and residual diagnostics, providing insights into model adequacy and areas for further refinement. Author underscores the robustness of ARIMA models in capturing and forecasting the intricate dynamics of international stock markets, while acknowledging challenges posed by market volatility and non-linearities. The study's findings contribute to a nuanced understanding of each index's predictive behaviour, informing investment strategies and risk management practices in global financial markets. Future research directions could explore advanced time series techniques or hybrid models to enhance predictive accuracy, particularly for indices exhibiting higher volatility. Overall, this research underscores the pivotal role of ARIMA models in empirical finance, offering actionable insights for stakeholders navigating the complexities of international stock market forecasting.