Navigating the Financial Landscape: The Power and Limitations of the ARIMA Model

Abstract

In today's dynamic world, accurately predicting future trends is crucial. This is the reason why the Auto-Regressive Integrated Moving Average (ARIMA) model has become so important. ARIMA is a statistical tool that helps analyze time series data and make predictions. ARIMA has proven to be incredibly valuable in the finance industry in applications. It greatly improves stock price forecasting with accuracy rates exceeding 70%, which empowers traders and investors alike. By integrating with GARCH models, ARIMA can even reduce portfolio volatility by up to 20%, which is great for risk management purposes. It also plays a role in credit risk assessment, economic forecasting, and option pricing. However, it's worth noting that ARIMA does have some limitations to be aware of. For example, its linear assumptions can lead to errors during events like market crashes. The process of selecting parameters can also introduce subjectivity and uncertainty into the analysis. Additionally, while ARIMA performs well for short-term forecasting it tends to have more significant errors when it comes to long-term predictions. Furthermore, since ARIMA relies on historical data it may not fully account for external factors naturally occurring in the real world. To overcome these limitations and ensure financial analysis and forecasting it's crucial to complement ARIMA with other models.