Utilizing ARIMA Models for Financial Forecasting and Risk Management
Keywords:
ARIMA, Financial Forecasting, Risk Management, Time Series Analysis, Financial Modeling, Author Name, Scopus, Springer, Journal Name, Wissira, Journal Short Form, Wissira Press, Wissira Research Lab, Research Gate, SSRN, ISSN, Academia, UGC Care, PubMed, WOSAbstract
ARIMA (AutoRegressive Integrated Moving Average) models have become a cornerstone in financial time series forecasting and risk management. This manuscript investigates the application of ARIMA models to forecast financial trends and manage associated risks, drawing on historical market data to illustrate model selection, parameter estimation, and forecast validation processes. By comparing several ARIMA specifications, we identify the optimal model that balances predictive accuracy with model parsimony. The findings suggest that when calibrated properly, ARIMA models offer robust forecasting capabilities that can enhance decision-making in financial risk management. Implications for portfolio management and financial stability are discussed alongside recommendations for future research.
