The behavior of financial markets is characterized by frequent changes due to external factors such as government policies, economic events and various regulations. These factors can cause shifts in the means, variances, serial correlation and skewness of asset returns.

Modeling the dependency and volatility of financial returns has been a key issue in financial analysis, as it helps to quantify risk better. Analyzing historical market information can provide a framework for understanding risk and determining potential financial losses. The Value-at-Risk (VaR), recommended by the Basel II Accord, has become the most widely used risk measurement tool by analysts. The Value-at-Risk (VaR) enables financial institutions to measure, for a given level of probability, the largest expected loss of a portfolio during a particular period. One method for calculating the Value-at-Risk (VaR) is the variance–covariance approach, which looks at historical price movements and then uses probability theory to calculate the maximum loss within a specified confidence interval.

This paper aims to analyze the weekly returns of the financial indices of three countries, the United Kingdom (FTSE100), Germany (DAX30) and France (CAC40), over a period of 10 years, between September 2014 and September 2024. The first step of this analysis is to model the returns to account for various deviations from normality, such as skewness, excess kurtosis and autocorrelation. After modeling the data, the Value-at-Risk (VaR) is calculated using the variance–covariance approach. The analysis is carried out in Python, which, with powerful libraries and computational capabilities, proves to be the ideal tool. Finally, the empirical results show the Value-at-Risk (VaR) forecasts at the quantile levels of 0.95 and 0.99. This paper establishes that the delivery of this analysis as a modular API makes it suitable for wider use in risk management, as well as being highly extensible, contributing to better and more informed decisions.