Herramientas Estadísticas y Riesgos Financieros

This module covers the consultation and structuring of information, focusing on discrete and continuous profitability calculations. Students will learn how to collect and organize financial data for analysis, understand the concepts of discrete and continuous returns, and apply these calculations to real financial data.

This module explores the concept of Value at Risk (VaR) and Expected Shortfall (ES), focusing on their application within normal distributions. Students will learn how to apply VaR in financial risk management, understand its limitations, and perform simulations to calculate both VaR and ES as risk measures for financial portfolios.

This module delves into the characteristics of normal distribution in financial data, including concepts of skewness and kurtosis. Students will also learn about the t-student distribution as an alternative model for financial returns, and perform simulations using t-student distribution to better capture financial risk in cases where returns do not follow a normal distribution.

This module focuses on volatility modeling and forecasting using GARCH models. Students will learn to distinguish between historical and future distribution of returns, understand the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model for estimating time-varying volatility, and perform simulations using GARCH models to generate more accurate risk forecasts for financial assets.