My Courses

Software

Course	Description	Contents	Places Taught
Introduction to R Programming	Learn the fundamentals of programming with R	Basic concepts of programming Data types and variables Control structures Functions Data frames and lists Data manipulation Data Visualization Statistical Analysis	Universidad Carlos III de Madrid (UC3M) Universidad del Rosario (UROSARIO)

Statistics: Prediction and Causal Inference

Course	Description	Contents	Places Taught
Mastering Time Series Analysis: ARIMA Models for Forecasting	The course will cover ARIMA models, which are widely used for time series analysis and forecasting. At the end of the course, students will gain a comprehensive understanding of how to analyze mean processes and apply the concepts to real-world time series data.	ARIMA Models Causality and Invertibility Prediction and Loss Function Stationarity: Dickey-Fuller, KPSS, Ljung-Box Box-Jenkins Routine	Universidad del Rosario (UROSARIO)
Volatility Forecasting: Advanced Techniques for Time Series Data	The course focuses on teaching students how to estimate and forecast volatility in time series data. This includes exploring second moments and historical variances in ARIMA models, as well as using the exponentially weighted moving average (EWMA) method. The course also covers more advanced techniques such as GARCH models for volatility forecasting, as well as the asymmetric GARCH model, which considers the impact curve, sign, and bias test. By the end of the course, students will be able to apply these concepts to real-world data and make informed decisions regarding volatility forecasting.	ARIMA Models: Second Moment Historical Variance EWMA GARCH ASYMMETRIC GARCH: New Impact Curve, Sign and Bias test.	Universidad del Rosario (UROSARIO)
Data Mining and Causal Inference in Finance: Advanced Techniques for Improved Decision-Making	This course on Data Mining and Causal Inference in Finance covers basic concepts and techniques of data mining for finance data. You will learn best practices for data visualization in causal inference using tables and plots. Regression analysis will be covered, including interpreting regression results and addressing common problems such as standard errors, collinearity, and endogeneity. Through hands-on exercises, you will explore how to use causal diagrams to improve your causal inference and how to address confounding using closing back doors and opening front doors. Regression discontinuity analysis, including sharp, fuzzy, and kernel estimation, selection bias, and other issues will also be explored. You will learn about difference in differences analysis, including estimating the average treatment effect, the two-way fixed effects model, and addressing limitations such as parallel trends and double robust estimation. Imputation methods such as generalized synthetic control and matrix completion will be covered. Advanced topics in causal inference will include propensity score matching, panel data analysis, and machine learning methods. Join us to discover the power of data mining and causal inference in finance, and learn how to make informed decisions based on the data	Introduction to data mining: basic concepts and techniques Plots and tables: best practices for data visualization in causal inference Fitting data: how to identify and use causal diagrams to improve causal inference Closing back doors: methods for addressing confounding in causal inference. Regression inference: understanding and interpreting regression results Dealing with standard errors: sandwich and bootstrap methods Collinearity and endogeneity: how to address these common problems in causal inference Regression discontinuity: sharp, fuzzy, and kernel estimation; selection bias and other issues Difference in difference: ATT, TWFE, parallel trends, limitations, double robust estimation, and more Imputation methods: generalized synthetic control and matrix completion Advanced topics in causal inference: propensity score matching, panel data analysis, machine learning methods, and more in finance Applications of causal inference: case studies and real-world examples from various finance fields, such as corporate finance, asset management, and financial regulation	Online

Finance

Course	Description	Contents	Places Taught
Corporate Finance	Learn to identify corporate governance and its conflicts, analyze and evaluate investment projects, estimate the cost of capital, evaluate financial leverage and working capital management, and value a company from the perspective of cash flows.	Corporate Governance Financial Statement Analysis CAPM Model Capital Cost Capital Structure Modigliani and Miller Theory Dividend Policy	Universidad del Rosario (UROSARIO)
Options and Derivatives: Pricing, Risk Management and Arbitrage	This course offers students a comprehensive understanding of options and derivatives in financial markets. They will learn about risk and uncertainty concepts and how to use future and option contracts for risk management. Additionally, students will gain the necessary skills to accurately price and value contracts for both arbitrage and risk management purposes.	Risk and Uncertainty Future Contracts Option Contracts: European, American, Asian, Barrier, Lookback. Black and Scholes Binomial Tree Montecarlo Simulation Delta Hedging Delta-Gamma Hedging Implied Volatility	Universidad Carlos III de Madrid (UC3M) Universidad del Rosario (UROSARIO)
Quantitative Finance: Pricing and Risk Analysis	This course covers quantitative methods for pricing financial instruments, including Monte Carlo simulation, stochastic processes, Markov chains, and the Black-Scholes approach. It also explores asymptotic theory and the Finite Difference Method, including consistency, stability, and convergence analysis. The practical application of these methods is illustrated by pricing European and American options.	Pricing with Montecarlo Stochastics Processes Markov Chains Black and Scholes Approach: Euler-Maruyama, and Milstein. Asymptotic Theory: Central Limit Theory and Kolmogorow Law Finite Difference Method First and Second Order Differences Forward, Backward and Central Approach Explicit, Implicit, and Crank-Nicolson Methods Consistency, Estability and Convergency Analysis Aplication on Europeans and American Options	Universidad del Rosario (UROSARIO)
Risk Management for Portfolio Success	This course covers risk management rules such as Basilea I, II, and III, coherent risk measures, and their application in portfolio management. Students will learn about Value at Risk (VaR), Expected Shortfall, and Stress Testing, and their advantages and disadvantages. The course focuses on the practical application of these risk measures to portfolio management, allowing students to make informed investment decisions. By the end of the course, students will have gained valuable knowledge and skills required to manage risks effectively.	Risk Management Rules: Basilea I, II, II Coherent Risk Measures Value at Risk VaR: Historical, Non-conditional Expected Shortfall Advantages and disadvantage Analysis Stress Testing Application on Portfolio Management	Universidad del Rosario (UROSARIO)