Every trading desk, risk unit, and investment committee eventually runs into the same question: what is the data really telling us, and how much can we trust that signal? Financial econometrics is the toolkit built to answer exactly that — a blend of statistics, probability, and finance theory used to model, test, and forecast the behaviour of financial markets.

Through this course, the Arab British Fellowship Training Academy walks participants from the core statistical foundations to the specialised models used across trading, risk, and portfolio management, so they leave able to build, estimate, and validate their own financial time-series models rather than just reading someone else's output.

This course is built for:

• Finance and quantitative-analysis professionals who work directly with market data.
• Risk management, regulatory and compliance staff who need to interpret econometric models rather than just apply them.
• Researchers, academics and graduate students working on empirical finance questions.
• Anyone moving into quantitative finance who needs a solid statistical foundation.

By the end of the course, you will be able to:

• Model and forecast financial time-series data using the right econometric technique for the question at hand.
• Read and interpret the major financial econometric models instead of treating them as a black box.
• Turn empirical analysis and statistical inference into an actual market decision.
• Run independent research on real financial datasets, from data cleaning to econometric testing.

Module 1: Why Financial Data Behaves Differently

• What makes financial data distinct from other economic data, and why standard statistical assumptions often break down.
• The core econometric concepts you will use throughout the course, and how they map onto real finance problems.

Module 2: The Statistical Toolbox, From Descriptive Stats to Regression

• Descriptive statistics, probability distributions, hypothesis testing and confidence intervals as applied to market data.
• Simple and multiple regression analysis, and how to test and interpret results in a financial context.

Module 3: Modelling Time and Change, Time Series and Volatility

• Fundamentals of time-series analysis and forecasting for financial variables.
• Understanding volatility and applying ARCH, GARCH and related models.

Module 4: Measuring and Managing Risk, Value at Risk in Practice

• How financial risk is measured and managed in practice.
• Estimating and interpreting Value at Risk (VaR).

Module 5: Building Better Portfolios, Optimization Techniques

• Modern portfolio theory and the logic of diversification.
• Mean-variance optimization and portfolio construction techniques.

Module 6: Beyond a Single Asset, Panel and High-Frequency Data

• Panel data analysis: fixed effects, random effects and dynamic panel models.
• The characteristics of high-frequency financial data and how to model it econometrically.