Econometrics and Financial Time Series
Study regression diagnostics, financial time series, volatility and forecast evaluation with applied econometrics.
Why study Econometrics and Financial Time Series?
Financial observations arrive in time, share common shocks and often change their behaviour. Econometrics helps you test a relationship or forecast without silently assuming that returns behave like independent laboratory measurements.
Who this is for
For learners comfortable with regression, probability and basic Python or R.
What you should be able to do
- Diagnose regression assumptions and estimation problems.
- Model time series, stationarity and cointegration.
- Evaluate volatility models and out-of-sample forecasts.
An example of the work this prepares you for
A forecast comparison with time-ordered evaluation, residual diagnostics and a discussion of changing market conditions.
Prerequisites and suggested preparation
You should be comfortable with the background covered in these programmes. Equivalent knowledge from university study or independent learning is also suitable.
A preview of the learning journey
The lessons build towards these kinds of reasoning and practical work.
Diagnose a relationship
Identify when regression assumptions undermine an interpretation.
Model observations through time
Reason about persistence, changing volatility and long-run relationships.
Evaluate a forecast
Compare a model with a simple benchmark on genuinely later observations.
Members work through the detailed lessons, derivations, worked examples and solutions inside the learning dashboard.
Explore lesson previews by topic
Open a topic preview to see its introduction, learning objectives and prerequisites. Previous and next links let you explore the lessons in study order. Full lessons are available in the member dashboard; selected samples are free to read in full.
Free resources to start learning
Use these alongside the programme or to prepare independently. Start with the introductory material, work through an example, then return to the research papers when the underlying ideas are familiar.
Introduction to Econometrics with R
Christoph Hanck, Martin Arnold, Alexander Gerber and Martin Schmelzer
Practise regression diagnostics and inference with reproducible R examples.
Forecasting: Principles and Practice
Rob J Hyndman and George Athanasopoulos · third edition
Study time-series exploration, forecast evaluation and benchmark methods.
Topics in Mathematics with Applications in Finance
MIT OpenCourseWare · 18.S096
Use the regression and time-series lectures for a financial bridge.
Kenneth R. French Data Library
Dartmouth · Tuck School of Business
Use public factor returns for an empirical regression exercise.
Links reviewed 14 October 2026. Free refers to the linked reading, notes or videos; printed books and certificates may cost extra.
Find more papers and learning materials in the SelfMadeQuant Directory.
Where this programme fits
- Systematic Trading and Quantitative Research · required programme
- Portfolio, Factor and Asset-Management Quant · required programme
- Risk and Model Validation Quant · elective programme
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