Practitioner course in quantitative foundations
Time Series Analysis and Financial Econometrics
The time series methods behind trading signals and risk forecasts: stationarity, autocorrelation, ARMA models, unit roots, volatility clustering and honest forecast evaluation. It also teaches financial econometrics: regression, its failures and their fixes.
Financial data arrives as time series, and the order matters. Yesterday’s volatility predicts today’s; a spread that drifts too far tends to come back; a model fitted to calm years fails in a crisis. Time series analysis is the set of methods that take the ordering seriously.
What is time series analysis used for in finance?
Forecasting volatility for risk and option pricing, modeling spreads for statistical arbitrage, testing whether a signal leads returns, and measuring how relationships between markets change. Each application appears in the course on real data.
Why do most backtests fail out of sample?
Financial econometrics is how a researcher tells a real effect from a lucky one. It takes regression and hypothesis testing and adapts them to returns, which are noisy, fat-tailed, dependent over time and searched through by thousands of people for the same patterns. This course teaches the methods and the discipline.
Mostly because many ideas were tried and the best one was kept. A strategy chosen from a hundred candidates will look good by chance. The final unit shows how to count the trials, adjust the statistics for them and estimate how much of a backtested Sharpe ratio is likely to survive. Employers ask about this in interviews because it costs them money when it is ignored.
Who is this course for?
Practitioners building trading signals or risk forecasts, and students preparing for statistical arbitrage or machine learning courses.
What you will be able to do
- Test a price or return series for stationarity and transform it appropriately.
- Identify, fit and diagnose ARMA models for returns and spreads.
- Recognize volatility clustering and fit a first GARCH model.
- Test for unit roots and explain why they matter for spread trading.
- Evaluate forecasts out of sample against a naive benchmark.
- Estimate and interpret regressions of returns, and choose standard errors that are robust to the features of market data.
Syllabus, module by module
Time series data
Prices, returns, sampling frequency, missing data and alignment across markets.
Stationarity and autocorrelation
Weak stationarity, the autocorrelation and partial autocorrelation functions, and their sampling error.
ARMA models
Autoregressive and moving average models, identification, estimation and residual checks.
Unit roots
Random walks, the Dickey-Fuller and KPSS tests, and differencing.
Regression on returns
Ordinary least squares, what the coefficients mean, and the assumptions market data breaks.
Standard errors that survive
Heteroskedasticity, autocorrelation, Newey-West and clustered errors.
What every course contains
Every paid course comes with the first seven and the certificate, whichever level it sits at. The proctored exam and the credential come when the course is taken as part of a Certificate, Diploma or Fellowship. Everything is self-paced: the material is yours to study in any order, at any hour, for the whole of the license.
- Workbooks. Written to train the thinking patterns and the important concepts of the course, with worked problems and the reasoning behind each answer, so that you learn to think the way the subject is thought.
- Audio edition. Focused, well-presented recordings with explainers and examples of the material, made to be interesting, relevant and entertaining, so that the general subject can be learned away from the desk.
- Notes. Pre-produced notes with every fact the course relies on, written out in full, so that nothing has to be transcribed from the films or the audio.
- Cheat sheet. One sheet with every important learning concept of the course on it, to keep beside you during the work and to revise from before the exam.
- Named sources. Every fact in the notes names its source, the paper, the book or the dataset, for anyone who wants to dig in further.
- Explainer films. Focused films, in the style of the sample films on the catalog page, that walk through the most difficult concepts of the course with visual explanations of a high standard.
- Transcript. A full transcript of every film and every audio recording, searchable and quotable.
- Proctored exam. Taken as part of a Certificate, Diploma or Fellowship, the course ends in a proctored multiple-choice questionnaire, with a pass mark of 60% and three retakes included. A course taken on its own is assessed by its coursework and has no exam.
- Verifiable certificate. A certificate of completion that a third party can verify: an employer sends the code on it to the credentials office and receives written confirmation of the course, the date and the result.
- Credential of the program. The Certificate, Diploma or Fellowship of the program, track or path the course belongs to, awarded when every course in it is passed. Which one depends on the program you choose.
How the certificate is earned
An assessed project: you forecast a financial series of your choice, compare at least three models against a naive benchmark out of sample, and submit the code with a short report. On a pass you receive the Course certificate in Time Series Analysis and Financial Econometrics, issued by the Institute with a verification code. A course taken as part of a Certificate, Diploma or Fellowship ends in a proctored online exam with a pass mark of 60%. The fee includes three retakes; each attempt after that carries a reduced proctored exam fee. A course taken on its own is assessed by its coursework and has no exam. Read how certification works.
Before you start
This course assumes the material of the courses below, or the same knowledge gained elsewhere.
This course counts toward
- Foundation Certificate in Computational FinanceThe entry quantitative finance certificate, and the opening stage of both the Professional Diploma and the Fellowship. Six courses that settle the mathematics, statistics, data and market knowledge every later course assumes, closed by a capstone.
- Certificate in Algorithmic TradingSeven courses and a capstone that take a strategy from idea to a costed, sized and validated system, traded on paper and reported the way a trading desk would read it.
- Certificate in Statistical ArbitrageSeven courses and a capstone on market-neutral trading: time series foundations, pairs and baskets, cointegration, regimes, costs and the validation that keeps spread strategies honest.
- Certificate in Quantitative Portfolio ManagementSeven courses and a capstone on building portfolios with numbers: mean-variance and its failures, robust optimization, Black-Litterman views, factors, asset allocation, bonds, attribution and risk.
- Certificate in Machine Learning for TradingFive courses and a capstone on applying machine learning to markets without fooling yourself: labeling, features, purged validation, deep and language models, regimes and reinforcement learning.
- Professional Diploma in Algorithmic TradingThe top credential on the practitioner path. Twenty-one courses and an 80-hour capstone covering everything between the mathematics and a book you could defend to a risk committee.
- Fellowship in Computational FinanceThe top credential on the researcher path. Twenty-one courses and a dissertation supervised in writing and examined by two faculty members.
Questions applicants ask
Can time series models predict stock prices?
Daily stock returns show very little linear predictability, and the course demonstrates this. Time series models are more useful for volatility, spreads and risk, which is where most of the course’s applications lie.
Does the course use R or Python?
Python, with statsmodels and arch.
What is financial econometrics?
It is statistics applied to financial data: estimating relations between returns and their drivers, and testing whether those relations are real. It differs from textbook statistics because market data is noisy, fat-tailed and dependent over time.