Advanced Econometrics and Deep Learning for Financial Time Series

This 3 ECTS self-paced course offers a comprehensive overview of modern financial modeling, combining the rigor of classical econometrics (ARMA and GARCH models) with the flexibility of deep neural networks (LSTM). Participants will learn to process financial data at high speed, automate investment decisions, and manage portfolios using both traditional and alternative data.

What You Will Learn

Learning objectives:

• Master the transition from classical statistical models (ARIMA, GARCH) to advanced deep learning architectures (LSTM, Autoencoders) for financial forecasting and classification.
• Analyze market behavior through the lens of volatility clustering and heavy-tailed distributions.
• Implement end-to-end algorithmic trading pipelines and automated portfolio management systems in Python.
• Evaluate model uncertainty and regime detection using sequential learning techniques.

Learning outcome

1. Build and tune ARIMA and GARCH models to estimate financial risk and volatility.
2. Design and implement LSTM-based architectures for multi-step price forecasting.
3. Use sequential learning to identify shifts in market regimes and volatility clusters.
4. Develop automated trading strategies and optimize portfolios using Deep Learning.
5. Integrate alternative data (Sentiment, News) into financial decision-making processes.

Agenda

The course will be self-paced 

Week 1: Financial Markets Zoo and Paper Trading

• Introduction to financialmarkets: purpose, asset classes, andmarket jargon.
• Overview of securities: stocks, bonds, options, futures andforwardscontracts.
• Understandingpayoffsand price relationships.
• Portfolio basics andmarketindices (S&P 500, ETFs). (Video lecture on Market Indices Zoo)
• Arbitrageconceptsand transaction costs.
• Basics of TechnicalandFundamental Analysis. (Video lecture)

Lab:

• Setting up a paper tradingaccount.
• Placingorders, monitoringpositions, and understanding commissions.
• Creatingandevaluating simple trade ideas.

Week 2: Time Series Analysis and Forecasting

• Principles of time series forecasting.
• Introduction to stationaryand non-stationary processes.
• Autocorrelation, seasonality, andtrends in financial data.
• Econometric models: AR, MA, ARIMA.
• Modelingvolatilitywith ARCH and GARCH. (Video lecture on Volatility)

Lab:

• Pricedirectionalityand forecasting
• Translating model outputs into simple tradeideas.
• Usingforecastsand volatility estimates to guide paper trading decisions.

Week 3: Neural Networks for Time Series and Strategy Design

• Introduction to neural networks for forecastingfinancial data. (Videolecture)
• LSTM networks for sequential data.
• Autoencodersfor featureextraction and anomaly detection.
• Trainingandevaluating neural networks on time series.
• Comparing neural network forecastswithtraditional time series models.
• Practicalconsiderations: cross-validation for sequence data, overfitting, data scaling, andinterpretability. (Video-lecture)

Lab:

• Evaluatethemodel’s output in a trading environment.
• Autoencoders for frauddetection

Week 4: Advanced Applications – Portfolio Theory, and Algorithmic Trading

• Introduction to portfoliotheory: risk, return, anddiversification.
• Constructingandevaluating portfolios using risk-adjusted metrics.
• Alternative data, sentiment analysis (Take-home readings, videolecture)
• Basics of algorithmictradingand automated execution.
• Combiningforecasts, volatilityinto actionable trade signals
• Evaluatingstrategy performance andrisk metrics
• Practicalconsiderations: overfitting, transaction costs, andmarket impact

Lab: Portfolio rebalancing strategies, backtesting Algorithmic trading with alternative data

Required readings or materials

• Argimiro Arratia (2014) Computational Finance: An Introductory Course with R (Atlantis Press-Springer). Available online for UPC members.
• E. Zivot & J. Wang (2006) Modelling Financial Time Series with S-PLUS. https://faculty.washington.edu/ezivot/econ589/manual.pdf
• James et al., An Introduction to Statistical Learning. https://www.statlearning.com
• Deep Learning for Finance (Selected papers on LSTM for sequence modeling, Autoencoders).
• Scikit-learn user guide: sections on supervised learning, unsupervised learning and model evaluation

Technical setup or resources needes

• Google Colab (Python 3.x).
• Libraries: numpy, pandas, matplotlib, statsmodels, arch, tensorflow, scikit-learn
• Computer with a minimum of 8 GB of RAM. 16GB recommended.
• Stable internet connectionwith the ability to video conference