Econometrics for financial markets - S. Galiani
Course Syllabus
Econometrics for Financial Markets
I semester - Fall 2022
Professor: Stefano Galiani (stefano.galiani@uniroma1.it)
Office: 107 - 1° piano (107 - 1st floor)
Office Hours Tuesday and Wednesday 10-11am subject to email confirmation.
Class Hours (day, time, room):
Tuesday 8:00 to 10:00am , DIDALAB computer lab classroom
Wednesday 8:00 to 10:00am , DIDALAB computer lab classroom
Thursday 8:00 to 10:00am , DIDALAB computer lab classroom
Friday 8:00 to 10:00am , DIDALAB computer lab classroom
Total Module Hours: 96hrs / 12 CFU
Course website: https://web.uniroma1.it/memotef/econometrics-financial-markets-s-galiani
Textbooks
Slides prepared by the instructor, along with Python based Jupyter Notebooks are the course main references.
Additional textbooks covering the topics of the course can be found in:
- Huang, C. and Petukhina, A. (2022). Applied Time Series Analysis and Forecasting with Python, Springer
- Kelliher, C. (2022) Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering, Routledge
- Cherubini, U. et al. (2004) Copula Methods in Finance, Wiley Finance
- Hilpisch, Y. (2016), Listed Volatility and Variance Derivatives: A Python-based Guide, Wiley Finance
- Geron, A. (2017), Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media
Additional Materials
- Notes used during the classes
- Financial time series and various datasets.
- Papers focusing on specific topics covered during the course
- Python/R functions and software
The additional materials will be available in a dedicated Google ClassRoom/Drive folder reserved for the students attending the course only.
Prerequisites
Statistics course covering probability theory and statistical inference.
Financial Mathematics concepts pertinent to present value and basic understanding of contingent claims.
Basic programming in any language, although the first part of the course will provide students with a solid foundations of relevant Python concepts.
Final and grade policy
The exam consists in two parts:
- the first one consists in a series of assignments covering empirical aspects and it is aimed at testing applied skills.
- the second one includes review questions to test theoretical knowledge and critical understanding.
Course Objectives
The course covers the essential tools of financial econometrics and empirical finance by focusing on the theory implementation and parameters calibration of advanced statistical models for financial data analysis and risk management.
Financial assets, prices, returns volatility and other risk measures are considered and critically reviewed.
Data sourcing procedures for macro indicators, stock prices, commodities and fixed income instruments are presented.
It then provides a review of the linear regression and classification models, along with the introduction of classical time series analysis and focuses on their estimation and interpretation.
Extensive treatment of asset volatilities, both realized and implied, are then considered within the context of global market flows and algorithmic trading frameworks.
Multivariate and portfolio-based dependency measures are then reviewed, interpreted and implemented.
Copula functions, both elliptical and Archimedean, are introduced along with their properties. Sampling, calibration and market data fitting procedures are then implemented and critically reviewed.
Students will also be introduced with new tools related to both supervised and unsupervised Machine Learning models such as Decision Trees, Random Forest, Neural Nets, K-Means and DBScan will be applied to case studies pertinent to real estate prices forecasting, credit loans approval and credit card fraud detection datasets.
Specific educational objectives include:
- Ability to interpret results and draw appropriate conclusions.
- Ability to apply theoretical and empirical models to real world problems.
- Python and R programming to perform data analysis.
- Enhance organizational, analytical and communication skills through participation in group project work
Preliminary Weekly Course Calendar
Week 1-3:
- Overview of Python and R data analysis tools and programming concepts.
- Introduction to Numpy, Pandas series, and Scipy modules for the analysis of financial data series and indicators.
Week 4-5:
Modelling volatility: Historical volatility and Implied volatility models. Advanced implied return distribution extraction techniques (Breeden-Litzenberger).
Week 6-7:
- Overview of the classical linear regression models, assumptions and diagnostic tests.
- Univariate time series modelling and forecasting: Moving average processes, Autoregressive processes, The partial autocorrelation function, ARMA processes; Examples of time series modelling in finance.
- Modelling relationships in finance: Stationarity and unit root testing; Cointegration.
- EWMA and GARCH volatility models.
Week 8-10:
- Covariance modelling in finance via Copula Functions (Elliptical and Archimedean). Multivariate risk management measures and interpretation. Probability Integral Transforms, Conditional Sampling, Maximum Likelihood Estimation. Introduction to Algorithmic Trading architectures.
Week 11-12:
- Introduction to Machine Learning applied to financial markets: Linear Regression, Classification via Logistic Regression.
i) Gradient Descents (Batch, Stochastic, Mini Batch), Regularization (Ridge, Lasso and Elastic Net).
ii) Loss Functions: MSE, Log-Loss and Cross Entropy for Multiclass target variables.
- Application in Python via the Scikit-Learn library applied to real estate prices, bank loans data and credit card transaction pools.
- Perceptons, Artificial Neural Networks. Activation Functions, Backpropagation, Parameter Tuning