Notizie
ECONOMETRICS FOR FINANCIAL MARKETS
Course Syllabus
Econometrics for Financial Markets
I semester - Fall 2024
Professor: Stefano Galiani (stefano.galiani@uniroma1.it)
Office: 107 - 1° piano (107 - 1st floor)
Office Hours Monday 8:00-9:00am 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://www.google.com/url?q=https://corsidilaurea.uniroma1.it/it/users/...
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
MODELS FOR RISK AND FORECASTING
Course Syllabus
Models for risk and forecasting
II semester - Spring 2024
Professor: Stefano Galiani (stefano.galiani@uniroma1.it)
Office: 107 - 1° piano (107 - 1st floor)
Office Hours: in person or Google Meet 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
Total Module Hours: 72hrs / 9 CFU
Exam Sessions: 4 June 2024, 4 July 2024, 19 September 2024, 24 October 2024 (to be booked on INFOSTUD platform)
Textbooks
Slides prepared by the instructor, along with Python based Jupyter Notebooks are the course main references.
Textbooks covering the topics of the course can be found in:
- Coqueret, G. and Guida, T. (2020) Machine Learning for Factor Investing, Chapman and Hall/CRC Financial Mathematics Series,
- Geron, A. (2017), Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media
- Agresti, A. and Kateri, M., Foundations of Statistics for Data Scientists: With R and Python (2021), Chapman & Hall/CRC Texts in Statistical Science
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
This course on quantitative risk management combine certain elements of Statistical Methods and Machine Learning and explain how to build systems that learn and adapt using real-world applications to detect and manage risks within a financial setting.
The first part of the course gathers preparatory material related to data analysis and preparation via Pandas Series and DataFrames.
Data cleaning, checking and grouping tools are presented.
The second part of the course introduces students to the economic foundations (theoretical and empirical) of factors construction, followed by an in-depth discussion of the application of these techniques to portfolio risk decisions, including the design of more robust factor models and the implementation of more efficient risk management models.
The third part of the course is dedicated to predictive algorithms in supervised learning used to forecast financial quantities such asset returns, volatilities and performance ratios. Among the models presented, the students will be introduced to:
- Penalized regressions: Ridge, LASSO and Elastic-Net
- Tree methods: CART algorithm for both regression and classification tasks
- Random Forests: bagging, pasting, out-of-bag evaluation, ADABOOST, Gradient Boosting
- Support Vector Machines: soft vs hard marging, non-linear SVM, kernel tricks
- Neural Networks: architecture, back-propagation, regularization
Every model will be critically reviewed and implemented in Python (Scikit-Learn, TensorFlow and PyTorch libraries).
Last, the course reviews the critical steps of model tuning and hyperparameters optimization and mentions the critical points that are often encountered at this stage.
Specific educational objectives include:
- Ability to interpret results and draw appropriate conclusions.
- Ability to apply theoretical and empirical models to real world problems.
- Python programming and data analysis.
- Enhance organizational, analytical and communication skills through participation in group project work
Preliminary Weekly Course Calendar
Week 1-3:
- Overview of Python data analysis tools and programming concepts.
- Introduction to Numpy, Pandas, and Scipy modules for the analysis of financial data series and indicators.
- Grouping, merging, mapping and location applied to financial datasets.
Week 4-5:
- Value at Risk and Expected Shortfall definition and portfolio implementation (parametric, non-parametric, simulation).
- Factor Investing and Smart Beta Strategies: economic foundations, empirical estimation of Factor Loadings with Python implementation.
Week 6-8:
- Introduction to Machine Learning applied to financial risks:
- Linear Regression, Classification via Logistic Regression.
i) Gradient Descents (Batch, Stochastic, Mini Batch),
ii) Loss Functions: MSE, Log-Loss and Cross Entropy for Multiclass target variables.
iii) Regularization (Ridge, Lasso and Elastic Net)
- Decision Trees and CART training algorithm
- Ensemble Learning and Random Forests (bagging, pasting, out-of-bag evaluation and boosting)
- SVM: primal vs dual problem, soft margin via slack variables, kernel tricks, Mercer's Theorem
- from Perceptons to Artificial Neural Networks. Activation Functions, Backpropagation, Parameter Tuning
Week 9
Model Calibration and hyperparameters tuning.
RISK MANAGEMENT AND CAPITAL REQUIREMENTS
Course Syllabus
Risk Management and Capital Requirements
II semester - Spring 2025
Professor(s):
Stefano Galiani (stefano.galiani@uniroma1.it)
Claudia Ceci (claudia.ceci@uniroma1.it)
Office: 107 - 1° piano – Ala 1 (107 - 1st floor) and 111 - 1° piano – Ala 1 (111 - 1st floor)
Office Hours: to be coordinated by email
Class Hours (day, time, room):
Tuesday 10am-12pm (Didalab), Wednesday 10am-12pm (Didalab), Thursday 10am-12pm (ECODIR Lab, 6th fl)
Total Module Hours: 72hrs / 9 CFU
Exam Sessions: 5 June 2024, 5 July 2024, 18 September 2024, 24 October 2024 (to be booked on INFOSTUD platform)
Recommended Textbooks
Slides prepared by the instructors, along with Python based Jupyter Notebooks, are the course main references.
Textbooks covering the topics of the course can be found in:
- McNeil, A., Frey, R. and Embrechts, P. (2015) Quantitative Risk Management: Concepts, Techniques and Tools, Wiley
- O’Kane, D. (2008), Modelling Single-name and Multi-name Credit Derivatives, Wiley
- Rösch, R., Scheule, H. (2022), Deep Credit Risk Machine Learning in Python
- Agresti, A. and Kateri, M., Foundations of Statistics for Data Scientists: With R and Python (2021), Chapman & Hall/CRC Texts in Statistical Science
Additional Materials
- Notes used during the classes
- Financial datasets provided by the instructirs
- Papers focusing on specific topics covered during the course
- Python 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
Credit risk is a topic of fundamental importance in modern banking systems. Quantitative credit risk methodologies play a fundamental role in the risk-management units of major investment banks. The recent crisis has led to numerous regulatory reforms requiring banks to comply with capital requirements. This can only be achieved via the implementation of a sophisticated and mathematically sound credit risk framework. This course deals with quantitative modeling and measuring of credit risk. You will learn how to price financial instruments, whose payoff is contingent to the realization of a credit event. You will also learn how to measure credit losses, manage portfolios of credit sensitive securities, and calibrate financial models to using market data.
Furthermore, in the last part of the course, a complete suite of statistical techniques, including models for probabilities of default using GLM Probit and Logit models, are then applied to a real loans dataset allowing students to learn the pre-processing, analysis and critical statistical understanding of the model outputs.
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 data analysis.
- Enhance organizational, analytical and communication skills through participation in group project work
Preliminary Weekly Course Calendar
Week 1-4 (Prof. Claudia Ceci):
-Introduction: OTC Markets, Credit Risk and Measuring Credit Quality;
-Structural model of default: Merton Model (Geometric Brownian motion);
-Intensity based (or hazard rate) models: Conditional expectation and conditional survival probability.
-Pricing of (defaultable) Bonds: DZCB with and without recovery, Defaultable Coupon Bonds. Credit spread.
-Credit Default Swap pricing.
Week 5-6 (Prof. Stefano Galiani):
- Hazard rate term structure bootstrapping from market prices
- Upfront vs Running CDS no-arbitrage pricing
- Senior vs Subordinated CDS relative value pricing
- Calibration of portfolio CDS to credit default swap indices (CDX and iTraxx) market prices.
- Brief introduction to multi-name CDS portfolio risk measures (Tail dependence)
Week 7-9 (Prof. Stefano Galiani):
- US retail loan portfolio exploration, cleaning and preparation via Pandas.
- Validation Metrics:
- Brier's Score
- Calibration Curve (aka Reliability Diagrams)
- Binomial Test
- Jeffrey's Prior
- Generalized Linear Models applied to credit markets:
- Theoretical Foundation
- Link Function
- GLM/Logit Model
- GLM/Probit Model
- Comparison GLM/Logit vs GLM/Probit Models
- Multivariate Interactions
- statsmodels vs scikit-learn Python implementation
- Default Probability Forecasting: the Comprehensive Model
- TTC (thru the cycle) vs PIT (point in time) Default Probability Estimates
- Asymptotic Single Risk Factor (Vasicek) model within the Basel Capital Charge Framework
- EBA / FRB Stress Scenario Approaches
- Machine Learning Techniques for Default Probability Estimates (Decision Tree and Random Forests)
Orari di ricevimento
Remote or in Presence
By appointment via email.
Curriculum
--Professional Experience--
University of Rome, Sapienza [Sep 2022 - ]
City: Rome
Country: Italy
Title: Adjunct Professor / Research Fellow
Adjunct Professor for the following courses within the Faculty of Economics, Finance and Insurance CdL:
Econometrics for Financial markets (12CFU), Models for Risk and Forecasting (9CFU), Risk Management and Capital Requirements (9CFU) and Laboratory Python (3CFU).
Research Fellow in Generative AI Models for Dynamic Reasoning under Partial Knowledge to make Interpretable Decisions
HSBC Bank USA [ Jun 2018 – Jan 2021 ]
City: New York
Country: United States
Title: Senior Vice President, Global Markets, Credit Trading
Head of Credit Derivatives Trading for North America Investment Grade and High Yield Indices.
Machine Learning driven Market making, Risk Management and Pricing systems for credit derivatives
Paloma Partners Investment Management [ Mar 2017 – May 2018 ] / Bluecrest Capital Management [ Mar 2012 – Mar 2017 ]
City: New York, NY and Greenwich, CT
Country: United States
Title: Hedge Fund Portfolio Manager
Responsible machine-learning based investment strategy of credit and equity derivatives volatility arbitrage using indices, options and ETF
Deutsche Bank [ Jul 2009 – Mar 2012 ]
City: New York
Country: United States
Title: Director, Credit Derivatives Trader
Head of US and European Credit Correlation Trading. Managed a team between London and New York responsible for market making, risk management and system design of structured credit products.
Morgan Stanley [ May 2008 – Jul 2009 ]
City: New York
Country: United States
Title: Executive Director, Structured Credit Trader
Managed the exotic and complex risk credit book.Responsible for structuring, pricing and trading bespoke credit investment solutions for the Bank's institutional clients.
Merrill Lynch [ Sep 2003 – May 2008 ]
City: New York
Country: United States
Title: Director, Structured Credit Trader
Responsible for market making, risk management and system design of structured credit and ABS index products in North America