Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Industry, innovation and infrastructure

Academic Year 2023/2024

Learning outcomes

The course concerns trading in financial markets: the main challenges, the economic principles, the statistical models for analyzing the data they generate, and the development of trading strategies, exploring in detail the modeling of high frequency financial data. At the end of the course the student will understand the structure of modern financial markets, the conceptual basics of trading, and will be able to use the economic and econometric models in the high frequency domain.

Course contents

  • Structure of financial markets and trading mechanisms: Call and continuous auction markets. Execution systems: order-driven, quote-driven and hybrid markets. Limit order book. Alternative Trading venues.
  • Microstructure models: Roll model: statistical and structural models. Sequential models: Glosten-Milgrom. Strategic models: Kyle model. Inventory management models. Spread and its components.
  • Algorithmic Trading: Market impact and order flow: empirical facts and modeling. Trading costs. Optimal order execution. Market making. Alpha strategies: mean reversion, statistical arbitrage, pair trading, index fund rebalancing. Statistical limit order book models.
  • High Frequency Econometrics: Efficient Market Hypothesis and empirical tests. Econometrics of High Frequency Data: Realized Variance, Realized Covariance, Realized volatility modelling. Point processes in finance (Hawkes processes and ACD models).


J. Hasbrouck, Empirical Market Microstructure, Oxford University Press 2007.

B. Johnson, Algorithmic Trading & DMA. 4Myeloma Press, 2010

Other relevant materials (data, papers, slides, etc.) will be made available during the course.

Teaching methods

The lectures will be given by using slides and at the blackboard and they will be structured in theoretical parts, examples, and exercises. There might be some laboratory activity using high frequency financial data and writing of code in R, Matlab or Python.

Assessment methods

The final score is given by:

  • Written exam with eight questions (40%)
  • A small (individual or group) project reproducing part of the results of a research paper and presented in a 30 min seminar (60%)

The project part might require the writing of some code in R, Matlab or Python to simulate some model or to analyze some data. Alternatively the project might concern a theoretical paper and in this case the student is expected to have deeply understood the mathematical derivations contained in the paper

Students are expected to know the basic quantitative methods discussed during lectures and to be able to critically discern the limitations of the models employed in the analysis.

In case online exams will be envisaged by the University of Bologna, the structure of the written exam and project part is the same. The exam will be run through Zoom and Exams Online (EOL). Detailed instructions on how to manage and hand in the online exam are available on the course page on the VIRTUALE platform.

The maximum possible score is 30 cum laude, in case all answers of the written part are correct and complete and the project is complete and critically assess the strengths and weaknesses of the research paper.

The grade is graduated as follows:

<18 failed
18-23 sufficient
24-27 good
28-30 very good
30 e lode excellent

Teaching tools

Slides, specialized articles and other didactic material, including data and codes (in R language), will be made available.

Office hours

See the website of Fabrizio Lillo