Empirical finance

Credits: 
2.0
Course Description: 

EMPIRICAL FINANCE SYLLABUS

Level:  Doctoral 
Course Status:  Elective

Full description: 

Background and overall aim of the course.

This course aims to describe the process of price formation of a financial asset starting from the analysis of the empirical regularities (often called as "stylized facts") observed in the time series of several financial assets recorded in markets of primary interest. The course starts with the discussion of efficient market hypothesis, presents stylized facts observed for primarily financial indicators and discuss market microstructure aspects. After a discussion about market data nature and sources the course consider stylized facts and case studies regarding individual and institutional investors, automated trading and the role of exogenous and endogenous news. The course ends with a short introduction to agent based models in finance. 

The course presents how to obtain, analyze and interpret financial data recorded both daily and at high-frequency. Along with the introduction of the concepts underlying the detection and interpretation of stylized facts, the course also presents algorithms that students can actively use to perform empirical analyses.

The learning outcomes of the course.

By successfully completing the course the students will be able to:

- Understand the concept of efficient market hypothesis and its relation with empirical stylized facts;

- Analyze and model several stylized facts of price formation in a financial market;

- Understand basic concepts of market microstructure and extreme events;

- Access relevant data for the analysis and modeling of specific aspects of the process of price formation.

More detailed presentation of course contents.

Week by week breakdown.

Week   Lecture                                                                                    Comments

1          Introduction

            Efficient market hypothesis

2          Volatility in financial markets

            Cross sectional correlation of financial assets

3          Stochastic models of price dynamics

            Data, tools of analysis, data sources and providers               First assignment

 

4          Market microstructure

            Extreme events in financial markets

5          Individual and institutional investors

            Exogenous and endogenous news and price formation

6          Automate trading and flash crashes

            Agent based models of financial markets                              Project assignment

Breakdown by topic:

Detailed description:

1. Introduction

Introduction, stylized facts (empirical regularities) in financial markets. Different time scales and heterogeneity of investors. Access to data.

2. Efficient market hypothesis

Efficient market hypothesis. Strong and weak form of the efficient market hypothesis. Properly anticipated prices present a random dynamics. The concept of absence of arbitrage. Absence of linear correlation in price return. Random processes. Leptokurtic return distribution.

3. Volatility in financial markets

Volatility of a financial asset. Mean reverting nature of volatility. Volatility in high-frequency data. Historical volatility and implied volatility. Long range correlated random processes.

4. Cross sectional correlation of financial assets

Correlation profile of a portfolio of stocks in a financial market. Role of the correlation in the diversification of a portfolio. Portfolios of different financial assets. Portfolio of stock indices.

5. Stochastic models of price dynamics

Geometric random walk. Levy stable random walks. Jump diffusion processes. Multifractal models.

6. Data, tools of analysis, data sources and providers 

Different set of data. High-frequency data. Tick by tick data. Metadata. Data containing the coded identity of investors. Electronic data about news. Data from social networks or from the web. Tools of analysis, data mining and data visualization.

7. Market microstructure

Order book in a double auction market. Limit orders and market orders. Adverse selection. Market impact and optimal execution.

8. Extreme events in financial markets

Extreme event statistics. Different classes of extreme events. Value at Risk and expected shortfall.

9. Individual and institutional investors

Trading profile of individual and institutional investors. Herding in financial markets. Momentum and contrarian investors.

10. Exogenous and endogenous news and price formation

Price formation in the presence of exogenous and endogenous news. Impact of news on price and volatility. Automated sentiment analysis of news. Quarterly announcements.

11. Automate trading and flash crashes

Automated trading in financial market. Time scales. Fraction of automated trading. Different market venues. The flash crash of May 2010. Debate about regulatory needs.

12. Agent based models of financial markets

Agent based models in economics and finance. Agent based models of financial markets. Classes of different ABMs. The challenge of calibration of ABMs.

Suggested reading:

J.Y.Campbell, A.W. Lo, and A.Craig. MacKinlay, The Econometrics of Financial Markets, Princeton University Press 1996.

J.E. Ingersoll, Jr, Theory of Financial Decision Making, Rowman & Littlefield 1987.

R.N. Mantegna and H.E. Stanley, Introduction to Econophysics: Correlation and Complexity in Finance, Cambridge University Press 1999.

Course Requirements

(1)   Assessments of type 1 (30 % of the final grade).  Starting from lecture 3 the students will get home workconsisting of empirical analyses, which they will have to submit electronically. 

(2) Assessment type 2 (30% of the final grade).  The midterm assignment will be after lecture 6, consisting of a test or of a small project on empirical and theoretical aspects presented in the course.

(3) Assessment type 3 (40% of the final grade).  In this final project students will have to perform a research project including some of the following activities: (i) constructing a model, (ii) running simulations, (iii) performing empirical analyses and (iv) critically analyzing results. They will have to prepare a written report and a presentation.

Such further items as the course website (e-learning site), assessment deadlines, office hours, contact details etc.

Contact: Prof. Rosario Nunzio Mantegna (MantegnaR@ceu.hu)

Consultation by arrangement

Further details will be given during the course.