Empirical Finance - JCM025

• Title: Empirické finance
• Guaranteed by: CERGE (23-CERGE)
• Faculty: Faculty of Social Sciences
• Actual: from 2023
• Semester: summer
• E-Credits: 9
• Examination process: summer s.:
• Hours per week, examination: summer s.:4/2, Ex [HT]
• Capacity: unknown / unknown (20)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: not taught
• Language: Czech
• Teaching methods: full-time
• Teaching methods: full-time
• Note: course can be enrolled in outside the study plan; enabled for web enrollment; priority enrollment if the course is part of the study plan
• Guarantor: Veronika Selezneva, Ph.D.
• Pre-requisite : JCM002, JCM017, JCM021, JCM028

Descriptors

Last update: Mgr. Anna Papariga (21.01.2022)

This course is the second half of the two-semester sequence “Time Series and Financial Econometrics” and relies extensively on time series concepts and tools developed in the first half. The purpose of this course is to introduce classic topics in the empirical asset pricing literature and provide participants with related econometric methods. The students will acquire a set of tools that are useful for modeling financial data and testing beliefs about how financial markets operate.

Literature

Last update: Mgr. Anna Papariga (21.01.2022)

The class is paper-based rather than textbook-based. Some classic sources may include:

 

·         Aït-Sahalia, Yacine, and Lars Peter Hansen, eds. Handbook of financial econometrics: tools and techniques. Elsevier, 2009.

·         Cambell, J.Y.,A.W. Lo, and A.C. MacKinlay, 1997, “The Econometrics of Financial Markets”, Princeton University Press..

·         Cochrane, J.H., 2001, “Asset Pricing”, Princeton University Press.

·         Hamilton, J.D., 1994, “Time Series Analysis”, Princeton University Press.

 

A collection of readings will complete the course material (TBA).

Requirements to the exam

Last update: Mgr. Anna Papariga (21.01.2022)

Requirements and grading

The course grade will depend on problem sets (20%), presentations (30%) and a final exam, which includes an individual project (50%). The students are allowed to work on the homework problems in groups up to 3 people. One assignment per group should be submitted. Some home assignments will include computer exercises with financial data. Any programming language can be used, but statistical packages are not allowed.

Syllabus

Last update: Mgr. Anna Papariga (21.01.2022)

Course outline (tentative and subject to change)

 

·         Building and testing asset pricing models

·         Arbitrage pricing theory

·         Mean variance efficient frontier

·         CAMP/Single factor models

·         Multi factor models

·         Cross-sectional regressions, Fama-MacBeth

·         Conditional vs. unconditional asset pricing models

·         Term structure models

·         Affine models

·         Factor analysis and principal component methods

·         State space models and filtering

·         Shadow rates, ZLB

·         Estimation methods

·         Simulated method of moments

·         Indirect inference and efficient method of moments

·         Market microstructure

·         Price discovery, market efficiency, liquidity

·         Algorithmic trading, high-frequency trading, arbitrage, high-frequency data

·         Topics (if time permits)

·         Green finance

·         Intro to corporate finance

·         Big data in finance, basic machine learning techniques

 

 

Entry requirements

Last update: Mgr. Anna Papariga (21.01.2022)

Prerequisites

Sufficient programming skills. The students should be prepared to process large datasets.