Количественные методы исследований в финансах


Evgenii V. Gilenko, Associate Professor, Ph.D.

Graduate School of Management, St. Petersburg State University



3 ECTS, 30 hours of classes (22 hours of lectures and 8 hours of seminatrs)



Knowledge of basics of probability theory and mathematical statistics, e.g., passing the course “Introduction to Quantitative Methods”


Goals and objectives: 

The goal of this course, which is intended for first-year Master students in Corporate Finance, is to provide them with theoretical and practical foundations for conducting empirical research in economics and finance. The focus is on econometric analysis of data; the topics covered range from the OLS estimation and inference to basic panel data models and binary dependent variable models (logit and probit). Computer exercises using statistical software package Stata are an integral part of the course, which ensures that students get hands-on experience of analyzing real world data.

After the course, students should have a firm grasp of the types of research design that can lead to convincing analysis in economics and finance. They should also learn basic commands for econometric analysis as well as programming options in Stata, one of the most popular general-purpose statistical packages.

The finance component of the course manifests itself in different applications of general statistical and econometric methods to typical corporate finance problems, such as estimation of the market model (CAPM) and analysis of banks’ decisions on granting/denying loans. Such applications, either in the form of classroom examples or computer lab exercises, ensure that students get prepared to conducting own empirical projects (including own Masters’ theses) in the field of corporate finance.


Course Content:

Topic 1. Basic econometric analysis. Pair-wise regression

  • The methodology of econometric research
  • The types of data in econometric analysis
  • Application of econometrics in finance
  • Pair-wise regression model setup
  • Specification tests and goodness-of-fit
  • Forecasting with a pair-wise regression


Topic 2. Multiple regression: modeling and inference         

  • Multiple regression model setup.
  • Omitted and redundant variables.
  • Multicollinearity.
  • Specification tests.


Topic 3. Multiple regression: specification problems

  • Heteroskedasticity of disturbances.
  • Autocorrelation of disturbances.
  • Dummy variables.


Topic 4. Binary response models

  • Linear probability model
  • Logit- and probit-models
  • Classification and goodness-of-fit


Topic 5. Time-series analysis in finance          

  • Non-stationary time-series.
  • Spurious regression.
  • Integrated time-series. Cointegration.
  • ARCH/GARCH processes

Topic 7. Panel regression models in finance  

  • Introduction to panel data analysis.
  • Fixed and random effects models.
  • Specification tests.



Course reading:


  • Stock, James H. and Mark W. Watson (2010) Introduction to Econometrics. Third Edition. Pearson Education, Inc.
  • Wooldridge, Jeffrey M. (2009) Introductory Econometrics: A Modern Approach. Fourth Edition. South-Western
  • Kohler, Ulrich and Kreuter, Frauke (2005) Data Analysis Using Stata, College Station, Texas: Stata Press
  • Gavrilova T., Zhukova S. Knowledge Engineering. Learning guide, GSOM, 2013. 


Optional Reading

  1. Nast  J. Idea Mapping: How to Access Your Hidden Brain Power, Learn Faster, Remember More, and Achieve Success in Business. Wiley, 2006.
  1. Schuster P.M. Concept Mapping: A Critical-Thinking Approach to Care Planning, F. A. Davis Company, 2007.


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