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Regular version of the site
Bachelor 2021/2022

Quantitative Finance

Area of studies: Economics
When: 4 year, 1-4 module
Mode of studies: distance learning
Online hours: 24
Open to: students of one campus
Instructors: Колесников Иван Андреевич, Спиридонов Игорь Александрович, Daniil Esaulov, Anatoly Evgenievich Patrik
Language: English
ECTS credits: 10
Contact hours: 112

Course Syllabus

Abstract

The course provides coverage of important topics in modern Quantitative Finance and Risk Management at the advanced undergraduate level. Itis intended for the 4th-year undergraduate students of the International College of Economics and Finance, High School of Economics, Moscow. Particular attention is given to the topics such as the Efficient Market Hypothesis, financial markets microstructure and types of arbitrage, general principles of modelling the price dynamics of financial assets, market risk and other types of financial risks, Value-at-Risk (VaR) approach and applications, modelling of extreme market events, VaR analysis for financial derivatives using the Kolmogorov equations framework, modelling of periodic and quasiperiodic trends in time series in connection with technical analysis, and the foundations of high frequency arbitrage trading. The topics covered in this course will enable the students to develop the theoretical knowledge and practical skills required for successful working with multiple types of risks in modern financial markets, both Russian and international. The course is taught in English. Prerequisites for the course areElements of Econometrics and Microeconomics. Good command of methods of calculus, general probability theory and mathematical statistics are also required for the course.
Learning Objectives

Learning Objectives

  • To give students insights in the functioning of financial markets, understanding of measuring and forecasting financial risks
  • To give students instruments required in order to analyze issues in asset pricing and market finance. After the course students should be familiar with recent empirical findings based on financial econometric models, have a good command of basic econometric techniques and understand practical issues in the forecasting of key financial market variables
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to compare VaR and ES forecasts; Be able to show that constructed VaR and ES forecasts are optimal
  • Be able to determine if the forecast is optimal; Be able to compare the quality of two and more forecasts of the same variable; Be able to combine several forecasts in order to obtain more optimal one
  • Be able to relate he concept of `efficient markets to the ARMA-GARCH models; Be able to understand basic data scooping mistakes the researcher might do when he tries to show the significance of particular variables in his econometric model
  • Learn extensions of basic ARCH/GARCH models and understand how they are connected to the financial data stylized facts
  • Learn main stylized facts of financial data and asset returns, in particular
  • Learn the concept of cointegration and Granger Causality; Be able to show that VAR model is weakly stationary; Be able to show that one variable Granger causes another
  • Learn the concept of diurnality; Be able to construct and estimate empirical models for intraday data
  • Understand the concept of stationarity and its connection to different ARMA representations; Be able to prove that time series is weakly stationary using definition and different criteria; Be able to represent stationary AR model as MA with infinitely many parameters; Be able to estimate ARMA models using software
  • Understand the concept of volatility; Understand what (G)ARCH models are and their basic properties; Be able to estimate (G)ARCH models
  • Understand what time series is and what is its difference from other data forms Be able to find time series moments, autocorrelation functions and other characteristics Be able to show that the time series is white noise Learn different forms of ARMA models
Course Contents

Course Contents

  • Basic time series concepts
  • Testing for stationarity
  • Empirical features of financial data
  • Modeling asset return volatility: introduction
  • Vector Autoregression
  • Modeling asset return volatility: extensions
  • Evaluating forecasts of risks and returns
  • The efficient market hypothesis and market predictability
  • Risk management and Value-at-Risk: models
  • Risk management and Value-at-Risk: backtesting
  • Modeling high frequency financial data
Assessment Elements

Assessment Elements

  • non-blocking UoL exam
    UoL exam is not included in overall grade for 4th year students.
  • non-blocking Midterm test
  • non-blocking December exam
    Online format.
  • non-blocking Final exam
  • non-blocking Home Assignments
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    0.2 * Midterm test + 0.7 * December exam + 0.1 * Home Assignments
  • 2021/2022 4th module
    0.3 * 2021/2022 2nd module + 0.1 * Home Assignments + 0.6 * Final exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Analysis of financial time series, Tsay, R. S., 2005
  • Applied econometric time series, Enders, W., 2004
  • Brooks,Chris. (2019). Introductory Econometrics for Finance. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9781108422536
  • Christoffersen, P. F. (2003). Elements of Financial Risk Management. Amsterdam: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=104701
  • Elements of financial risk management, Christoffersen, P. F., 2012
  • Introductory econometrics for finance, Brooks, C., 2007

Recommended Additional Bibliography

  • Paul Wilmott. (2013). Paul Wilmott on Quantitative Finance. [N.p.]: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=185503