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Quantitative Finance ISyllabus

WS 2010/2011

Principal literature:

1. Campbell, Lo and MacKinlay (CLM): The Econometrics of Financial Markets,Princeton, 1997.2. Tsay R.S.: Analysis of Financial Time Series, Wiley, 2002.4. Barucci E.(BE): Financial Markets Theory,Springer, 2003.6. Hamilton J.C. (HJ): Time Setries Analysis, Princeton, 19947. Evzen Kocenda, Alexander Cerny (2007) Elements of Time Series Econometrics. The

Karolinum Press, UK8. Walter Enders (2004) .Applied Econometric Time Series, Second Edition9. Hommes CH (2006) Heterogeneous agent models in economics and finance. In:

Tesfatsion L, Judd KL (eds) Handbook of computational economics, agent-basedcomputational economics, vol 2. Elsevier Science BV, Amsterdam, pp 11091186

Lecture notes and other material used during lectures will also be available at:http://staff.utia.cas.cz/barunik/

Classification and Organization:

Final Exam: . 40%Midterm Exam: .... 20%Homeworks and Presentations: ... 40%+Extra points for active class participation / extraordinary presentation and

homeworks max 10%

Course Organization:Winter Semester, every Tuesday, 15:30 - 18:20Room 105, IES, Opletalova 26, Prague 1

Lecturers:

Jozef Barunk (JB) barunik@utia.cas.czLuk Vcha (LV) vachal@utia.cas.cz

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Quantitative Finance ISyllabus cont.

5.10. (JB+LV) 1. Introductory Lecture

12.10. (JB) 1. Introduction to Financial Time Series (Assets, Prices, RandomWalk, Moving average Models) (CLM, Chapter 2).

Exercise: Simulation of random walk - application on stock market time series.Homework: Random walk in selected time series..

19.10. (JB) 2. Testing for Random Walk in Asset Prices - Statistical Theory,Unit Root Tests.* (CLM, Chapter 2).

Exercise: Testing for random walk - application on stock market time series.Homework: Testing for random walk in selected time series.* Optional: Overview of Classical Linear Regression

26.10. (LV) 3. Linear Models of Financial Time Series - Moving AverageModels, AR, ARMA, ARIMA. (BC, Chapter 5).

Exercise: Application on real-world data.Homework: Estimation of MA, AR, ARMA, ARIMA

2.11. (JB+LV) 4. Reading (modeling volatility)

Exercise: ReadingHomework: Reading

9.11. (LV) 5. Introduction to Nonlinearities in Financial Data I(Modelling volatility and correlation, GARCH models)

Exercise: Application on the real-world dataHomework: GARCH estimation

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Quantitative Finance ISyllabus cont.

16.11 (JB) 6. Introduction to Nonlinearities in Financial Data II(Threshold models, Parametric Estimation, Neural Networks)

Exercise: Application on the real-world dataHomework: Kernel Regression estimation

23.11. (JB+LV) 7. Reading (high-frequency data)

Exercise: Reading

Homework: Reading

30.11 (JB) 8. High-frequency financial models I(Realized Volatility)

Exercise: basic properties of high-frequency data seriesHomework: Estimation

7.12. (JB) 9. High-frequency financial models II

(Jumps, microstructure noise bias on realized volatility estimation)

Exercise: realized volatility estimationHomework: realized volatility estimation

14.12. (LV) 10. Heterogeneous Agent Models (HAM) I.

Exercise & Homework: Simulation of basic HAM model.

Additional Reading: Brock WA, Hommes CH (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J EconDyn Control 22:12351274

21.12. (LV) 11. Heterogeneous Agent Models (HAM) II.

Exercise: Simulations.Homework: Simulation of basic HAM model.

? (JB, LV) 12. Final