Quantitative Finance II - JEM061

• Title: Quantitative Finance II
• Guaranteed by: Institute of Economic Studies (23-IES)
• Faculty: Faculty of Social Sciences
• Actual: from 2006 to 2006
• Semester: summer
• E-Credits: 6
• Examination process: summer s.:
• Hours per week, examination: summer s.:2/2, Ex [HT]
• Capacity: unknown / unknown (unknown)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: Czech
• Teaching methods: full-time
• Teaching methods: full-time
• Additional information: https://vosvrdaweb.utia.cas.cz/derivaty/der_d.htm
• Note: course can be enrolled in outside the study plan; enabled for web enrollment
• Guarantor: prof. Ing. Miloslav Vošvrda, CSc.
• Class: Courses for incoming students

Literature

Last update: PhDr. Petr Bednařík, Ph.D. (06.06.2020)

1.    Beran, J. (1994): Statistics for Long - Memory Processes. New York, Chapman and Hall. 

2.    Percival, D. B., Walden, A. T. (2000), Wavelet Methods for Time series Analysis. Cambridge University Press. 

3.    Ramsey, J. B. (2002), Wavelets in economics and finance: Past and future. Studies in Nonlinear Dynamics & Econometrics, 3, 1. 

4.    Samorodinsky, G. (2006) Long Range Dependence, In Foundations and Trends in Stochastic Systems, Vol. 1, No. 3 163–257.

5.    Aguiar-Conraria, L., Martins, M. M., & Soares, M. J. (2012). The yield curve and the macro- economy across time and frequencies. Journal of Economic Dynamics and Control.

6.    Gencay, R., & Signori, D. (2015). Multi-scale tests for serial correlation. Journal of Econometrics, 184(1), 62-80.

7.    + Lecture notes 

 

Syllabus

Last update: Mgr. Michaela Čuprová (07.06.2020)

Main topics:
1. Introduction (non-linear processes, long memory, self-similarity)
2. Introduction to frequency domain (2 Lectures) Fourier transform, Parseval's theorem
3. Advanced spectral techniques: Estimation of the Spectrum, Periodogram, Correlogram, Coherency spectrum
4. Filters
5. Long memory (2 Lectures)
6. Wavelets (2 Lectures)
7. Recent applications of spectral methods in finance