Universität Wien
Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.

390043 UK PhD: Advanced Time Series And Financial Econometrics (2017W)

12.00 ECTS (3.00 SWS), SPL 39 - Doktoratsstudium Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 24 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Dienstag 03.10. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 10.10. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 17.10. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 24.10. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 31.10. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 07.11. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 14.11. 09:45 - 13:00 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 21.11. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 28.11. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 05.12. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 12.12. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 09.01. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 16.01. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 23.01. 09:45 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 30.01. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Target audience: PhD in Finance/Economics/Business; Advanced Master students all areas; PhD in Statistics(with interest in financial applications; here Chap 4-6 most relevant)

Aim of the course:
i. Providing a sound background in multiple time series analysis, state-of-the-art volatility modeling and econometric models for high-frequency data
ii. Implementing econometric theory using real financial data
iii. Practicing programing in R
iv. Evaluating and validating empirical research

Course Outline:
1. Basic Concepts
1.1. Stochastic Processes
1.2. Basic Concepts of Time Series Analysis
1.3. Basic Concepts of Asymptotic Analysis
1.4. (Pseudo) Maximum Likelihood Estimation
1.5. Some Matrix Properties

2. Vector Autoregressive Processes
2.1. Stable Vector Autoregressive Processes
2.2. Structural Analysis
2.3. Estimation and Diagnostics
2.4. Examples

3. Cointegrated Processes
3.1. Integrated Processes
3.2. Cointegration
3.3. Cointegrated VAR Models
3.4. Statistical Inference
3.5. Examples

4. GARCH and Stochastic Volatility Models
4.1. Univariate GARCH Models
4.2. Multivariate Volatility Models
4.3. Stochastic Volatility Models

5. High-Frequency Data & Realized Variances
5.1. Introduction to High-Frequency Data
5.2. Handling High-Frequency Data
5.3. Properties of High-Frequency Data
5.4. The Realized Variance
5.5. Modelling Realized Variances

6. High-Frequency Based (Co-)Variance Estimation
6.1. Constant Volatility Under Noise
6.2. Noise-Adjusted Estimators
6.3. Jump-Robust Estimation
6.4. Realized Covariance
6.5. Multivariate Realized Kernels
6.6. Applications to Portfolio Allocation

Art der Leistungskontrolle und erlaubte Hilfsmittel

1) Take-home exam (45%)
Performing research and writing research report on an empirical problem using data and programming, after written exam
2) Exam, Date TBA (30%)
3) Assessments in R: Each student has to present one R assessment (maybe group work) (25%)

Mindestanforderungen und Beurteilungsmaßstab

1) Take-home exam (45%)
Performing research and writing research report on an empirical problem using data and programming, after written exam
2) Exam, Date TBA (30%)
3) Assessments in R: Each student has to present one R assessment (maybe group work) (25%)

Prüfungsstoff

Literatur

Ait-Sahalia, Y., Mykland, P. A., and Zhang, L. (2005): "How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise", Review of Financial Studies, 18, 351-416.

Andersen, T.G., Dobrev, D. and Schaumburg, E. (2012): "Jump-robust volatility estimation using nearest neighbor truncation", Journal of Econometrics, 169, 75-93.
Andersen, T.G., Davis, R.A., Kreiß, J.-P., and Mikosch, T. (2009) :“Handbook of Financial Time Series”, Springer

Barndorff-Nielsen, O. E., Hansen, P.R., Lunde, A., and Shephard, N. (2008): "Designing Realized Kernels to Measure the Ex Post Variation of Equity Prices in the Presence of Noise", Econometrica, 76, 1481-1536.

Barndorff-Nielsen, O. E., Hansen, P.R., Lunde, A., and Shephard, N. (2011): "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading", Journal of Econometrics, 162, 149-169.

Barndorff-Nielsen, and Shephard, N. (2004): "Econometric Analysis of Realized Covariation: High Frequency Based Covariane, Regression, and Correlation in Financial Economics", Econometrica, 72, 885-925.

Bauwens, L., Hafner, C., and Laurent S. (2012): “Handbook of Volatility Models and their Applications”, Wiley.

Corsi, F. (2009): "A Simple Approximate Long-Memory Model of Realized Volatility", Journal of Financial Econometrics, 7, 174-196

Diebold, F. X., and Yilmaz, K. (2014): " On the network topology of variance decompositions: Measuring the connectedness of financial firms", Journal of Econometris, 182, 119-134.

Engle, R. F., and Kelly, B. (2012): "Dynamic Equicorrelation", Journal of Business & Economic Statistics, 30, 212-228.
Gouriéroux, C. and Monfort, A. (1995): Statistics and Econometric Models, Cambridge
University Press, Vol. 1

Hayashi, F. (2000): Econometrics, Princeton University Press.

Hasbrouck, J. (2007):“Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading”, Oxford University Press.

Hansen, P. R., Huang, Z., and Shek, H.S. (2012): "Realized GARCH: A Joint Model for Returns and Realized Measures of Volatility", Journal of Applied Econometrics, 27, 877-906.

Hansen, P. R., and Lunde, A. (2006): "Realized Variance and Market Microstructure Noise", Journal of Business & Economics Statistics, 24, 127-161.

Hautsch, N. (2012): “Econometrics of Financial High-Frequency Data”, Springer.

Hautsch, N., Kyj, L., and Oomen, R.C.A. (2012): "A blocking and regularization approach to high dimensional realized covariance estimation", Journal of Applied Econometrics, forthcoming

Hautsch, N., Kyj, L., and Malec, P. (2013): "Do High-Frequency Data Improve High-Dimensional Portfolio Allocation?", Journal of Applied Econometrics, forthcoming

Juselius, K. (2006): “The Cointegrated VAR Model”, Oxford University Press.

Lütkepohl, H. (2006): “New Introduction to Multiple Time Series Analysis”, Springer.

Noureldin, D., Shephard, N., and Sheppard, K. (2012): "Multivariate High-Frequency-Based Volatility (HEAVY) Models", Journal of Applied Econometrics, 27, 907-933.

Pesaran, H.H., and Shin, Y., (1998): "Generalized impulse response analysis in linear multivariate models", Economics Letters, 58, 17-29.

Shephard, N., and Sheppard, K. (2010): "Realising the Future: Forecasting with High-Frequency-Based Volatility (HEAVY) Models", Journal of Applied Econometrics, 25, 197-231.

Taylor, S. J. (2005): ''Asset Price Dynamics, Volatility, and Prediction'', Princeton University Press.

Zhang, L., Mykland, P. A., and Ait-Sahalia, Y. (2005): "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data", Journal of the American Statistical Association, 100, 1394-1411.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 07.09.2020 15:46