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

12.00 ECTS (3.00 SWS), SPL 39 - Doktoratsstudium Wirtschaftswissenschaften
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 24 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

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

Information

Aims, contents and method of the course

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

Assessment and permitted materials

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%)

Minimum requirements and assessment criteria

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%)

Examination topics

Reading list

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.

Association in the course directory

Last modified: Mo 07.09.2020 15:46