390043 UK PhD-AW and VGSF: Advanced Time Series And Financial Econometrics (2015W)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 14.09.2015 09:00 to We 30.09.2015 14:00
- Deregistration possible until We 14.10.2015 23:59
Details
max. 24 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Thursday
01.10.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
08.10.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
22.10.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
29.10.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
05.11.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
12.11.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
19.11.
15:00 - 18:15
Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
26.11.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
03.12.
15:00 - 18:15
Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
10.12.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
17.12.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
07.01.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
14.01.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
21.01.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday
28.01.
15:00 - 18:15
Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
1) Take-home exam (45%)
Performing research and writing research report on an empirical problem using data and programming, first week of February 2016
2) Exam, 28.01. 2016 (30%)
3) Assessments in R: Each student has to present one R assessment (maybe group work) (25%)To pass the course, a minimum of 50% is required.
Performing research and writing research report on an empirical problem using data and programming, first week of February 2016
2) Exam, 28.01. 2016 (30%)
3) Assessments in R: Each student has to present one R assessment (maybe group work) (25%)To pass the course, a minimum of 50% is required.
Minimum requirements and assessment criteria
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
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
Examination topics
Lectures, discussion, empirical exercises, programming in R
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.
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 Li, C. (2006): "Forecasting the term structure of government bond yields", Journal of Econometrics, 130, 337-364.
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.
Tsay, R. S. (2010): ''Analysis of Financial Time Series: Financial Econometrics'', Wiley, 3rd edition.
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.
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.
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 Li, C. (2006): "Forecasting the term structure of government bond yields", Journal of Econometrics, 130, 337-364.
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.
Tsay, R. S. (2010): ''Analysis of Financial Time Series: Financial Econometrics'', Wiley, 3rd edition.
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
1.1. Stochastic Processes
1.2. Basic Concepts of Time Series Analysis
1.3. Basic Concepts of Asymptotic Analysis
1.4. Some Matrix Properties2. Vector Autoregressive Processes
2.1. Stable Vector Autoregressive Processes
2.2. Structural Analysis
2.3. Estimation and Diagnostics
2.4. Examples3. Cointegrated Processes
3.1. Integrated Processes
3.2. Cointegration
3.3. Cointegrated VAR Models
3.4. Statistical Inference
3.5. Examples4. Regime Switching and State Space Models
4.1. Threshold Models
4.2. Markov Regime Switching Models
4.3. State-Space Models5. GARCH and Stochastic Volatility Models
5.1. Univariate GARCH Models
5.2. Multivariate Volatility Models
5.3. Stochastic Volatility Models6. High-Frequency Based Volatility Estimation
6.1. Realized Volatility
6.2. Constant Volatility Under Noise
6.3. Noise-Adjusted Estimators
6.4. Extensions7. Models for High-Frequency Financial Data
7.1. Financial Transaction Data
7.2. Dynamic Point Process Models
7.3. Models of the Trading Process