IRTG 1792/2: High Dimensional Non Stationary Time Series


Quantitative economics often faces the challenge of modelling high dimensional, unstructured data and nonstationary time series. The standard tool sets that are based on a low parameter dimension and a growing number of observations are not applicable and one needs to use adaptive techniques and local stationary models to match data phenomena. The IRTG investigates how high complexity and dimensionality can be downscaled to lower dimensional structures. The dynamic of which can be understood via dimension reduced statistical modelling. The key goals are the development of new statistical and econometric theory to create a broader field of analysis supported by e.g. time varying machine learning or network techniques. The computational methods will be provided on a free accessible platform that allows for transparent and reproducible scientific research. Areas of application include neuroscience, high frequency finance, time varying clustering of unstructured data, dynamic topic modelling, sentiment reactions and herding effects. For realistic adoption of these new tools one needs to eliminate, or at least relax, Gaussianity assumptions via various types of regularisations. A new estimation theory based on functional structural sparsity will allow a fair balance between model interpretability, distributional flexibility and structural complexity. A typical example is time varying Lasso with predictable and economically interpretable sparseness parameter. A graph based view on time series data is necessary if one considers e.g. the dynamics of social or systemic risk networks. Here the IRTG contributes via research on combinatorial inference on e.g. which nodes are central and which nodes are infectious in the sense of creating similar sentiments in a financial context. Such analysis is also demanded in the rapidly growing crypto currency system, where one likes to identify market movers and dynamic survival rates of crypto currencies. To achieve these research goals the qualification programme requires a multi facet course programme that involves solid mathematical and statistical training in combination with modern machine learning skills. Short courses augmenting knowledge on current topics in dimension reduction, dynamic sparseness and networks are held by visiting researchers.


Principal Investigators
Härdle, Wolfgang Prof. Dr. (Details) (Statistics)

Further project members
Breunig, Christoph Prof. Dr. (Details) (Econometrics (J))
Burda, Michael C. Prof. Ph. D. (Details) (Economic Theory II)
Fitzenberger, Bernd Prof. Ph.D. (Details) (Econometrics (Faculty of Economics and Business Administration))
Lessmann, Stefan Prof. Dr. (Details) (Information Systems)
López Cabrera, Brenda Prof. Dr. (Details) (Quantitative Climate, Weather and Energy Analysis)
Reiß, Markus Prof. Dr. (Details) (Mathematical Statistics)
Wang, Weining Prof. Dr. (Details) (Nonparametric Statistics and Dynamic Risk Management)

Participating external organizations

Duration of Project
Start date: 04/2018
End date: 09/2022

Research Areas
Economics

Research Areas
Betriebswirtschaft, Big Data, Univariate and Multivariate Regression, Regression Models, China

Publications
1. Moro RA, Härdle WK, Schäfer D (2017) Company rating with support vector machines. Statistics&risk modeling, Vol 34 Issue: 1-2 Pages: 55-67 DOI: doi 10.1515/strm-2012-1141
2. Liu R, Härdle WK, Zhang G (2017) Statistical Inference for Generalized Additive Partially Linear Model, J Multivariate Analysis, doi 10.1016/j.jmva.2017.07.011
3. Härdle WK, Osipenko M (2017) Dynamic Valuation of Weather Derivatives under Default Risk, International Journal of Financial Studies, doi 10.3390/ijfs5040023
4. Belomestny D, Härdle WK, Krymova E (2017) Sieve estimation of the minimal entropy martingale marginal density with application to pricing kernel estimation, International J of Theoretical and Applied Finance, DOI 10.1142/S0219024917500418
5. Chao SK, Härdle WK, Huang C (2018) Multivariate Factorisable Sparse Asymmetric Least Squares Regression. Comp Stat Data Analysis, doi 10.1016/j.csda.2017.12.001
6. Linton M, Teo EGS, Bommes E, Chen CYH, Härdle WK (2017) Dynamic Topic Modelling for Cryptocurrency Community Forums. p 355-372, Applied Quantitative Finance (Härdle, Chen, Overbeck eds) Springer Verlag, DOI 10.1007/978-3-662-54486-0
7. Härdle W K, Phoon KF, Lee D (2017) Credit Rating Score Analysis. p 223-244 Applied Quantitative Finance, (Härdle WK, Chen YH, Overbeck L eds), Springer Verlag, DOI 10.1007/978-3-662-54486-0
8. Chen CYH, Chiang CT, Härdle WK (2018) Downside risk and stock returns: An empirical analysis of the long-run and short-run dynamics from the G-7 Countries. J Banking and Finance, Volume 93, August 2018, pp. 21-32, DOI 10.1016/j.jbankfin.2018.05.012
9. Zharova A, Tellinger-Rice J, Härdle WK (2018) How to Measure the Performance of a Collaborative Research Center, Scientometrics, https://link.springer.com/article/10.1007/s11192-018-2910-8 DOI: https://doi.org/10.1007/s11192-018-2910-8
10. Winkelmann, L, Bibinger, M (2018) Common price and volatility jumps in noisy high-frequency data. Electronic Journal of Statistics, 12, 2018-2073, 2018
11. Chen CYH, Härdle WK, Okhrin Y (2018) Tail event driven networks of SIFIs. J Econometrics, DOI: https://doi.org/10.1016/j.jeconom.2018.09.016
12. Chen Y, Härdle WK, Qiang H, Majer, P (2018) Risk Related Brain Regions Detected with 3D Image FPCA, Statistics and Risk Modeling, DOI: https://doi.org/10.1515/strm-2017-0011
13. Ngoc MT, Osipenko M, Härdle WK, Burdejova P (2018) Principal Components in an Asymmetric Norm. J Multivariate Analysis 20181008 accepted
14. Trimborn S, Härdle WK (2018) CRIX an Index for Cryptocurrencies, Empirical Finance, DOI: https://doi.org/10.1016/j.jempfin.2018.08.004
15. Vomfell L, Härdle WK, Lessmann, S (2018) Improving Crime Count Forecasts Using Twitter and Taxi Data, Decision Support Systems, DOI:https://doi.org/10.1016/j.dss.2018.07.003
16. Bibinger M, Neely Ch, Winkelmann L (2019) Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book, DOI:https://doi.org/10.1016/j.jeconom.2019.01.001
17. Chua WS, Chen Y, Härdle WK (2019) Forecasting Limit Order Book Liquidity Supply-Demand Curves with Functional AutoRegressive Dynamics. Quantitative Finance, DOI: https://doi.org/10.1080/14697688.2019.1622290
18. Kostmann M, Härdle WK (2019) Forecasting in Blockchain-Based Local Energy Markets. Energies 2019, 12(14), 2718; https://doi.org/10.3390/en12142718
19. Klein N, Werwatz H, Kneib T (2019)Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales. Journal of Econometrics Corresponding. https://doi.org/10.1016/j.jeconom.2019.07.003
20. Lux M, Härdle WK, Lessmann S (2019) An AI approach to measuring financial risk. Comp Stat Data Analysis, DOI: 10.1007/s00180-019-00934-7
21. Yu L, Härdle WK, Borke L, Benschop T (2019) Data Driven Value-at-Risk Forecasting using a SVR-GARCH-KDE Hybrid. The Singapore Economic Review, DOI: 10.1142/S0217590819500668
20. Qian Y, Härdle WK, Chen CYH (2019) Modelling Industry Interdependency Dynamics in a Network Context. Studies in Economics and Finance. DOI: https://doi.org/10.1108/SEF-07-2019-0272

Last updated on 2021-12-10 at 10:01