Lehrstuhl für Statistik und Ökonometrie

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90403 Nürnberg


Forschungsprojekt(e)


Heterogenität makroökonomischer Erwartungen: Welche Rolle spielen individuelle historische Erfahrungen, das örtliche Umfeld und sozioökonomische Faktoren?
Prof. Dr. Jonas Dovern
(01.07.2019 - 30.06.2022)



Publikationen (Download BibTeX)

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Endres, S., & Stübinger, J. (2019). Optimal trading strategies for Levy-driven Ornstein-Uhlenbeck processes. Applied economics, 51(29), 3153-3169. https://dx.doi.org/10.1080/00036846.2019.1566688
Stübinger, J. (2019). Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500. Quantitative Finance, 19(6), 921-935. https://dx.doi.org/10.1080/14697688.2018.1537503
Knoll, J., Stübinger, J., & Grottke, M. (2019). Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500. Quantitative Finance, 19(4), 571-585. https://dx.doi.org/10.1080/14697688.2018.1521002
Möstel, L., Fischer, M., Pfaelzner, F., & Pfeuffer, M. (2019). Parameter estimation of Tukey-type distributions: A comparative analysis. Communications in Statistics-Simulation and Computation. https://dx.doi.org/10.1080/03610918.2019.1571604
Endres, S., & Stübinger, J. (2019). A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns. Quantitative Finance. https://dx.doi.org/10.1080/14697688.2019.1585562
Fischer, T., Krauss, C., & Deinert, A. (2019). Statistical Arbitrage in Cryptocurrency Markets. Journal of Risk and Financial Management, 12(1). https://dx.doi.org/10.3390/jrfm12010031
Schnaubelt, M., Rende, J., & Krauss, C. (2019). Testing Stylized Facts of Bitcoin Limit Order Books. Journal of Risk and Financial Management, 12(1). https://dx.doi.org/10.3390/jrfm12010025
Dovern, J., & Zuber, C. (2019). Recessions and Potential Output: Disentangling Measurement Errors, Supply Shocks, and Hysteresis Effects. Scandinavian Journal of Economics, forthcoming. https://dx.doi.org/10.1111/sjoe.12385
Schnaubelt, M., Fischer, T., & Krauß, C. (2019). Separating the signal from the noise - financial machine learning for Twitter. (Unpublished, Submitted).
Stübinger, J., Mangold, B., Krauß, C., & Krauss, C. (2018). Statistical arbitrage with vine copulas. Quantitative Finance. https://dx.doi.org/10.1080/14697688.2018.1438642
Stübinger, J., & Endres, S. (2018). Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. Quantitative Finance. https://dx.doi.org/10.1080/14697688.2017.1417624
Klein, I., Fischer, M., & Pleier, T. (2018). Weighted Power Mean Copulas: Theory and Application. Model Assisted Statistics and Applications, 13(3), 253-270. https://dx.doi.org/10.3233/MAS-180436
Fischer, T., & Krauß, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669. https://dx.doi.org/10.1016/j.ejor.2017.11.054
Glas, A., Donaubauer, J., Meyer, B., & Nunnenkamp, P. (2018). Disentangling the impact of infrastructure on trade using a new index of infrastructure. Review of World Economics, 154 (4), 745-784.
Clegg, M., & Krauß, C. (2018). Pairs trading with partial cointegration. Quantitative Finance, 18(1), 121-138.
Pfeuffer, M., Möstel, L., & Fischer, M. (2018). An Extended Likelihood Framework for Modeling Discretely Observed Credit Rating Transitions. Quantitative Finance. https://dx.doi.org/10.1080/14697688.2018.1465196
Doll, M., Seebauer, M., & Tonn, M. (2017). Bargaining over waiting time in gain and loss framed ultimatum games.
Krauss, C., & Stübinger, J. (2017). Nonlinear dependence modeling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100. Applied economics, 49(52), 5352-5369. https://dx.doi.org/10.1080/00036846.2017.1305097
Dovern, J., & Jannsen, N. (2017). Systematic errors in growth expectations over the business cycle. International Journal of Forecasting, 33(4), 760-769. https://dx.doi.org/10.1016/j.ijforecast.2017.03.003
Agarwal, N., Grottke, M., Mishra, S., & Brem, A. (2017). A Systematic Literature Review of Constraint-Based Innovations: State of the Art and Future Perspectives. IEEE Transactions on Engineering Management, 64(1), 3-15. https://dx.doi.org/10.1109/TEM.2016.2620562

Zuletzt aktualisiert 2019-24-04 um 10:15