Lehrstuhl für Statistik und Ökonometrie

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90403 Nürnberg

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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
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
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
Clegg, M., & Krauß, C. (2018). Pairs trading with partial cointegration. Quantitative Finance, 18(1), 121-138.
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
Stübinger, J., & Bredthauer, J. (2017). Statistical arbitrage pairs trading with high-frequency data. International Journal of Economics and Financial Issues, 7(4), 650-662.
Fischer, M., Kraus, D., Pfeuffer, M., & Czado, C. (2017). Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression. Risks, 5(3), 38. https://dx.doi.org/10.3390/risks5030038
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
Clegg, M., Krauss, C., & Rende, J. (2017). partialCI: An R package for the analysis of partially cointegrated time series.
Krauß, C., & Herrmann, K. (2017). On the power and size properties of cointegration tests in the light of high-frequency stylized facts. Journal of Financial Risk Management, 10(1).
Krauß, C. (2017). Statistical arbitrage pairs trading strategies: Review and outlook. Journal of Economic Surveys, 31(2), 513-545. https://dx.doi.org/10.1111/joes.12153
Krauss, C., & Stübinger, J. (2017). Non-linear dependence modelling 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
Krauß, C., Do, X.A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702. https://dx.doi.org/10.1016/j.ejor.2016.10.031
Pfeuffer, M. (2017). ctmcd: An R Package for Estimating the Parameters of a Continuous-Time Markov Chain from Discrete-Time Data. The R Journal, 9(2), 127-141.
Fischer, M., & Pfeuffer, M. (2016). IFRS 9 Impairment von Finanzinstrumenten: Parametrische Modellierung von PD-Kurven. Risiko Manager, 11(7), 10-14.
Klein, I., Mangold, B., & Doll, M. (2016). Cumulative Paired ϕ-Entropy. Entropy, 18(7), 1-45. https://dx.doi.org/10.3390/e18070248
Kilgert, B., Rybizki, L., Grottke, M., Neurath, M., & Neumann, H. (2014). Prospective long-term assessment of sedation-related adverse events and patient satisfaction for upper endoscopy and colonoscopy. Digestion, 90(1), 42-8. https://dx.doi.org/10.1159/000363567

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