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Dominik Wied

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Jorg Breitung & Alexander Mayer & Dominik Wied, 2022. "Asymptotic Properties of Endogeneity Corrections Using Nonlinear Transformations," Papers 2207.09246, arXiv.org, revised Nov 2023.

    Cited by:

    1. Dominik Wied, 2022. "Semiparametric Distribution Regression with Instruments and Monotonicity," Papers 2212.03704, arXiv.org.

  2. Wagner, Martin & Wied, Dominik, 2014. "Monitoring Stationarity and Cointegration," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100386, Verein für Socialpolitik / German Economic Association.

    Cited by:

    1. Vasyl Golosnoy, 2018. "Sequential monitoring of portfolio betas," Statistical Papers, Springer, vol. 59(2), pages 663-684, June.
    2. Martin Wagner & Dominik Wied, 2017. "Consistent Monitoring of Cointegrating Relationships: The US Housing Market and the Subprime Crisis," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 960-980, November.

  3. Bucher, Axel & Jaschke, Stefan & Wied, Dominik, 2013. "Nonparametric tests for constant tail dependence with an application to energy and finance," LIDAM Discussion Papers ISBA 2013033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Guo, Ranran & Ye, Wuyi, 2021. "A model of dynamic tail dependence between crude oil prices and exchange rates," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. Kuang-Liang Chang, 2021. "A New Dynamic Mixture Copula Mechanism to Examine the Nonlinear and Asymmetric Tail Dependence Between Stock and Exchange Rate Returns," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 965-999, December.
    3. Monica Billio & Lorenzo Frattarolo & Dominique Guégan, 2022. "High-Dimensional Radial Symmetry of Copula Functions: Multiplier Bootstrap vs. Randomization," Post-Print hal-04085236, HAL.
    4. Y Hoga, 2018. "A structural break test for extremal dependence in β-mixing random vectors," Biometrika, Biometrika Trust, vol. 105(3), pages 627-643.
    5. Can, S.U. & Einmahl, John & Laeven, Roger, 2021. "Two-Sample Testing for Tail Copulas with an Application to Equity Indices," Other publications TiSEM 65a9e694-665d-4671-aaf1-4, Tilburg University, School of Economics and Management.
    6. Kiriliouk, Anna & Segers, Johan & Tafakori, Laleh, 2017. "An estimator of the stable tail dependence function based on the empirical beta copula," LIDAM Discussion Papers ISBA 2017028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Marek Omelka & Šárka Hudecová & Natalie Neumeyer, 2021. "Maximum pseudo‐likelihood estimation based on estimated residuals in copula semiparametric models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1433-1473, December.
    8. Arnold Polanski & Evarist Stoja & Ching‐Wai (Jeremy) Chiu, 2021. "Tail risk interdependence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5499-5511, October.
    9. Kiriliouk, Anna & Segers, Johan & Tafakori, Laleh, 2018. "An estimator of the stable tail dependence function based on the empirical beta copula," LIDAM Discussion Papers ISBA 2018029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Qin, Xiao & Zhou, Chen, 2021. "Systemic risk allocation using the asymptotic marginal expected shortfall," Journal of Banking & Finance, Elsevier, vol. 126(C).
    11. Roman Matkovskyy, 2019. "Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries," Post-Print hal-02332090, HAL.
    12. Semeyutin, Artur & O’Neill, Robert, 2019. "A brief survey on the choice of parameters for: “Kernel density estimation for time series data”," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).

  4. Rothe, Christoph & Wied, Dominik, 2012. "Misspecification Testing in a Class of Conditional Distributional Models," IZA Discussion Papers 6364, Institute of Labor Economics (IZA).

    Cited by:

    1. Franco Peracchi & Samantha Leorato, 2015. "Shape Regressions," Working Papers gueconwpa~15-15-06, Georgetown University, Department of Economics.
    2. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2009. "Inference on Counterfactual Distributions," Papers 0904.0951, arXiv.org, revised Sep 2013.
    3. Igor L. Kheifets, 2015. "Specification tests for nonlinear dynamic models," Econometrics Journal, Royal Economic Society, vol. 18(1), pages 67-94, February.
    4. Chernozhukov, Victor & Fernández-Val, Iván & Melly, Blaise & Wüthrich, Kaspar, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," University of California at San Diego, Economics Working Paper Series qt5zm6m9rq, Department of Economics, UC San Diego.
    5. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    6. Wied, Dominik & Dehling, Herold & van Kampen, Maarten & Vogel, Daniel, 2014. "A fluctuation test for constant Spearman’s rho with nuisance-free limit distribution," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 723-736.
    7. Russell Davidson & Victoria Zinde‐Walsh, 2017. "Advances in specification testing," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1595-1631, December.
    8. Pallab Kumar Ghosh & Jae Yoon Lee, 2016. "Decomposition of Changes in Korean Wage Inequality, 1998–2007," Journal of Labor Research, Springer, vol. 37(1), pages 1-28, March.
    9. Redmond, Paul & Doorley, Karina & McGuinness, Seamus, 2019. "The impact of a change in the National Minimum Wage on the distribution of hourly wages and household income in Ireland," Research Series, Economic and Social Research Institute (ESRI), number RS86, June.
    10. Alexander Sohn, 2015. "Beyond Conventional Wage Discrimination Analysis: Assessing Comprehensive Wage Distributions of Males and Females Using Structured Additive Distributional Regression," SOEPpapers on Multidisciplinary Panel Data Research 802, DIW Berlin, The German Socio-Economic Panel (SOEP).
    11. Miguel A. Delgado & Andr'es Garc'ia-Suaza & Pedro H. C. Sant'Anna, 2019. "Distribution Regression in Duration Analysis: an Application to Unemployment Spells," Papers 1904.06185, arXiv.org, revised Nov 2021.
    12. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
    13. Guillaume Bagnarosa & Mark Cummins & Michael Dowling & Fearghal Kearney, 2022. "Commodity risk in European dairy firms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(1), pages 151-181.
    14. Rothe, Christoph, 2012. "Decomposing the Composition Effect," IZA Discussion Papers 6397, Institute of Labor Economics (IZA).
    15. Paul Redmond & Karina Doorley & Seamus McGuinness, 2021. "The impact of a minimum wage change on the distribution of wages and household income," Oxford Economic Papers, Oxford University Press, vol. 73(3), pages 1034-1056.
    16. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2023. "Distributional Vector Autoregression: Eliciting Macro and Financial Dependence," Papers 2303.04994, arXiv.org.
    17. Wang, Yunyun & Oka, Tatsushi & Zhu, Dan, 2023. "Bivariate distribution regression with application to insurance data," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 215-232.
    18. Jorg Breitung & Alexander Mayer & Dominik Wied, 2022. "Asymptotic Properties of Endogeneity Corrections Using Nonlinear Transformations," Papers 2207.09246, arXiv.org, revised Nov 2023.
    19. Hong Chen & Maik Döring & Uwe Jensen, 2018. "Test for model selection using Cramér–von Mises distance in a fixed design regression setting," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 505-535, October.
    20. Jiun-Hua Su, 2019. "Counterfactual Inference in Duration Models with Random Censoring," Papers 1902.08502, arXiv.org.
    21. Yuanhua Feng & Wolfgang Karl Härdle, 2021. "Uni- and multivariate extensions of the sinh-arcsinh normal distribution applied to distributional regression," Working Papers CIE 142, Paderborn University, CIE Center for International Economics.
    22. Dominik Wied, 2022. "Semiparametric Distribution Regression with Instruments and Monotonicity," Papers 2212.03704, arXiv.org.
    23. Roger Koenker & Samantha Leorato & Franco Peracchi, 2013. "Distributional vs. Quantile Regression," CEIS Research Paper 300, Tor Vergata University, CEIS, revised 17 Dec 2013.
    24. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.
    25. Ghosh, Pallab Kumar, 2014. "The contribution of human capital variables to changes in the wage distribution function," Labour Economics, Elsevier, vol. 28(C), pages 58-69.
    26. Mittag, Nikolas, 2016. "Correcting for Misreporting of Government Benefits," IZA Discussion Papers 10266, Institute of Labor Economics (IZA).
    27. Kneib, Thomas & Silbersdorff, Alexander & Säfken, Benjamin, 2023. "Rage Against the Mean – A Review of Distributional Regression Approaches," Econometrics and Statistics, Elsevier, vol. 26(C), pages 99-123.
    28. Afrouz Azadikhah Jahromi & Brantly Callaway, 2019. "Heterogeneous Effects of Job Displacement on Earnings," DETU Working Papers 1901, Department of Economics, Temple University.
    29. Rothe, Christoph & Wied, Dominik, 2020. "Estimating derivatives of function-valued parameters in a class of moment condition models," Journal of Econometrics, Elsevier, vol. 217(1), pages 1-19.
    30. Pallab Ghosh & Jae Lee, 2016. "Decomposition of Changes in Korean Wage Inequality, 1998–2007," Journal of Labor Research, Springer, vol. 37(1), pages 1-28, March.
    31. Bargain, Olivier & Doorley, Karina & Van Kerm, Philippe, 2018. "Minimum Wages and the Gender Gap in Pay: New Evidence from the UK and Ireland," IZA Discussion Papers 11502, Institute of Labor Economics (IZA).
    32. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.
    33. Philippe Van Kerm & Seunghee Yu & Chung Choe, 2016. "Decomposing quantile wage gaps: a conditional likelihood approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 507-527, August.
    34. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    35. Pedro H. C. Sant'Anna & Xiaojun Song, 2020. "Specification tests for generalized propensity scores using double projections," Papers 2003.13803, arXiv.org, revised Apr 2023.
    36. Nikolas Mittag, 2019. "Correcting for Misreporting of Government Benefits," American Economic Journal: Economic Policy, American Economic Association, vol. 11(2), pages 142-164, May.
    37. Brantly Callaway & Weige Huang, 2020. "Distributional Effects of a Continuous Treatment with an Application on Intergenerational Mobility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 808-842, August.
    38. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.

Articles

  1. Fang Duan & Hans Manner & Dominik Wied, 2022. "Model and Moment Selection in Factor Copula Models [Extensions to the Gaussian Copula: Random Recovery and Random Factor Loadings]," Journal of Financial Econometrics, Oxford University Press, vol. 20(1), pages 45-75.

    Cited by:

    1. K. B. Gubbels & J. Y. Ypma & C. W. Oosterlee, 2023. "Principal Component Copulas for Capital Modelling," Papers 2312.13195, arXiv.org.

  2. Manner Hans & Stark Florian & Wied Dominik, 2021. "A monitoring procedure for detecting structural breaks in factor copula models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(4), pages 171-192, September.

    Cited by:

    1. Mayer, Alexander & Wied, Dominik, 2023. "Estimation and inference in factor copula models with exogenous covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 1500-1521.

  3. Victor Troster & Dominik Wied, 2021. "A specification test for dynamic conditional distribution models with function-valued parameters," Econometric Reviews, Taylor & Francis Journals, vol. 40(2), pages 109-127, February.

    Cited by:

    1. Perera, Indeewara & Silvapulle, Mervyn J., 2023. "Bootstrap specification tests for dynamic conditional distribution models," Journal of Econometrics, Elsevier, vol. 235(2), pages 949-971.

  4. Rothe, Christoph & Wied, Dominik, 2020. "Estimating derivatives of function-valued parameters in a class of moment condition models," Journal of Econometrics, Elsevier, vol. 217(1), pages 1-19.

    Cited by:

    1. Rafael Weißbach & Dominik Wied, 2022. "Truncating the exponential with a uniform distribution," Statistical Papers, Springer, vol. 63(4), pages 1247-1270, August.
    2. Jonas Meier, 2020. "Multivariate Distribution Regression," Diskussionsschriften dp2023, Universitaet Bern, Departement Volkswirtschaft.

  5. Manner, Hans & Stark, Florian & Wied, Dominik, 2019. "Testing for structural breaks in factor copula models," Journal of Econometrics, Elsevier, vol. 208(2), pages 324-345.

    Cited by:

    1. Gaete, Michael & Herrera, Rodrigo, 2023. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," Journal of Commodity Markets, Elsevier, vol. 32(C).
    2. Cai, Guixin & Zhang, Hao & Chen, Ziyue, 2019. "Comovement between commodity sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1247-1258.
    3. Mayer, Alexander & Wied, Dominik, 2023. "Estimation and inference in factor copula models with exogenous covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 1500-1521.
    4. Tim Kutzker & Florian Stark & Dominik Wied, 2021. "Testing for relevant dependence change in financial data: a CUSUM copula approach," Empirical Economics, Springer, vol. 60(4), pages 1875-1894, April.
    5. Li, Zhenghui & Chen, Liming & Dong, Hao, 2021. "What are bitcoin market reactions to its-related events?," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 1-10.
    6. Chen Tong & Peter Reinhard Hansen, 2023. "Characterizing Correlation Matrices that Admit a Clustered Factor Representation," Papers 2308.05895, arXiv.org.
    7. Florian Stark & Sven Otto, 2020. "Testing and Dating Structural Changes in Copula-based Dependence Measures," Papers 2011.05036, arXiv.org.

  6. Peter N. Posch & Daniel Ullmann & Dominik Wied, 2019. "Detecting structural changes in large portfolios," Empirical Economics, Springer, vol. 56(4), pages 1341-1357, April.

    Cited by:

    1. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    2. Florian Stark & Sven Otto, 2020. "Testing and Dating Structural Changes in Copula-based Dependence Measures," Papers 2011.05036, arXiv.org.

  7. Matei Demetrescu & Dominik Wied, 2019. "Testing for constant correlation of filtered series under structural change," The Econometrics Journal, Royal Economic Society, vol. 22(1), pages 10-33.

    Cited by:

    1. Georgios Bampinas & Theodore Panagiotidis & Panagiotis Politsidis, 2023. "Sovereign bond and CDS market contagion: A story from the Eurozone crisis," Post-Print hal-04164277, HAL.
    2. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    3. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Cambridge Working Papers in Economics 2367, Faculty of Economics, University of Cambridge.
    4. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    5. Nils Bertschinger & Axel A. Araneda, 2021. "Cross-ownership as a structural explanation for rising correlations in crisis times," Papers 2112.04824, arXiv.org.

  8. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.

    Cited by:

    1. Duan, Fang, 2022. "Forecasting risk measures based on structural breaks in the correlation matrix," Ruhr Economic Papers 945, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    3. N. Henze & C. Kirch & S. G. Meintanis, 2018. "Special Issue with papers from the “3rd workshop on Goodness-of-fit and change-point problems”," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 587-588, August.

  9. Dominik Wied, 2017. "A nonparametric test for a constant correlation matrix," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1157-1172, November.

    Cited by:

    1. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2022. "Change point analysis of covariance functions: A weighted cumulative sum approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    2. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.
    3. Peter N. Posch & Daniel Ullmann & Dominik Wied, 2019. "Detecting structural changes in large portfolios," Empirical Economics, Springer, vol. 56(4), pages 1341-1357, April.
    4. Duan, Fang, 2022. "Forecasting risk measures based on structural breaks in the correlation matrix," Ruhr Economic Papers 945, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. Choi, Ji-Eun & Shin, Dong Wan, 2019. "Moving block bootstrapping for a CUSUM test for correlation change," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 95-106.
    6. Choi, Ji-Eun & Shin, Dong Wan, 2020. "A self-normalization test for correlation change," Economics Letters, Elsevier, vol. 193(C).
    7. Thilo A. Schmitt & Rudi Schäfer & Dominik Wied & Thomas Guhr, 2016. "Spatial dependence in stock returns: local normalization and VaR forecasts," Empirical Economics, Springer, vol. 50(3), pages 1091-1109, May.
    8. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    9. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Cambridge Working Papers in Economics 2367, Faculty of Economics, University of Cambridge.
    10. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    11. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.

  10. Martin Wagner & Dominik Wied, 2017. "Consistent Monitoring of Cointegrating Relationships: The US Housing Market and the Subprime Crisis," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 960-980, November.

    Cited by:

    1. Julia Reynolds & Leopold Sögner & Martin Wagner, 2021. "Deviations from Triangular Arbitrage Parity in Foreign Exchange and Bitcoin Markets," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(2), pages 105-146, June.
    2. Skrobotov, Anton, 2021. "Structural breaks in cointegration models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 117-141.
    3. Alfredo Cuecuecha Mendoza & Miguel Cruz, 2022. "Impact of the Covid19 Pandemic on Remittances to Mexico," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(3), pages 1-26, Julio - S.
    4. Gueye, Ghislain Nono, 2021. "Pitfalls in the cointegration analysis of housing prices with the macroeconomy: Evidence from OECD countries," Journal of Housing Economics, Elsevier, vol. 51(C).
    5. Knorre, Fabian & Wagner, Martin & Grupe, Maximilian, 2020. "Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions," IHS Working Paper Series 27, Institute for Advanced Studies.
    6. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.
    7. Luis F. Martins & Paulo M. M. Rodrigues, 2022. "Tests for segmented cointegration: an application to US governments budgets," Empirical Economics, Springer, vol. 63(2), pages 567-600, August.

  11. Dehling, Herold & Vogel, Daniel & Wendler, Martin & Wied, Dominik, 2017. "Testing For Changes In Kendall’S Tau," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1352-1386, December.

    Cited by:

    1. Zacharias Psaradakis & Marian Vavra, 2020. "On Using Triples to Assess Symmetry Under Weak Dependence," Working and Discussion Papers WP 7/2020, Research Department, National Bank of Slovakia.
    2. Mariusz Czekala & Zbigniew Kurylek, 2021. "Inversions Distribution and Testing Correlation Changes for Rates of Return," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 633-650.
    3. Betken, Annika & Dehling, Herold & Nüßgen, Ines & Schnurr, Alexander, 2021. "Ordinal pattern dependence as a multivariate dependence measure," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    4. Manner, Hans & Stark, Florian & Wied, Dominik, 2019. "Testing for structural breaks in factor copula models," Journal of Econometrics, Elsevier, vol. 208(2), pages 324-345.

  12. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.

    Cited by:

    1. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.
    2. Peter N. Posch & Daniel Ullmann & Dominik Wied, 2019. "Detecting structural changes in large portfolios," Empirical Economics, Springer, vol. 56(4), pages 1341-1357, April.
    3. Zifeng Zhao & Feiyu Jiang & Xiaofeng Shao, 2022. "Segmenting time series via self‐normalisation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1699-1725, November.
    4. Dominik Wied, 2017. "A nonparametric test for a constant correlation matrix," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1157-1172, November.

  13. Holger Dette & Dominik Wied, 2016. "Detecting relevant changes in time series models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 371-394, March.

    Cited by:

    1. Chi Yao & Wei Yu & Xuejun Wang, 2023. "Strong Consistency for the Conditional Self-weighted M Estimator of GRCA(p) Models," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-21, March.
    2. Tim Kutzker & Florian Stark & Dominik Wied, 2021. "Testing for relevant dependence change in financial data: a CUSUM copula approach," Empirical Economics, Springer, vol. 60(4), pages 1875-1894, April.
    3. Boot, Tom & Pick, Andreas, 2020. "Does modeling a structural break improve forecast accuracy?," Journal of Econometrics, Elsevier, vol. 215(1), pages 35-59.
    4. Anastasiou, Andreas & Cribben, Ivor & Fryzlewicz, Piotr, 2022. "Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity," LSE Research Online Documents on Economics 112148, London School of Economics and Political Science, LSE Library.
    5. Horváth, Lajos & Trapani, Lorenzo, 2019. "Testing for randomness in a random coefficient autoregression model," Journal of Econometrics, Elsevier, vol. 209(2), pages 338-352.
    6. Emura, Takeshi & Lai, Ching-Chieh & Sun, Li-Hsien, 2023. "Change point estimation under a copula-based Markov chain model for binomial time series," Econometrics and Statistics, Elsevier, vol. 28(C), pages 120-137.
    7. Tom Boot & Andreas Pick, 2017. "A near optimal test for structural breaks when forecasting under square error loss," Tinbergen Institute Discussion Papers 17-039/III, Tinbergen Institute.
    8. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    9. Axel Bücher & Holger Dette & Florian Heinrichs, 2023. "A portmanteau-type test for detecting serial correlation in locally stationary functional time series," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 255-278, July.
    10. Likai Chen & Weining Wang & Wei Biao Wu, 2017. "Dynamic Semiparametric Factor Model with a Common Break," SFB 649 Discussion Papers SFB649DP2017-026, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Eustasio del Barrio & Hristo Inouzhe & Carlos Matrán, 2020. "Box-Constrained Monotone Approximations to Lipschitz Regularizations, with Applications to Robust Testing," Journal of Optimization Theory and Applications, Springer, vol. 187(1), pages 65-87, October.

  14. Wied, Dominik & Weiß, Gregor N.F. & Ziggel, Daniel, 2016. "Evaluating Value-at-Risk forecasts: A new set of multivariate backtests," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 121-132.

    Cited by:

    1. Holger Fink & Andreas Fuest & Henry Port, 2018. "The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates," Risks, MDPI, vol. 6(3), pages 1-19, August.
    2. Małecka Marta, 2021. "Testing for a serial correlation in VaR failures through the exponential autoregressive conditional duration model," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 145-162, March.
    3. Marie Kratz & Yen H. Lok & Alexander J McNeil, 2016. "Multinomial VaR Backtests: A simple implicit approach to backtesting expected shortfall," Papers 1611.04851, arXiv.org.
    4. Michael B. Gordy & Alexander J. McNeil, 2018. "Spectral Backtests of Forecast Distributions with Application to Risk Management," Finance and Economics Discussion Series 2018-021, Board of Governors of the Federal Reserve System (U.S.).
    5. Nikola RADIVOJEVIĆ & Luka FILIPOVI & Тomislav D. BRZAKOVIĆ, 2020. "A New Semiparametric Mirrored Historical Simulation Value-At-Risk Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 5-21, March.
    6. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    7. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    8. Denisa Banulescu & Christophe Hurlin & Jeremy Leymarie & Olivier Scaillet, 2020. "Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures," Working Papers halshs-03088668, HAL.
    9. Leung, Melvern & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A., 2021. "Bayesian Value-at-Risk backtesting: The case of annuity pricing," European Journal of Operational Research, Elsevier, vol. 293(2), pages 786-801.
    10. Codrut Florin Ivascu & Daniela Serban, 2023. "Value at Risk Estimation for Non-Gaussian Distributions," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 15(2), pages 181-190, December.

  15. Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.

    Cited by:

    1. Castrillón-Candás, Julio E. & Kon, Mark, 2022. "Anomaly detection: A functional analysis perspective," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    2. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    3. Lorenzo Cerboni Baiardi & Massimo Costabile & Domenico De Giovanni & Fabio Lamantia & Arturo Leccadito & Ivar Massabó & Massimiliano Menzietti & Marco Pirra & Emilio Russo & Alessandro Staino, 2020. "The Dynamics of the S&P 500 under a Crisis Context: Insights from a Three-Regime Switching Model," Risks, MDPI, vol. 8(3), pages 1-15, July.
    4. Josua Gösmann & Tobias Kley & Holger Dette, 2021. "A new approach for open‐end sequential change point monitoring," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 63-84, January.
    5. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.
    6. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.

  16. Krämer, Walter & Wied, Dominik, 2015. "A simple and focused backtest of value at risk," Economics Letters, Elsevier, vol. 137(C), pages 29-31.

    Cited by:

    1. Yannick Hoga & Matei Demetrescu, 2023. "Monitoring Value-at-Risk and Expected Shortfall Forecasts," Management Science, INFORMS, vol. 69(5), pages 2954-2971, May.

  17. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.

    Cited by:

    1. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2022. "Change point analysis of covariance functions: A weighted cumulative sum approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    2. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.
    3. Jorge V Pérez-Rodríguez & María Santana-Gallego, 2020. "Modelling tourism receipts and associated risks, using long-range dependence models," Tourism Economics, , vol. 26(1), pages 70-96, February.
    4. Zhou, Xinmiao & Qian, Huanhuan & Pérez-Rodríguez, Jorge. V. & González López-Valcárcel, Beatriz, 2020. "Risk dependence and cointegration between pharmaceutical stock markets: The case of China and the USA," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    5. Duan, Fang, 2022. "Forecasting risk measures based on structural breaks in the correlation matrix," Ruhr Economic Papers 945, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. Sylvia Gottschalk, 2023. "From Black Wednesday to Brexit: Macroeconomic shocks and correlations of equity returns in France, Germany, Italy, Spain, and the United Kingdom," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2843-2873, July.
    7. Choi, Ji-Eun & Shin, Dong Wan, 2020. "A self-normalization test for correlation change," Economics Letters, Elsevier, vol. 193(C).
    8. Chakraborty, Sandip & Kakani, Ram Kumar & Sampath, Aravind, 2022. "Portfolio risk and stress across the business cycle," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    9. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    10. Hood, Matthew & Malik, Farooq, 2018. "Estimating downside risk in stock returns under structural breaks," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 102-112.
    11. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.
    12. Dominik Wied, 2017. "A nonparametric test for a constant correlation matrix," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1157-1172, November.

  18. Bücher, Axel & Jäschke, Stefan & Wied, Dominik, 2015. "Nonparametric tests for constant tail dependence with an application to energy and finance," Journal of Econometrics, Elsevier, vol. 187(1), pages 154-168.
    See citations under working paper version above.
  19. Wied, Dominik & Dehling, Herold & van Kampen, Maarten & Vogel, Daniel, 2014. "A fluctuation test for constant Spearman’s rho with nuisance-free limit distribution," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 723-736.

    Cited by:

    1. Hannele Väyrynen & Nina Helander & Tytti Vasell, 2017. "KNOWLEDGE MANAGEMENT FOR OPEN INNOVATION: COMPARING RESEARCH RESULTS BETWEEN SMEs AND LARGE COMPANIES," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1-22, June.
    2. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2013. "Über die Anwendbarkeit eines neuen Fluktuationstests für Korrelationen auf Finanzzeitreihen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 87-103, March.
    3. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    4. Jean-François Quessy, 2019. "Consistent nonparametric tests for detecting gradual changes in the marginals and the copula of multivariate time series," Statistical Papers, Springer, vol. 60(3), pages 717-746, June.
    5. Bücher, Axel & Kojadinovic, Ivan & Rohmer, Tom & Segers, Johan, 2014. "Detecting changes in cross-sectional dependence in multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 111-128.
    6. Florian Stark & Sven Otto, 2020. "Testing and Dating Structural Changes in Copula-based Dependence Measures," Papers 2011.05036, arXiv.org.
    7. Bucher, Axel & Jaschke, Stefan & Wied, Dominik, 2013. "Nonparametric tests for constant tail dependence with an application to energy and finance," LIDAM Discussion Papers ISBA 2013033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  20. Galeano, Pedro & Wied, Dominik, 2014. "Multiple break detection in the correlation structure of random variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 262-282.

    Cited by:

    1. Josua Gösmann & Daniel Ziggel, 2018. "An innovative risk management methodology for trading equity indices based on change points," Journal of Asset Management, Palgrave Macmillan, vol. 19(2), pages 99-109, March.
    2. Georgios Bampinas & Theodore Panagiotidis & Panagiotis Politsidis, 2023. "Sovereign bond and CDS market contagion: A story from the Eurozone crisis," Post-Print hal-04164277, HAL.
    3. Dominik Wied & Daniel Ziggel & Tobias Berens, 2013. "On the application of new tests for structural changes on global minimum-variance portfolios," Statistical Papers, Springer, vol. 54(4), pages 955-975, November.
    4. Adams, Zeno & Füss, Roland & Glück, Thorsten, 2017. "Are correlations constant? Empirical and theoretical results on popular correlation models in finance," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 9-24.
    5. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2022. "Change point analysis of covariance functions: A weighted cumulative sum approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    6. Cécile Bastidon & Antoine Parent & Pablo Jensen & Patrice Abry & Pierre Borgnat, 2020. "Graph-based era segmentation of international financial integration," Post-Print hal-04255796, HAL.
    7. Adams, Zeno & Glück, Thorsten, 2015. "Financialization in commodity markets: A passing trend or the new normal?," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 93-111.
    8. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    9. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.
    10. Adams, Zeno & Glück, Thorsten, 2013. "Financialization in Commodity Markets: Disentangling the Crisis from the Style Effect," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79949, Verein für Socialpolitik / German Economic Association.
    11. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2013. "Über die Anwendbarkeit eines neuen Fluktuationstests für Korrelationen auf Finanzzeitreihen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 87-103, March.
    12. David Hallac & Peter Nystrup & Stephen Boyd, 2019. "Greedy Gaussian segmentation of multivariate time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 727-751, September.
    13. Duan, Fang, 2022. "Forecasting risk measures based on structural breaks in the correlation matrix," Ruhr Economic Papers 945, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    14. Choi, Ji-Eun & Shin, Dong Wan, 2019. "Moving block bootstrapping for a CUSUM test for correlation change," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 95-106.
    15. Sylvia Gottschalk, 2023. "From Black Wednesday to Brexit: Macroeconomic shocks and correlations of equity returns in France, Germany, Italy, Spain, and the United Kingdom," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2843-2873, July.
    16. Choi, Ji-Eun & Shin, Dong Wan, 2020. "A self-normalization test for correlation change," Economics Letters, Elsevier, vol. 193(C).
    17. Nguyen, Quynh Nga & Aboura, Sofiane & Chevallier, Julien & Zhang, Lyuyuan & Zhu, Bangzhu, 2020. "Local Gaussian correlations in financial and commodity markets," European Journal of Operational Research, Elsevier, vol. 285(1), pages 306-323.
    18. Georgios Bampinas & Theodore Panagiotidis, 2017. "Oil and stock markets before and after financial crises : a local Gaussian correlation approach," Bank of Estonia Working Papers wp2016-11, Bank of Estonia, revised 06 Feb 2017.
    19. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    20. Ordu-Akkaya, Beyza Mina & Soytas, Ugur, 2020. "Unconventional monetary policy and financialization of commodities," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    21. Adams, Zeno & Glueck, Thorsten, 2014. "Financialization in Commodity Markets: A Passing Trend or the New Normal?," Working Papers on Finance 1413, University of St. Gallen, School of Finance, revised Aug 2015.
    22. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.
    23. Bucher, Axel & Jaschke, Stefan & Wied, Dominik, 2013. "Nonparametric tests for constant tail dependence with an application to energy and finance," LIDAM Discussion Papers ISBA 2013033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  21. Ziggel, Daniel & Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2014. "A new set of improved Value-at-Risk backtests," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 29-41.

    Cited by:

    1. Berger, Theo & Gençay, Ramazan, 2018. "Improving daily Value-at-Risk forecasts: The relevance of short-run volatility for regulatory quality assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 30-46.
    2. Holger Fink & Andreas Fuest & Henry Port, 2018. "The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates," Risks, MDPI, vol. 6(3), pages 1-19, August.
    3. Argyropoulos, Christos & Panopoulou, Ekaterini, 2019. "Backtesting VaR and ES under the magnifying glass," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 22-37.
    4. R Herrera & Adam Clements, 2015. "Point process models for extreme returns: Harnessing implied volatility," NCER Working Paper Series 104, National Centre for Econometric Research.
    5. Emrah Altun & Huseyin Tatlidil & Gamze Ozel & Saralees Nadarajah, 2018. "Does the Assumption on Innovation Process Play an Important Role for Filtered Historical Simulation Model?," JRFM, MDPI, vol. 11(1), pages 1-13, January.
    6. Krämer, Walter & Wied, Dominik, 2015. "A simple and focused backtest of value at risk," Economics Letters, Elsevier, vol. 137(C), pages 29-31.
    7. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    8. Dionne, Georges & Pacurar, Maria & Zhou, Xiaozhou, 2014. "Liquidity-adjusted Intraday Value at Risk modeling and risk management: An application to data from Deutsche Börse," Working Papers 14-1, HEC Montreal, Canada Research Chair in Risk Management.
    9. Michael B. Gordy & Alexander J. McNeil, 2018. "Spectral Backtests of Forecast Distributions with Application to Risk Management," Finance and Economics Discussion Series 2018-021, Board of Governors of the Federal Reserve System (U.S.).
    10. Gabriela Zeller & Matthias Scherer, 2023. "Risk mitigation services in cyber insurance: optimal contract design and price structure," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 48(2), pages 502-547, April.
    11. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    12. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    13. Yannick Hoga & Matei Demetrescu, 2023. "Monitoring Value-at-Risk and Expected Shortfall Forecasts," Management Science, INFORMS, vol. 69(5), pages 2954-2971, May.
    14. Pourkhanali, Armin & Tafakori, Laleh & Bee, Marco, 2023. "Forecasting Value-at-Risk using functional volatility incorporating an exogenous effect," International Review of Financial Analysis, Elsevier, vol. 89(C).
    15. Leung, Melvern & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A., 2021. "Bayesian Value-at-Risk backtesting: The case of annuity pricing," European Journal of Operational Research, Elsevier, vol. 293(2), pages 786-801.
    16. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    17. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
    18. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.
    19. Benjamin Mögel & Benjamin R. Auer, 2018. "How accurate are modern Value-at-Risk estimators derived from extreme value theory?," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 979-1030, May.
    20. Slim, Skander & Koubaa, Yosra & BenSaïda, Ahmed, 2017. "Value-at-Risk under Lévy GARCH models: Evidence from global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 30-53.
    21. Taras Bodnar & Vilhelm Niklasson & Erik Thors'en, 2022. "Volatility Sensitive Bayesian Estimation of Portfolio VaR and CVaR," Papers 2205.01444, arXiv.org.
    22. Santiago Carrillo Menéndez & Bertrand Kian Hassani, 2021. "Expected Shortfall Reliability—Added Value of Traditional Statistics and Advanced Artificial Intelligence for Market Risk Measurement Purposes," Mathematics, MDPI, vol. 9(17), pages 1-20, September.
    23. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    24. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    25. Ahmed BenSaïda & Sabri Boubaker & Duc Khuong Nguyen & Skander Slim, 2018. "Value‐at‐risk under market shifts through highly flexible models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 790-804, December.
    26. Acereda, Beatriz & Leon, Angel & Mora, Juan, 2020. "Estimating the expected shortfall of cryptocurrencies: An evaluation based on backtesting," Finance Research Letters, Elsevier, vol. 33(C).
    27. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    28. Slim, Skander & Dahmene, Meriam & Boughrara, Adel, 2020. "How informative are variance risk premium and implied volatility for Value-at-Risk prediction? International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 22-37.
    29. Hood, Matthew & Malik, Farooq, 2018. "Estimating downside risk in stock returns under structural breaks," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 102-112.
    30. Codrut Florin Ivascu & Daniela Serban, 2023. "Value at Risk Estimation for Non-Gaussian Distributions," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 15(2), pages 181-190, December.
    31. Soren Bettels & Sojung Kim & Stefan Weber, 2022. "Multinomial Backtesting of Distortion Risk Measures," Papers 2201.06319, arXiv.org, revised Jan 2024.
    32. Klein, Tony & Walther, Thomas, 2016. "Oil price volatility forecast with mixture memory GARCH," Energy Economics, Elsevier, vol. 58(C), pages 46-58.
    33. Chen, Yu & Wang, Zhicheng & Zhang, Zhengjun, 2019. "Mark to market value at risk," Journal of Econometrics, Elsevier, vol. 208(1), pages 299-321.
    34. Evangelos Vasileiou, 2022. "Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1155-1171, March.
    35. Wied, Dominik & Weiß, Gregor N.F. & Ziggel, Daniel, 2016. "Evaluating Value-at-Risk forecasts: A new set of multivariate backtests," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 121-132.

  22. Tobias Berens & Dominik Wied & Daniel Ziggel, 2014. "Automated Portfolio Optimization Based on a New Test for Structural Breaks," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 2(2), pages 243-264, April.

    Cited by:

    1. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    2. Ourania Theodosiadou & Sotiris Skaperas & George Tsaklidis, 2017. "Change Point Detection and Estimation of the Two-Sided Jumps of Asset Returns Using a Modified Kalman Filter," Risks, MDPI, vol. 5(1), pages 1-14, March.

  23. Matthias Arnold & Sebastian Stahlberg & Dominik Wied, 2013. "Modeling different kinds of spatial dependence in stock returns," Empirical Economics, Springer, vol. 44(2), pages 761-774, April.

    Cited by:

    1. Matteo Foglia & Eliana Angelini, 2019. "The Time-Spatial Dimension of Eurozone Banking Systemic Risk," Risks, MDPI, vol. 7(3), pages 1-25, July.
    2. Mardi Dungey & Moses Kangogo & Vladimir Volkov, 2022. "Dynamic effects of network exposure on equity markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(4), pages 569-629, December.
    3. Hüttner, Amelie & Scherer, Matthias & Gräler, Benedikt, 2020. "Geostatistical modeling of dependent credit spreads: Estimation of large covariance matrices and imputation of missing data," Journal of Banking & Finance, Elsevier, vol. 118(C).
    4. Rusmanto, Toto & Soedarmono, Wahyoe & Tarazi, Amine, 2020. "Credit information sharing in the nexus between charter value and systemic risk in Asian banking," Research in International Business and Finance, Elsevier, vol. 53(C).
    5. Tam, Pui Sun, 2014. "A spatial–temporal analysis of East Asian equity market linkages," Journal of Comparative Economics, Elsevier, vol. 42(2), pages 304-327.
    6. Capasso Salvatore & D’Uva Marcella, & Fiorelli Cristiana & Napolitano Oreste, 2022. "Assessing the Impact of Country-Specific Sovereign Risk on Financial and Banking System in EMU: the Role of Italy," CSEF Working Papers 654, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    7. Asgharian, Hossein & Hess, Wolfgang & Liu, Lu, 2013. "A spatial analysis of international stock market linkages," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4738-4754.
    8. Guoli Mo & Chunzhi Tan & Weiguo Zhang & Xuezeng Yu, 2023. "Dynamic spatiotemporal correlation coefficient based on adaptive weight," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-43, December.
    9. Tobias Berens & Dominik Wied & Daniel Ziggel, 2014. "Automated Portfolio Optimization Based on a New Test for Structural Breaks," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 2(2), pages 243-264, April.
    10. Yun Feng & Xin Li, 2022. "The Cross-Shareholding Network and Risk Contagion from Stochastic Shocks: An Investigation Based on China’s Market," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 357-381, January.
    11. Capasso, Salvatore & D'Uva, Marcella & Fiorelli, Cristiana & Napolitano, Oreste, 2023. "Cross-border Italian sovereign risk transmission in EMU countries," Economic Modelling, Elsevier, vol. 126(C).
    12. Syed Mujahid Hussain & Amjad Naveed & Sheraz Ahmed & Nisar Ahmad, 2022. "Disaggregating the impact of oil prices on European industrial equity indices: a spatial econometric analysis," Empirical Economics, Springer, vol. 62(6), pages 2673-2692, June.
    13. Francisco Blasques & Siem Jan Koopman & Andre Lucas & Julia Schaumburg, 2014. "Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models," Tinbergen Institute Discussion Papers 14-107/III, Tinbergen Institute.
    14. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
    15. Heil, Thomas L.A. & Peter, Franziska J. & Prange, Philipp, 2022. "Measuring 25 years of global equity market co-movement using a time-varying spatial model," Journal of International Money and Finance, Elsevier, vol. 128(C).
    16. Cristiana Fiorelli & Alfredo Cartone & Matteo Foglia, 2021. "Shadow rates and spillovers across the Eurozone: a spatial dynamic panel model," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(1), pages 223-245, February.
    17. Ou Bianling & Zhao Xin & Wang Mingxi, 2015. "Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices," Journal of Systems Science and Information, De Gruyter, vol. 3(5), pages 463-471, October.
    18. Thilo A. Schmitt & Rudi Schäfer & Dominik Wied & Thomas Guhr, 2016. "Spatial dependence in stock returns: local normalization and VaR forecasts," Empirical Economics, Springer, vol. 50(3), pages 1091-1109, May.
    19. Karatetskaya Efrosiniya & Lakshina Valeriya, 2018. "Volatility Spillovers With Spatial Effects On The Oil And Gas Market," HSE Working papers WP BRP 72/FE/2018, National Research University Higher School of Economics.
    20. Yun Feng & Xin Li, 2021. "Does cross-shareholding lead to China's stock returns comovement? Evidence from a GMM-based spatial AR model," Empirical Economics, Springer, vol. 61(6), pages 3213-3237, December.
    21. Ding, Dong & Sickles, Robin C., 2018. "Capital Regulation, Efficiency, and Risk Taking: A Spatial Panel Analysis of U.S. Banks," Working Papers 18-004, Rice University, Department of Economics.
    22. Leopoldo Catania & Anna Gloria Bill'e, 2016. "Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," Papers 1602.02542, arXiv.org, revised Jan 2023.
    23. Kangogo, Moses & Volkov, Vladimir, 2021. "Dynamic effects of network exposure on equity markets," Working Papers 2021-03, University of Tasmania, Tasmanian School of Business and Economics.
    24. F. Blasques & P. Gorgi & S. J. Koopman & J. Sampi, 2023. "Does trade integration imply growth in Latin America? Evidence from a dynamic spatial spillover model," Tinbergen Institute Discussion Papers 23-007/IVI, Tinbergen Institute.
    25. Wahyoe Soedarmono & Romora Edward Sitorus & Amine Tarazi, 2016. "Bank Charter Value, Systemic Risk and Credit Reporting Systems: Evidence from the Asia-Pacific Region," Working Papers hal-01284976, HAL.

  24. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
    See citations under working paper version above.
  25. Dominik Wied, 2013. "CUSUM-type testing for changing parameters in a spatial autoregressive model for stock returns," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(2), pages 221-229, March.

    Cited by:

    1. Guoli Mo & Chunzhi Tan & Weiguo Zhang & Xuezeng Yu, 2023. "Dynamic spatiotemporal correlation coefficient based on adaptive weight," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-43, December.
    2. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    3. Francisco Blasques & Siem Jan Koopman & Andre Lucas & Julia Schaumburg, 2014. "Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models," Tinbergen Institute Discussion Papers 14-107/III, Tinbergen Institute.
    4. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
    5. Heil, Thomas L.A. & Peter, Franziska J. & Prange, Philipp, 2022. "Measuring 25 years of global equity market co-movement using a time-varying spatial model," Journal of International Money and Finance, Elsevier, vol. 128(C).
    6. Ou Bianling & Zhao Xin & Wang Mingxi, 2015. "Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices," Journal of Systems Science and Information, De Gruyter, vol. 3(5), pages 463-471, October.
    7. Aparna Sengupta, 2017. "Testing for a Structural Break in a Spatial Panel Model," Econometrics, MDPI, vol. 5(1), pages 1-17, March.
    8. Thilo A. Schmitt & Rudi Schäfer & Dominik Wied & Thomas Guhr, 2016. "Spatial dependence in stock returns: local normalization and VaR forecasts," Empirical Economics, Springer, vol. 50(3), pages 1091-1109, May.
    9. Holger Dette & Dominik Wied, 2016. "Detecting relevant changes in time series models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 371-394, March.
    10. Manner, Hans & Stark, Florian & Wied, Dominik, 2019. "Testing for structural breaks in factor copula models," Journal of Econometrics, Elsevier, vol. 208(2), pages 324-345.
    11. F. Blasques & P. Gorgi & S. J. Koopman & J. Sampi, 2023. "Does trade integration imply growth in Latin America? Evidence from a dynamic spatial spillover model," Tinbergen Institute Discussion Papers 23-007/IVI, Tinbergen Institute.

  26. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2013. "Über die Anwendbarkeit eines neuen Fluktuationstests für Korrelationen auf Finanzzeitreihen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 87-103, March.

    Cited by:

    1. Ralf Thomas Münnich, 2014. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(3), pages 89-90, September.
    2. Tobias Berens & Dominik Wied & Daniel Ziggel, 2014. "Automated Portfolio Optimization Based on a New Test for Structural Breaks," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 2(2), pages 243-264, April.
    3. Robert Garthoff, 2014. "Sequentielle Überwachung von Finanzzeitreihen anhand von Residuenkarten," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(3), pages 91-113, September.
    4. Ralf Münnich, 2013. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 83-85, March.

  27. Dominik Wied & Daniel Ziggel & Tobias Berens, 2013. "On the application of new tests for structural changes on global minimum-variance portfolios," Statistical Papers, Springer, vol. 54(4), pages 955-975, November.

    Cited by:

    1. Josua Gösmann & Daniel Ziggel, 2018. "An innovative risk management methodology for trading equity indices based on change points," Journal of Asset Management, Palgrave Macmillan, vol. 19(2), pages 99-109, March.
    2. Julia Reynolds & Leopold Sögner & Martin Wagner, 2021. "Deviations from Triangular Arbitrage Parity in Foreign Exchange and Bitcoin Markets," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(2), pages 105-146, June.
    3. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    4. Tobias Berens & Dominik Wied & Daniel Ziggel, 2014. "Automated Portfolio Optimization Based on a New Test for Structural Breaks," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 2(2), pages 243-264, April.
    5. Li, Hua & Bai, Zhi Dong & Wong, Wing Keung, 2015. "High dimensional Global Minimum Variance Portfolio," MPRA Paper 66284, University Library of Munich, Germany.
    6. Sylvia Gottschalk, 2023. "From Black Wednesday to Brexit: Macroeconomic shocks and correlations of equity returns in France, Germany, Italy, Spain, and the United Kingdom," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2843-2873, July.
    7. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    8. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    9. Górecki, Tomasz & Horváth, Lajos & Kokoszka, Piotr, 2018. "Change point detection in heteroscedastic time series," Econometrics and Statistics, Elsevier, vol. 7(C), pages 63-88.
    10. Ruili Sun & Tiefeng Ma & Shuangzhe Liu, 2020. "Portfolio selection: shrinking the time-varying inverse conditional covariance matrix," Statistical Papers, Springer, vol. 61(6), pages 2583-2604, December.

  28. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2012. "A new fluctuation test for constant variances with applications to finance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1111-1127, November.

    Cited by:

    1. Josua Gösmann & Daniel Ziggel, 2018. "An innovative risk management methodology for trading equity indices based on change points," Journal of Asset Management, Palgrave Macmillan, vol. 19(2), pages 99-109, March.
    2. Georgios Bampinas & Theodore Panagiotidis & Panagiotis Politsidis, 2023. "Sovereign bond and CDS market contagion: A story from the Eurozone crisis," Post-Print hal-04164277, HAL.
    3. Julia Reynolds & Leopold Sögner & Martin Wagner, 2021. "Deviations from Triangular Arbitrage Parity in Foreign Exchange and Bitcoin Markets," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(2), pages 105-146, June.
    4. Yannick Hoga, 2022. "Quantifying the data-dredging bias in structural break tests," Statistical Papers, Springer, vol. 63(1), pages 143-155, February.
    5. Nicolai Bissantz & Daniel Ziggel & Kathrin Bissantz, 2011. "An Empirical Study of Correlation and Volatility Changes of Stock Indices and their Impact on Risk Figures," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 4(4), pages 127-141, August.
    6. Tobias Berens & Dominik Wied & Daniel Ziggel, 2014. "Automated Portfolio Optimization Based on a New Test for Structural Breaks," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 2(2), pages 243-264, April.
    7. Peter N. Posch & Daniel Ullmann & Dominik Wied, 2019. "Detecting structural changes in large portfolios," Empirical Economics, Springer, vol. 56(4), pages 1341-1357, April.
    8. Bekierman Jeremias & Gribisch Bastian, 2016. "Estimating stochastic volatility models using realized measures," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 279-300, June.
    9. Duan, Fang, 2022. "Forecasting risk measures based on structural breaks in the correlation matrix," Ruhr Economic Papers 945, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    10. Sylvia Gottschalk, 2023. "From Black Wednesday to Brexit: Macroeconomic shocks and correlations of equity returns in France, Germany, Italy, Spain, and the United Kingdom," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2843-2873, July.
    11. Ziggel, Daniel & Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2014. "A new set of improved Value-at-Risk backtests," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 29-41.
    12. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2020. "Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis," JRFM, MDPI, vol. 13(10), pages 1-18, October.
    13. Max Wornowizki & Roland Fried & Simos G. Meintanis, 2017. "Fourier methods for analyzing piecewise constant volatilities," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(3), pages 289-308, July.
    14. Hood, Matthew & Malik, Farooq, 2018. "Estimating downside risk in stock returns under structural breaks," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 102-112.
    15. Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.

  29. Dominik Wied & Rafael Weißbach, 2012. "Consistency of the kernel density estimator: a survey," Statistical Papers, Springer, vol. 53(1), pages 1-21, February.

    Cited by:

    1. David Atienza & Pedro Larrañaga & Concha Bielza, 2022. "Rejoinder on: Hybrid semiparametric Bayesian networks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 344-347, June.
    2. Ouafae Benrabah & Elias Ould Saïd & Abdelkader Tatachak, 2015. "A kernel mode estimate under random left truncation and time series model: asymptotic normality," Statistical Papers, Springer, vol. 56(3), pages 887-910, August.
    3. R. Zamini & V. Fakoor & M. Sarmad, 2015. "On estimation of a density function in multiplicative censoring," Statistical Papers, Springer, vol. 56(3), pages 661-676, August.
    4. Geng, Pei, 2022. "Estimation of functional-coefficient autoregressive models with measurement error," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    5. Giovanni Paolo Crespi & Elisa Mastrogiacomo, 2020. "Qualitative robustness of set-valued value-at-risk," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 91(1), pages 25-54, February.
    6. Nassira Menni & Abdelkader Tatachak, 2018. "A note on estimating the conditional expectation under censoring and association: strong uniform consistency," Statistical Papers, Springer, vol. 59(3), pages 1009-1030, September.
    7. Diaa Al Mohamad, 2018. "Towards a better understanding of the dual representation of phi divergences," Statistical Papers, Springer, vol. 59(3), pages 1205-1253, September.
    8. Abhik Ghosh & Ayanendranath Basu, 2017. "The minimum S-divergence estimator under continuous models: the Basu–Lindsay approach," Statistical Papers, Springer, vol. 58(2), pages 341-372, June.
    9. Rafael Weißbach & Wladislaw Poniatowski & Walter Krämer, 2013. "Nearest neighbor hazard estimation with left-truncated duration data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(1), pages 33-47, January.
    10. Romain Azaïs & Alexandre Genadot, 2015. "Semi-parametric inference for the absorption features of a growth-fragmentation model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 341-360, June.
    11. Wang, Xinchang, 2016. "Stochastic resource allocation for containerized cargo transportation networks when capacities are uncertain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 334-357.
    12. Tepegjozova Marija & Zhou Jing & Claeskens Gerda & Czado Claudia, 2022. "Nonparametric C- and D-vine-based quantile regression," Dependence Modeling, De Gruyter, vol. 10(1), pages 1-21, January.
    13. Zhang, Archer Gong & Chen, Jiahua, 2022. "Density ratio model with data-adaptive basis function," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    14. Dutta, Santanu & Goswami, Alok, 2013. "Pointwise and uniform convergence of kernel density estimators using random bandwidths," Statistics & Probability Letters, Elsevier, vol. 83(12), pages 2711-2720.

  30. Wied, Dominik & Krämer, Walter & Dehling, Herold, 2012. "Testing For A Change In Correlation At An Unknown Point In Time Using An Extended Functional Delta Method," Econometric Theory, Cambridge University Press, vol. 28(3), pages 570-589, June.

    Cited by:

    1. Josua Gösmann & Daniel Ziggel, 2018. "An innovative risk management methodology for trading equity indices based on change points," Journal of Asset Management, Palgrave Macmillan, vol. 19(2), pages 99-109, March.
    2. Georgios Bampinas & Theodore Panagiotidis & Panagiotis Politsidis, 2023. "Sovereign bond and CDS market contagion: A story from the Eurozone crisis," Post-Print hal-04164277, HAL.
    3. Adams, Zeno & Füss, Roland & Glück, Thorsten, 2017. "Are correlations constant? Empirical and theoretical results on popular correlation models in finance," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 9-24.
    4. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2022. "Change point analysis of covariance functions: A weighted cumulative sum approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    5. Philip Bertram & Robinson Kruse & Philipp Sibbertsen, 2013. "Fractional integration versus level shifts: the case of realized asset correlations," Statistical Papers, Springer, vol. 54(4), pages 977-991, November.
    6. Wied, Dominik & Dehling, Herold & van Kampen, Maarten & Vogel, Daniel, 2014. "A fluctuation test for constant Spearman’s rho with nuisance-free limit distribution," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 723-736.
    7. Packham, Natalie & Woebbeking, Fabian, 2021. "Correlation scenarios and correlation stress testing," IRTG 1792 Discussion Papers 2021-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Adams, Zeno & Glück, Thorsten, 2015. "Financialization in commodity markets: A passing trend or the new normal?," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 93-111.
    9. Yannick Hoga, 2022. "Quantifying the data-dredging bias in structural break tests," Statistical Papers, Springer, vol. 63(1), pages 143-155, February.
    10. Fabian Woebbeking, 2021. "Cryptocurrency volatility markets," Digital Finance, Springer, vol. 3(3), pages 273-298, December.
    11. Y Hoga, 2018. "A structural break test for extremal dependence in β-mixing random vectors," Biometrika, Biometrika Trust, vol. 105(3), pages 627-643.
    12. Packham, N. & Woebbeking, C.F., 2019. "A factor-model approach for correlation scenarios and correlation stress testing," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 92-103.
    13. Chibane, Messaoud & Gabriel, Amadeus & Giménez Roche, Gabriel A., 2022. "Credit booms and crisis-emergent asset comovement: The problem of latent correlation," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 270-279.
    14. Nicolai Bissantz & Daniel Ziggel & Kathrin Bissantz, 2011. "An Empirical Study of Correlation and Volatility Changes of Stock Indices and their Impact on Risk Figures," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 4(4), pages 127-141, August.
    15. Horváth, Lajos & Reeder, Ron, 2012. "Detecting changes in functional linear models," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 310-334.
    16. Nasri, Bouchra R. & Rémillard, Bruno N. & Bahraoui, Tarik, 2022. "Change-point problems for multivariate time series using pseudo-observations," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    17. Marco Barassi & Lajos Horváth & Yuqian Zhao, 2020. "Change‐Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 340-349, April.
    18. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    19. Adams, Zeno & Glück, Thorsten, 2013. "Financialization in Commodity Markets: Disentangling the Crisis from the Style Effect," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79949, Verein für Socialpolitik / German Economic Association.
    20. Tobias Berens & Dominik Wied & Daniel Ziggel, 2014. "Automated Portfolio Optimization Based on a New Test for Structural Breaks," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 2(2), pages 243-264, April.
    21. Alexander Schnurr & Herold Dehling, 2017. "Testing for Structural Breaks via Ordinal Pattern Dependence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 706-720, April.
    22. Packham, N. & Woebbeking, F., 2023. "Correlation scenarios and correlation stress testing," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 55-67.
    23. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.
    24. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2013. "Über die Anwendbarkeit eines neuen Fluktuationstests für Korrelationen auf Finanzzeitreihen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 87-103, March.
    25. N. Packham & F. Woebbeking, 2021. "Correlation scenarios and correlation stress testing," Papers 2107.06839, arXiv.org, revised Sep 2022.
    26. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2012. "A new fluctuation test for constant variances with applications to finance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1111-1127, November.
    27. Dominik Wied, 2022. "Semiparametric Distribution Regression with Instruments and Monotonicity," Papers 2212.03704, arXiv.org.
    28. Alexander Mayer & Dominik Wied & Victor Troster, 2024. "Quantile Granger Causality in the Presence of Instability," Papers 2402.09744, arXiv.org.
    29. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    30. Choi, Ji-Eun & Shin, Dong Wan, 2019. "Moving block bootstrapping for a CUSUM test for correlation change," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 95-106.
    31. Sylvia Gottschalk, 2023. "From Black Wednesday to Brexit: Macroeconomic shocks and correlations of equity returns in France, Germany, Italy, Spain, and the United Kingdom," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2843-2873, July.
    32. Sakurai, Yuji & Kurosaki, Tetsuo, 2023. "Have cryptocurrencies become an inflation hedge after the reopening of the U.S. economy?," Research in International Business and Finance, Elsevier, vol. 65(C).
    33. Choi, Ji-Eun & Shin, Dong Wan, 2020. "A self-normalization test for correlation change," Economics Letters, Elsevier, vol. 193(C).
    34. Likai Chen & Weining Wang & Wei Biao Wu, 2017. "Dynamic Semiparametric Factor Model with a Common Break," SFB 649 Discussion Papers SFB649DP2017-026, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    35. Galeano, Pedro & Wied, Dominik, 2014. "Multiple break detection in the correlation structure of random variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 262-282.
    36. Georgios Bampinas & Theodore Panagiotidis, 2017. "Oil and stock markets before and after financial crises : a local Gaussian correlation approach," Bank of Estonia Working Papers wp2016-11, Bank of Estonia, revised 06 Feb 2017.
    37. Holger Dette & Dominik Wied, 2016. "Detecting relevant changes in time series models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 371-394, March.
    38. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    39. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Cambridge Working Papers in Economics 2367, Faculty of Economics, University of Cambridge.
    40. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    41. Natalie Packham & Fabian Woebbeking, 2018. "A factor-model approach for correlation scenarios and correlation stress-testing," Papers 1807.11381, arXiv.org, revised Jan 2019.
    42. Horváth, Lajos & Rice, Gregory, 2019. "Asymptotics for empirical eigenvalue processes in high-dimensional linear factor models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 138-165.
    43. Sakurai, Yuji, 2021. "How has the relationship between safe haven assets and the US stock market changed after the global financial crisis?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    44. Aslanidis, Nektarios & Martinez, Oscar, 2021. "Correlation regimes in international equity and bond returns," Economic Modelling, Elsevier, vol. 97(C), pages 397-410.
    45. Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.
    46. Adams, Zeno & Glueck, Thorsten, 2014. "Financialization in Commodity Markets: A Passing Trend or the New Normal?," Working Papers on Finance 1413, University of St. Gallen, School of Finance, revised Aug 2015.
    47. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2021. "A tensor-based unified approach for clustering coefficients in financial multiplex networks," Papers 2105.14325, arXiv.org, revised Apr 2022.
    48. Zifeng Zhao & Feiyu Jiang & Xiaofeng Shao, 2022. "Segmenting time series via self‐normalisation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1699-1725, November.
    49. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.
    50. Bucher, Axel & Jaschke, Stefan & Wied, Dominik, 2013. "Nonparametric tests for constant tail dependence with an application to energy and finance," LIDAM Discussion Papers ISBA 2013033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    51. Górecki, Tomasz & Horváth, Lajos & Kokoszka, Piotr, 2018. "Change point detection in heteroscedastic time series," Econometrics and Statistics, Elsevier, vol. 7(C), pages 63-88.

  31. Arnold, Matthias & Wied, Dominik, 2010. "Improved GMM estimation of the spatial autoregressive error model," Economics Letters, Elsevier, vol. 108(1), pages 65-68, July.

    Cited by:

    1. Doğan, Osman & Taşpınar, Süleyman, 2013. "GMM estimation of spatial autoregressive models with moving average disturbances," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 903-926.
    2. Matthias Arnold & Dominik Wied, 2014. "Improved GMM estimation of random effects panel data models with spatially correlated error components," Papers in Regional Science, Wiley Blackwell, vol. 93(1), pages 77-99, March.
    3. Jцrg Breitung & Christoph Wigger, 2017. "Alternative GMM estimators for spatial regression models," Working Paper Series in Economics 89, University of Cologne, Department of Economics.
    4. Osman Dogan, 2013. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term," Working Papers 2, City University of New York Graduate Center, Ph.D. Program in Economics.
    5. Osman Doğan, 2015. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with a Moving Average Disturbance Term," Econometrics, MDPI, vol. 3(1), pages 1-27, February.
    6. Baltagi, Badi H. & Liu, Long, 2011. "An improved generalized moments estimator for a spatial moving average error model," Economics Letters, Elsevier, vol. 113(3), pages 282-284.

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