Articles in journals

• Seifert, M. I. (2024). Characterization of valid auxiliary functions for representations of extreme value distributions and their max-domains of attraction. Scandinavian Journal of Statistics, 51(2): 832–860.
• Golosnoy, V., Hildebrandt, B., Köhler, S., Schmid, W., Seifert, M. I. (2023). Control charts for measurement error models. AStA Advances in Statistical Analysis 107(4): 693-712.
• Vogler, J., Golosnoy, V. (2023). Unrestricted Maximum Likelihood Estimation of Multivariate Realized Volatility Models. European Journal of Operational Research 304(3): 1063-1074, [WP]
• Dette, H., Golosnoy, V., Kellermann, J. (2023). The effect of intraday periodicity on realized volatility measures. Metrika, 86(3): 315-342.
• Golosnoy, V., Gribisch, B., Seifert, M. I. (2022). Sample and realized minimum variance portfolios: Estimation, statistical inference, and tests. Wiley Interdisciplinary Reviews: Computational Statistics, 14(5), e1556..
• Dette, H., Golosnoy, V., Kellermann, J. (2022). Correcting intraday periodicity bias in realized volatility measures. Econometrics and Statistics, 23,36-52.
• Golosnoy, V., Gribisch, B. (2022). Modeling and forecasting realized portfolio weights. Journal of Banking & Finance, 138, 106404
• Golosnoy, V., Seifert, M.I. (2021). Monitoring mean changes in persistent multivariate time series. Statistics 55(3): 475–488.
• Golosnoy, V., Köhler, S., Schmid, W., Seifert, M. I. (2021). Testing for parameter changes in linear state space models.Applied Stochastic Models in Business and Industry, 37(6), 1060-1079.
• Demetrescu, M., Golosnoy, V., Titova, A. (2020). Bias corrections for exponentially transformed forecasts: Are they worth the effort? International Journal of Forecasting 36, 761–780.
• Golosnoy, V., Schmid, W., Seifert, M.I., Lazariv, T. (2020). Statistical Inferences for Realized Portfolio Weights. Econometrics and Statistics 14, 49–62. [WP]
• Golosnoy, V., Hildebrandt, B., Köhler, S. (2019). Modeling and forecasting realized portfolio diversification benefits. Journal of Risk and Financial Management 12(3): 116; https://doi.org/10.3390/jrfm12030116.
• Golosnoy, V., Gribisch, B., Seifert, M.I. (2019). Exponential smoothing of realized portfolio weights. Journal of Empirical Finance 53: 222–237.
• Golosnoy, V., Roestel, J. (2019). Real time monitoring of the US inflation expectation process. Macroeconomic Dynamics 23 (6): 2221–2249.
• Golosnoy, V., Rossen, A. (2018). Modeling dynamics of metal price series via state space approach with two common factors. Empirical Economics 54(4): 1477–1501.
• Golosnoy, V. (2018). Sequential Monitoring of Portfolio Betas. Statistical Papers 59(2), 663–684.
• Golosnoy, V., Parolya, N. (2017). Portfolio Choice for Cooperating Mean-Variance Savers. Quantitative Finance 15(6), 1055–1073.
• Liesenfeld, R., Richard, J.-F., Vogler, J. (2017). Likelihood-Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes. Journal of Applied Econometrics 32(3), 600–620.
• Aßmann, C., Boysen-Hogrefe, J., Pape, M. (2016). Bayesian Analysis of Static and Dynamic Factor Models: An Ex-Post Approach towards the Rotation Problem. Journal of Econometrics 192, 190–206.
• Liesenfeld, R., Richard, J.-F., Vogler, J. (2016). Likelihood evaluation of high-dimensional spatial latent Gaussian models with non-Gaussian response variables. Advances in Econometrics 37, 35–77.
• Mozharovskyi, P., Vogler, J. (2016). Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples. Economics Letters 148, 87–90.
• Golosnoy, V., Gribisch, B., Liesenfeld, R. (2015). Intra-Daily Volatility Spillovers in International Stock Markets. Journal of International Money and Finance 53: 95–114.
• Golosnoy, V., Okhrin, Y. (2015). Using Information Quality for Volatility Model Combinations. Quantitative Finance 15: 1055–1073.
• Golosnoy, V., Hamid, A., Okhrin, Y. (2014). The Empirical Similarity Approach for Volatility Prediction,
  Journal of Banking & Finance 40, 321--329.
• Garthoff, R., Golosnoy, V., Schmid, W. (2013). Monitoring the Mean of Multivariate Financial Time Series,
  Applied Stochastic Models in Business and Industry, 30, 328-–340.
• Golosnoy, V., Hogrefe, J. (2013). "Signaling NBER Turning Points: A Sequential Approach",
  Journal of Applied Statistics 40, 438-448.(Early WP).
• Golosnoy, V., Herwartz, H. (2012). "Dynamic Modeling of High-Dimensional Correlation Matrices in Finance",
  International Journal of Theoretical & Applied Finance 15, article 1250035, 22 pages. (Early WP).
• Golosnoy, V., Gribisch, B., Liesenfeld, R. (2012).
  "The Conditional Autoregressive Wishart Model for Multivariate Stock Market Volatility",
  Journal of Econometrics 167, 211-223. (link, SSRN)
• Golosnoy, V., Okhrin, I., Schmid, W. (2012). "Statistical Surveillance of Volatility Forecasting Models",
  Journal of Financial Econometrics 10, 513-543. (link, SSRN).
• Golosnoy, V., Ragulin, S., Schmid, W. (2011). "CUSUM Control Charts for Monitoring Optimal Portfolio Weights",
  Computational Statistics & Data Analysis 55,2991-3009. (link)
• Golosnoy, V., Okhrin, Y. (2011). "Nonparametric Monitoring of Equal Predictive Ability",
  Journal of Statistical Planning and Inference 141, 3170-3180. (link)
• Boysen-Hogrefe, J., Pape, M. (2011). More Than Just One Labor Market Cycle in Germany? An Analysis of Regional Unemployment Data. Journal of Labor Market Research 44, 279--292.
• Golosnoy, V., Liesenfeld, R. (2011). "Interval Shrinkage Estimators",
  Journal of Applied Statistics 38, 465 - 477.(link, SSRN)
• Golosnoy, V. (2010). "No-Transaction Bounds and Estimation Risk",
  Quantitative Finance 10, 487-493. (link)
• Golosnoy, V., Okhrin, I., Schmid, W. (2010). "New Characteristics for Portfolio Surveillance",
  Statistics: A Journal of Theoretical and Applied Statistics 44, 303-321. (link)
• Golosnoy, V., Ragulin, S., Schmid, W. (2009)."Multivariate CUSUM Chart: Properties and Enhancements",
  AStA Advances in Statistical Analysis 93, 263-279.(link)
• Golosnoy, V., Okhrin, Y. (2009). "Flexible Shrinkage in Portfolio Selection",
  Journal of Economic Dynamics and Control 33, 317-328.(link)
• Golosnoy, V., Okhrin, Y. (2008). "General Uncertainty in Portfolio Selection: A Case-Based Decision Approach",
  Journal of Economic Behavior and Organization 67, 718-734. (link)
• Golosnoy, V. (2007). "Sequential Monitoring of Minimum Variance Portfolio",
  AStA Advances in Statistical Analysis 91, 39-55.(link)
• Golosnoy, V., Okhrin, Y. (2007). "Multivariate Shrinkage for Optimal Portfolio Weights",
  European Journal of Finance 13, 441-458. (link)
• Golosnoy, V., Schmid, W. (2007). "EWMA Control Charts for Monitoring Optimal Portfolio Weights",
  Sequential Analysis 26, 195-224. (link)

Articles in readers

• Golosnoy, V., Okhrin, I., Ragulin, S., Schmid, W. (2010). "On the Application of SPC in Finance",
  in Lenz, H.-J., Wilrich, P.-T., Schmid, W. (Eds.) Frontiers in Statistical Quality Control 9, 119-130. (link)
• Golosnoy, V., Schmid, W. (2009). "Statistical Process Control in Asset Management",
  in W. Härdle, N. Hautsch, L. Overbeck (editors) Applied Quantitative Finance, Springer, 2nd edition, 399-416. (link)
• Golosnoy, V., Schmid, W., Okhrin, I. (2007). "Sequential Monitoring of Optimal Portfolio Weights",
  in M. Frisén (editor) Financial Surveillance, Wiley, 179-211. (link)

Proceedings

• Golosnoy, V., Okhrin, I., Schmid, W. (2011). Monitoring Validity of Daily Volatility Model,
  Proceedings of the ISI World Statistics Congress, 58th Session in Dublin 2011, Link.
• Golosnoy, V., Okhrin, I., Schmid, W. (2009). Modeling and Forecasting Realized Volatility via State-Space Representation,
  9th Workshop on Stochastic Models and Their Applications, RWTH Aachen. (Proceedings, pp. 32-33.)
• Golosnoy, V., Okhrin, I., Schmid, W. (2007). Surveillance of the Minimum Variance Portfolio Composition,
  Bulletin of the International Statistical Institute (ISI), 56th Session in Lisbon 2007, (Proceedings, pp. 2029-2032.)