References
- Bersimis S, Sgora A, Psarakis S. The application of multivariate statistical process monitoring in non-industrial processes. Qual Technol Quant Manag. 2018;15(4):526–549.
- Leeb H, Pötscher B, Model Selection. In: Andersen TG, Davis RA, Kreiss J-P, et al., editors. Handbook of financial time series. Berlin: Springer; 2009.
- Engle RF, Kelly B. Dynamic equicorrelation. J Bus Econ Stat. 2012;30:212–228.
- Byrne J, Fazio G, Fiess N. Primary commodity prices: co-movement, common factors and fundamentals. J Dev Econ. 2013;101:16–26.
- Clements A, Scott A, Silvennoinen A. On the benefits of equicorrelation for portfolio allocation. J Forecast. 2015;34:507–522.
- Golosnoy V, Gribisch B, Seifert MI. Exponential smoothing of realized portfolio weights. J Empirical Finance. 2019;53:222–237.
- Engle RF. Dynamic conditional correlation – a simple class of multivariate GARCH models. J Bus Econ Stat. 2002;20:339–350.
- Bollerslev T, Patton AJ, Quaedvlieg R. Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions. J Econom. 2018;207:71–91.
- Alwan LC, Roberts HV. Time–series modelling for statistical process control. J Bus Econ Stat. 1988;6:87–95.
- Kramer H, Schmid W. EWMA charts for multivariate time series. Seq Anal. 1997;16:131–154.
- Bodnar O, Schmid W. Surveillance of the mean behaviour of multivariate time series. Stat Neerl. 2007;61:383–406.
- Bodnar O, Schmid W. CUSUM charts for monitoring the mean of a multivariate Gaussian process. J Stat Plan Inference. 2011;141:2055–2070.
- Okhrin Y, Schmid W, Surveillance of univariate and multivariate linear time series. In: Frisén M, editor. Financial surveillance. Chichester (UK): Wiley; 2007. p. 115–152.
- Frisén M, Knoth S. Minimax optimality of CUSUM for an autoregressive model. Stat Neerl. 2012;66:357–379.
- Rosolowski M, Schmid W. EWMA charts for monitoring the mean and the autocovariances of stationary processes. Statistical Papers. 2006;47:595–630.
- Lütkepohl H, Univariate time series analysis. In: Lütkepohl H, Krätzig M, editors. Applied time series econometrics. Cambridge: Cambridge University Press; 2004. p. 8–86.
- Pignatiello J, Runger G. Comparison of multivariate CUSUM charts. J Qual Technol. 1990;22:173–186.
- Golosnoy V, Ragulin S, Schmid W. Multivariate CUSUM chart: properties and enhancements. Adv Statist Anal. 2009;93:263–279.
- Garthoff R, Golosnoy V, Schmid W. Monitoring the mean of multivariate financial time series. Appl Stoch Models Bus Ind. 2014;30:328–340.
- Golosnoy V, Köhler S, Schmid W, Seifert MI. Testing for parameter changes in linear state space models. Appl Stoch Model Bus Ind. Forthcoming. DOI:10.1002/asmb.2636
- Woodall WH, Montgomery DC. Some current directions in the theory and application of statistical process monitoring. J Qual Technol. 2014;46(1):78–94.
- Jiang W, Tsui K-L, Woodall WH. A new SPC monitoring method: the ARMA chart. Technometrics. 2000;42:399–410.
- Lütkepohl H. New introduction to multiple time series analysis. Berlin, Germany: Springer; 2005.
- Golosnoy V, Okhrin I, Schmid W. New characteristics for portfolio surveillance. Statistics. 2010;44:303–321.
- Montgomery DC. Statistical quality control: a modern introduction. 7th ed. New York (NY): Wiley; 2013.
- Ngai H-M, Zhang J. Multivariate cumulative sum control charts based on projection pursuit. Stat Sin. 2001;11:747–766.
- Bodnar O, Schmid W. CUSUM control schemes for monitoring the covariance matrix of multivariate time series. Statistics. 2017;51:722–744.
- Bollerslev T, Patton AJ, Quaedvlieg R. Exploiting the errors: a simple approach for improved volatility forecasting. J Econom. 2016;192:1–18.
- Fan J, Fan Y, Lv J. High dimensional covariance matrix estimation using a factor model. J Econom. 2008;147:186–197.
- Fan J, Liao Y, Mincheva M. High-dimensional covariance matrix estimation in approximate factor models. Ann Stat. 2011;39:3320–3356.
- Ledoit O, Wolf M. Nonlinear shrinkage estimation of large-dimensional covariance matrices. Ann Stat. 2012;40:1024–1060.
- Bodnar T, Gupta AK, Parolya N. On the strong convergence of the optimal linear shrinkage estimator for large dimensional covariance matrix. J Multivar Anal. 2014;132:215–228.
- Bodnar T, Gupta AK, Parolya N. Direct shrinkage estimation of large dimensional precision matrix. J Multivar Anal. 2016;146:223–236.
- Dette H, Wied D. Detecting relevant changes in time series models. J R Stat Soc Ser B. 2016;78:371–394.
- Brockwell PJ, Davis RA. Introduction to time series and forecasting. 2nd ed. New York (NY): Springer; 2002.