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Background Internet penetration worldwide has increased rapidly over the recent years. With this growth, modern information and communication technologies (ICT) have become increasingly important. They do not only change daily life but also patient-physician interaction and health related information search, which can be summarized as electronic Health (eHealth). eHealth was already known before the emergence of the coronavirus disease 2019 (COVID-19), but this pandemic substantially challenged health systems, physicians and hospitals so profoundly that new services and methods of patient-physician interaction had to be implemented rapidly. This study investigates the attitude of cancer patients towards eHealth and the potential impact of COVID-19 on its use. Methods and findings The study was a multicentered study carried out at the university hospitals Bonn and Aachen. Patients were asked to answer a structured questionnaire in the time span between September 2019 and February 2021. Due to the COVID-19 pandemic, no patients were addressed between March 2020 and July 2020. The questionnaire focused on socio-demographic data, the dissemination of internet-enabled devices, the patients’ attitude towards eHealth and the use of modern ICT in daily life and for health-related information search. In total, 280 patients have filled the questionnaire of which 48% were female and 52% were male. Men have a slightly more positive attitude towards the overall potential of eHealth than women which was shown by a significant influence for receiving medical information via e-mail. Hematological-oncological patients with a higher education level reported a significantly higher willingness to send personal health information to their physician and health insurance. A frequency of medical consultation of more than 5 times during the previous year has a significantly positive impact regarding the use of online communication, online video consultation and treatment quality. Younger patients have more concerns about data security than older patients. The study shows a different attitude towards the influence of eHealth on the patient-physician relationship in different therapy situations. While there were no significant changes in patients’ attitude towards eHealth after the start of the COVID-19 pandemic, there was a trend towards an increasingly embracing attitude in patients, who answered the questionnaire during COVID-19 pandemic situation. Conclusions Overall, cancer patients had a positive attitude towards eHealth and the dissemination of internet-enabled devices was high. The study shows that the potential of eHealth is high among hematological-oncological patients. Further eHealth technologies and especially telemedically supported care processes should be implemented to improve patient-physician interaction and cross-sectoral care. COVID-19 pandemic led to a fast initiation and acceleration of new structures and routines for physicians, hospitals and patients. These new processes should be used to promote digitalization in hematological and oncological telemedicine. To successfully implement new eHealth technologies, future research should focus on patients’ concerns about data privacy and data availability especially in the context of exchange of medical information in cross sectoral and interdisciplinary care processes.

We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss function from which we can infer the optimal GMVP weights without imposing any distributional assumptions on the returns. In order to capture time variation in the returns' conditional covariance structure, we model the portfolio weights through a recursive least squares (RLS) scheme as well as by generalized autoregressive score (GAS) type dynamics. Sparse parameterizations combined with targeting towards nonlinear shrinkage estimates of the long-run GMVP weights ensure scalability with respect to the number of assets. An empirical analysis of daily and monthly financial returns shows that the proposed models perform well in-and out-of-sample in comparison to existing approaches.

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