Quantile connectedness among real estate investment trusts during COVID-19: evidence from the extreme tails of distributions

Purpose: Given the evolving market integration, this study aims to explore the connectedness of 12 real estate investment trusts (REITs) during the COVID-19 period. Design/methodology/approach: The connectedness of 12 REITs was examined by considering three sample periods: full period, COVID peak period and COVID recovery period by using the quantile vector autoregressive (VAR) approach. Findings: The findings ascertain that REIT markets are sensitive to COVID, revealing significant connectedness during each sample period. The USA and The Netherlands are the major shock transmitters;thus, these countries are relatively better options for the predictive behavior of the rest of the REIT markets. In contrast, Hong Kong and Japan are the least favorable REIT markets with higher shock-receiving potential. Research limitations/implications: The study recommends implications for real estate industry agents and investors to evaluate and anticipate the direction of return connectedness at each phase of the pandemic, such that they can incorporate those global REITs less vulnerable to unplanned crises. Apart from these implications, the study is limited to the global REIT markets and only focused on the period of COVID-19, excluding the concept of other financial and health crises. Originality/value: This study uses a novel approach of the quantile-based VAR to determine the connectedness among REITs. Furthermore, the present work is a pioneer study because it is targeting different time periods of the pandemic. Additionally, the outcomes of the study are valuable for investors, policymakers and portfolio managers to formulate future development strategies and consolidate REITs during the period of crisis. © 2023, Emerald Publishing Limited.

A Fitness Education and Scoring System Based on 3D Human Body Reconstruction

Affected by the Covid-19 epidemic, online fitness education has attracted a large number of users. However, when there are a large number of students in a same online classroom, it is difficult to get the coach’s advice and scores in time. To overcome this problem, we propose an AI fitness education system that uses 3D reconstruction technology to restore the shape of the human body and its bones. The skeleton is used for posture scoring. The 3D human body model is reconstructed by our improved VIBE network, with the accurate posture, shape and movement of the coach and students. By adding the loss function of the end limbs to the 3D human body model, compared with the performance of the original VIBE, we reduce the jitter noise in continuous motion. The training results show the accuracy of our improved VIBE. In addition, we have also established a scoring system, which can score the posture of the trainees based on the coach’s posture, and provide feedback through visual tag points. The experimental results show that our method is feasible and worthy of further exploration. © 2021, Springer Nature Switzerland AG.