China Real Estate Price and Stock Market Volatility during COVID-19

Abstract

Purpose: This study examined the response of stock prices on the China Stock Exchange (SSE) and Real Estate prices to COVID-19 using an event study approach and the GARCH model. Methodology: In this study, the dimensions and key components of the use of large data obtained from the Internet of Things (IoT) in an industry's supply chain are investigated as a case study. Finally, a model for implementing an agile and lean supply chain based on IoT data analysis to improve the supply chain performance of these industries during emergency drug distribution during critical conditions is presented. Findings: We measure volatility spillovers by defining the volatility of each sector in the SSE index. In this study, we investigate the volatility of China stock market. Furthermore, we analyze the dynamic connectedness during COVID-19 pandemic periods to identify the changes in their relationship following the two categories. These empirical findings have several important implications for portfolio managers, policymakers, and investors. Originality/Value: This paper focuses on investigating the impacts of the novel coronavirus (COVID-19) on the China stock market volatility from a GHARCH and VAR model point of view. The GHARCH model used proves that during the COVID-19 pandemic, stock price volatility and real estate price volatility increases and lead to a decrease in abnormal returns. The empirical findings also validate the efficient market hypothesis theory related to the study of events and the theory of financial behavior related to uncertainty.