Volatility connectedness across global e-commerce stocks

Abstract

This paper studies the volatility connectedness between the stock prices of e-commerce companies. For that matter, we implement the TVP-VAR-Based connectedness procedure to unearth the dynamic structure of volatilities. This approach helps us to determine the volatility spillover between assets that are risk receivers or risk transmitters. We utilize the daily log-return data of the largest e-commerce companies by market cap. The data set consists of the daily open, close, high, and low prices of stocks between 2019-01-02 and 2022-12-23. We obtain the volatility of stocks using the Garman-Klass range-based approach. The findings reveal that the average total connected index is relatively high by 65.45%, which means that the forecasting error variance in the variables is due to the transmission and connectedness between these variables. Furthermore, net pairwise directional connectedness results evidence that the most dominant stock is Amazon within the network. Ultimately, we find the strongest bilateral volatility interconnectedness to occur between the stocks of Alibaba and Jingdong Mall