Risk Modeling and Connectedness Across Global and Industrial US Fintech Stock Market: Evidence from the COVID‑19 Crisis

Abstract

The main purpose of this paper is to test the performance of GARCH models in estimating and forecasting VaR (value at risk) of the US Fintech stock market from July 20, 2016, to December 31, 2021. In addition, this study examines the impact of COVID‑19 on the risk spillover between the adequate VaR series of the US global KFTX index and the five Fintech industries. Specifically, we compare different VaR estimates (862 in‑sample daily returns) and predictions (550 out‑of‑sample daily returns) of several GARCH model specifications under a normal and Student‑t distribution with 1% and 5% significance. The Backtesting results indicate that I‑GARCH with Student‑t distribution is a good model for estimating and forecasting VaR of the US Fintech stock market before and during COVID-19. Moreover, the total connectedness results suggest that global and each Fintech industry increases significantly under turbulent market conditions. Given these considerations, this paper provides policymakers and regulators with a better understanding of risk in the Fintech industry without inhibiting innovation.

Keywords

• value at risk
• fintech stock market
• garch model specifications
• covid‑19
• var series
• connectedness