Cryptocurrencies are gaining momentum in investor attention, are about to become a new asset class, and may provide a hedging alternative against the risk of devaluation of fiat currencies following the COVID-19 crisis. In order to provide a thorough understanding of this new asset class, risk indicators need to consider tail risk behaviour and the interdependencies between the cryptocurrencies not only for risk management but also for portfolio optimization. The tail risk network analysis framework proposed in the paper is able to identify individual risk characteristics and capture spillover effect in a network topology. Finally we construct tail event sensitive portfolios and consequently test the performance during an unforeseen COVID-19 pandemic.
This paper develops a new risk meter specifically for China -FRM@China -to detect systemic financial risk as well as tail-event (TE) dependencies among major financial institutions (FIs). Compared with the CBOE FIX VIX, which is currently the most popular financial risk measure, FRM@China has less noise. It also emitted a risk signature much earlier than the CBOE FIX VIX index in the 2020 COVID pandemic. In addition, FRM@China uses a single quantile-lasso regression model to allow both the assessment of risk transfer between different sectors in which FIs operate and the prediction of systemic risk. Because the risk indicator in FRM@China is based on penalization terms, its relationship with macro variables are unknown and non-linear. This paper further expands the existing FRM approach by using Shapley values to identify the dynamic contribution of different macro features in this type of "black box" situation. The results show that short-term interest rates and forward guidance are significant risk drivers. This paper considers the interaction among FIs from mainland China, Hong Kong and Taiwan to provide an enhanced regional tool set for regulators to evaluate financial policy responses. All quantlets are available on quantlet.com.
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