  Value at risk and expected shortfall are increasingly popular tail risk
measures in the financial risk management field. Both academia and financial
institutions are working to improve tail risk forecasts in order to meet the
requirements of the Basel Capital Accord; it states that one purpose of risk
management and measuring risk accuracy is, since extreme movements cannot
always be avoided, financial institutions can prepare for these extreme returns
by capital allocation, and putting aside the appropriate amount of capital so
as to avoid default in times of extreme price or index movements. Forecast
combination has drawn much attention, as a combined forecast can outperform the
individual forecasts under certain conditions. We propose two methodology, one
is a semiparametric combination framework that can jointly produce combined
value at risk and expected shortfall forecasts, another one is a parametric
regression framework named as Quantile-ES regression that can produce combined
expected shortfall forecasts. The favourability of the semiparametric
combination framework has been presented via an empirical study - application
in cryptocurrency markets with high-frequency data where the necessity of risk
management application increases as the cryptocurrency market becomes more
popular and mature. Additionally, the general framework of the parametric
Quantile-ES regression has been presented via a simulation study, whereas it
still need to be improved in the future. The contributions of this work include
but are not limited to the enabling of the combination of expected shortfall
forecasts and the application of risk management procedures in the
cryptocurrency market with high-frequency data.
