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Value at risk and expected shortfall are increasingly popular tail riskmeasures in the financial risk management field. Both academia and financialinstitutions are working to improve tail risk forecasts in order to meet therequirements of the Basel Capital Accord; it states that one purpose of riskmanagement and measuring risk accuracy is, since extreme movements cannotalways be avoided, financial institutions can prepare for these extreme returnsby capital allocation, and putting aside the appropriate amount of capital soas to avoid default in times of extreme price or index movements. Forecastcombination has drawn much attention, as a combined forecast can outperform theindividual forecasts under certain conditions. We propose two methodology, oneis a semiparametric combination framework that can jointly produce combinedvalue at risk and expected shortfall forecasts, another one is a parametricregression framework named as Quantile-ES regression that can produce combinedexpected shortfall forecasts. The favourability of the semiparametriccombination framework has been presented via an empirical study - applicationin cryptocurrency markets with high-frequency data where the necessity of riskmanagement application increases as the cryptocurrency market becomes morepopular and mature. Additionally, the general framework of the parametricQuantile-ES regression has been presented via a simulation study, whereas itstill need to be improved in the future. The contributions of this work includebut are not limited to the enabling of the combination of expected shortfallforecasts and the application of risk management procedures in thecryptocurrency market with high-frequency data.

Honour Thesis: A Joint Value at Risk and Expected Shortfall Combination Framework and its Applications in the Cryptocurrency Market