Abstract
Risk in finance may come from (negative) asset returns whilst payment loss is a typical risk in insurance. It is often that we encounter several risks, in practice, instead of single risk. In this paper, we construct a dependence modeling for financial risks and form a portfolio risk of cryptocurrencies. The marginal risk model is assumed to follow a heteroscedastic process of GARCH(1,1) model. The dependence structure is presented through vine copula. We carry out numerical analysis of cryptocurrencies returns and compute Value-at-Risk (VaR) forecast along with its accuracy assessed through different backtesting methods. It is found that the VaR forecast of returns, by considering vine copula-based dependence among different returns, has higher forecast accuracy than that of returns under prefect dependence assumption as benchmark. In addition, through vine copula, the aggregate VaR forecast has not only lower value but also higher accuracy than the simple sum of individual VaR forecasts. This shows that vine copula-based forecasting procedure not only performs better but also provides a well-diversified portfolio.
Highlights
In finance and insurance, one of the major and challenging issues is managing quantitative risk, forecasting future risk
We find that the VaR forecasts for returns by assuming vine copula-based dependence have higher forecast accuracy
This is because the coverage probability (CP) for these VaR forecasts are closer to the corresponding confidence levels (CLs) and their actual over expected (AE) ratio are closer to one
Summary
One of the major and challenging issues is managing quantitative risk, forecasting future risk. Risk in finance may come from (negative) asset returns whilst, in insurance, a typical risk is a payment loss. The second topic lies on the fact that dependent risks have a technical problem with regard to finding an exact form of distribution function (cdf) or probability function (pdf). Both topics above eventually bring us to learn and employ a more sophisticated
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