Open AccessModeling risk dependence and portfolio VaR forecast through vine copula for cryptocurrencies
Author(s) -
Khreshna Syuhada,
Arief Rachman Hakim
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0242102
Subject(s) - vine copula , copula (linguistics) , econometrics , value at risk , portfolio , economics , cryptocurrency , expected shortfall , autoregressive conditional heteroskedasticity , tail dependence , mathematics , statistics , risk management , computer science , financial economics , multivariate statistics , finance , volatility (finance) , computer security
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.