However, the coherence is found to fluctuate at higher frequencies and be significantly stable at lower frequencies. The results of wavelet coherence confirm the short-run and long-run market integration among some cryptocurrency pairs. The cross wavelet transforms demonstrate Ripple and Ethereum to be trivial origins of market contagion. The results provide evidence of high levels of dependency from 2016 to 2018 at daily frequency scales. In this paper, we investigate the dynamics of multiscale interdependencies among five leading and liquid cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash) using wavelet-based analyses that account for the heterogeneous behaviour of crypto-traders and crypto-investors. The extreme price swings and complexity in cryptocurrency markets drives multifarious research into co-movements, in both time and frequency, among cryptocurrencies. When there is a tail dependence, the use of upper and lower tail dependence provides a better forecast instead of the single correlation coefficient. In any dependence measure, generally, stronger and negative dependence gives a higher forecast. Furthermore, the stronger the dependence of a random loss to the target loss, in linear correlation, the larger/smaller the conditional mean/variance. It is found that the estimative VaR forecast is more accurate when a copula is employed. In this paper, the quantile-based estimative VaR forecast for dependent random losses is explored through a simulation approach. In practice, we have an estimative VaR forecast in which the distribution parameter vector is replaced by its estimator. In both cases, the VaR forecast is obtained by employing its (conditional) probability distribution of loss data, specifically the quantile of loss distribution. The simulated and real studies show that the model is sufficiently good to find the most appropriate truncation level with a good fit of a given data.Ī Value-at-Risk (VaR) forecast may be calculated for the case of a random loss alone and/or of a random loss that depends on another random loss. This newly proposed truncation method is evaluated in simulation studies and a real application of financial returns dataset. Inspired by the relationship between copula’s parameters (and the corresponding Kendall’s tau) and the mutual information on the one side and the mutual information and copula entropy on another side, this study proposed a novel truncation vine copula model using only mutual information values among variables. However, they are either time-consuming or model-dependent or require additional computational efforts. Attempts have been existing to reduce the model complexity by searching for a subclass of truncation vine copulas, of which only a limited number of vine trees are estimated. They, however, lose their flexibility with dimensions. This shows that vine copula-based forecasting procedure not only performs better but also provides a well-diversified portfolio.īased on (different) bivariate copulas as simple building blocks to model complex multivariate dependency patterns, vine copulas provide flexible multivariate models. 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. 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. We carry out numerical analysis of cryptocurrencies returns and compute Value-at-Risk (VaR) forecast along with its accuracy assessed through different backtesting methods. The dependence structure is presented through vine copula. The marginal risk model is assumed to follow a heteroscedastic process of GARCH(1,1) model. In this paper, we construct a dependence modeling for financial risks and form a portfolio risk of cryptocurrencies. It is often that we encounter several risks, in practice, instead of single risk. Risk in finance may come from (negative) asset returns whilst payment loss is a typical risk in insurance.
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