Q1. Why do the authors use the model by Lee and Yang?
The authors use the model by Lee and Yang (2014) to examine the dependence between the GFSI and RBC using a parametric copula because the linear Granger causality test cannot model the asymmetric dependence between the GFSI and RBC, possibly because of the existence of nonlinearity and structural breaks.
Q2. What is the definition of a distributed ledger?
Blockchain is a distributed ledger made of an unchangeable chain of data blocks spread across multiple sites but chained together cryptographically.
Q3. What is the significance of the cross quantilograms?
The cross-quantilogram p ̂_α (k) for α1 = 0.7, 0.8, 0.9, and 0.95 is negative and significant for most lags, indicating that when GSFI is very low, it is less likely to have a very large positive gain for Bitcoin.
Q4. What is the way to model GCQ?
The small p-values of the reality check signal the rejection of the null hypothesis, indicating that there is a copula function to model GCQ and produce a better quantile forecast of the RBC by conditioning on the GSFI.
Q5. What is the directional predictability test used by Han et al.?
The directional predictability test of Han et al. (2016) was used by Jiang et al. (2016) to investigate the daily, overnight, intraday, and rolling return spillovers of four key agricultural commodities—soybeans, wheat, corn, and sugar— between the U.S. and Chinese futures markets.
Q6. What is the model for the quantile forecasting of the RBC?
The authors can observe that a quantile forecasting model with no Granger causality in the quantile is rejected in many quantiles, except for the quantile at 40%, 50%, and 60% with evidence at 1 percent significance level.
Q7. What is the probability of having negative losses to Bitcoin for a maximum of 50-60 days?
This finding implies that when financial stress is higher than the 0.9 quantile, there is an increased likelihood of having very large negative losses to Bitcoin for a maximum of 50-60 days.
Q8. What is the importance of the copula structure?
the increased knowledge of which risks are essential and against which to hedge them in the different quantiles whilst explaining the copula dependence structure are two crucial aspects of successful investing.
Q9. What is the main difference between copulas and the Sklar theorem?
copulas are invariant to increasing and continuous transformations (Ning, 2010), such as the scaling of logarithm returns, which is commonly used in economic and finance studies.
Q10. What is the expected check loss for a quantile forecast?
For each copula distribution function Ck(u; v), the authors also denote the corresponding quantile forecast as 𝑞𝛼,𝑘(𝑌𝑡|ℱ𝑡) and its expected check loss as Qk(α).