Multivariate DCC-GARCH output table
| Model component | Parameter | Coefficient | Std. err. | t-value |
|---|---|---|---|---|
| Joint | Dcca1 | 0.009 | 0.007 | 1,33 |
| Joint | Dccb1 | 0.688*** | 0.065 | 10.6 |
| No. observations | 1,442 | |||
| Log-likelihood | 54,138.92 | |||
| Av. Log-likelihood | 37.54 | |||
| Model component | Parameter | Coefficient | Std. err. | t-value |
|---|---|---|---|---|
| Joint | Dcca1 | 0.009 | 0.007 | 1,33 |
| Joint | Dccb1 | 0.688 | 0.065 | 10.6 |
| No. observations | 1,442 | |||
| Log-likelihood | 54,138.92 | |||
| Av. Log-likelihood | 37.54 | |||
Notes:
Multivariate DCC-GARCH model output summarizing the dcca1 and dccb1 coefficients of digital and conventional assets considered in evaluating conditional volatility during the Terra Luna crash, represented by a sample period from January 1st through May 31st, 2022. The dcca1 coefficient suggests relatively independent dynamics among the assets over time, while the dccb1 coefficient highlights a significant and persistent relationship. The significant dccb1 coefficient underscores the model’s robustness, indicating validity for conclusions drawn from the model, which provide valuable insights for risk management and portfolio diversification. The test for normal distribution was performed and showed no abnormalities. The symbols ***, ** and * denote statistical significance of t-tests at the 1, 5 and 10% level, respectively