Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Tests par cēloņsakarību dispersijā× | DCC-MIDAS (dinamiskā nosacītā korelācija ar jauktām frekvencēm)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1996 | 2013 |
| Autors≠ | Yin-Wong Cheung and Lilian Ng | Engle, Ghysels, and Sohn |
| Tips≠ | Conditional variance test | Time-varying correlation model |
| Pirmavots≠ | Cheung, Y. W., & Ng, L. K. (1996). A causality-in-variance test and its application to financial market prices. Journal of Econometrics, 72(1-2), 33-61. DOI ↗ | Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797. DOI ↗ |
| Citi nosaukumi | Volatility spillover test | DCC mixed-frequency model |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | The causality-in-variance test detects whether shocks to one variable cause changes in the conditional variance (volatility) of another variable, distinct from mean-level causality. Introduced by Cheung and Ng (1996), it identifies volatility spillovers and contagion effects—crucial for risk management and understanding financial market interdependencies. This approach has become standard in studying shock transmission across asset classes and geographies. | DCC-MIDAS combines dynamic conditional correlation (DCC) GARCH with mixed-frequency data sampling (MIDAS), enabling estimation of time-varying correlations between variables when observations arrive at different frequencies. Introduced by Engle et al. (2013), it models how correlations evolve with low-frequency macroeconomic conditions using high-frequency asset price information. This is crucial for portfolio risk management and understanding macro-finance linkages. |
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