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Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| DCC-MIDAS× | Component GARCH× | Kvantil-VAR× | |
|---|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 2013 | 1999 | 2006 |
| Upphovsperson≠ | Engle, Ghysels, and Sohn | Engle and Lee | Koenker and Xiao |
| Typ≠ | Time-varying correlation model | Decomposed variance model | Distribution impulse response |
| Ursprungskälla≠ | Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797. DOI ↗ | Engle, R. F., & Lee, G. (1999). A permanent and transitory component model of stock return volatility. Journal of Political Economy, 107(6), 1363-1384. link ↗ | Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗ |
| Alias | DCC mixed-frequency model | Volatility components model | Quantile-based impulse response |
| Närliggande | 3 | 3 | 3 |
| Sammanfattning≠ | 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. | Component GARCH decomposes conditional variance into transitory (short-term) and permanent (long-term) components with different dynamics, allowing flexibility in capturing volatility behavior at multiple frequencies. Introduced by Engle and Lee (1999), it elegantly models the empirical finding that volatility exhibits both rapid mean-reversion (daily shocks) and slow mean-reversion (level shifts). This framework is crucial for understanding volatility persistence and improving long-horizon volatility forecasting. | Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behavior and contagion effects invisible to mean-based VAR analysis. This is essential for risk management and understanding how crises propagate differently than normal times. |
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