方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 马尔可夫开关多重分形模型× | 向量自回归 (VAR)× | |
|---|---|---|
| 领域≠ | 时间序列 | 计量经济学 |
| 方法族≠ | Process / pipeline | Regression model |
| 起源年份≠ | 2004 | 1980 |
| 提出者≠ | Luc E. Calvet | Christopher A. Sims |
| 类型≠ | Stochastic volatility model | Multivariate time-series model |
| 开创性文献≠ | Calvet, L. E., & Fisher, A. J. (2004). How to forecast long-run volatility: regime-switching and the estimation of multifractal processes. Journal of Financial Econometrics, 2(1), 49–83. DOI ↗ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ |
| 别名≠ | MSM, Markov-switching multifractal volatility | VAR, VAR model, vector autoregressive model, multivariate autoregression |
| 相关≠ | 3 | 5 |
| 摘要≠ | The Markov-Switching Multifractal (MSM) model is a flexible framework for capturing time-varying volatility and long-memory effects in financial time series. Developed by Calvet and Fisher (2004), it combines Markov chain theory with multifractal scaling principles to generate volatility that exhibits multiple frequency components, each switching between high and low regimes. This approach is particularly effective for modeling asset returns with realistic fat tails and clustered volatility. | Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance. |
| ScholarGate数据集 ↗ |
|
|