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。
| 金融时间序列的小波分析× | 金融序列的马尔可夫状态转换模型× | |
|---|---|---|
| 领域 | 金融学 | 金融学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2001 | 1989 |
| 提出者≠ | Gençay, Selçuk & Whitcher; Aguiar-Conraria & Soares | James D. Hamilton |
| 类型≠ | Time-frequency decomposition | Markov regime-switching time-series model |
| 开创性文献≠ | Gençay, R., Selçuk, F. & Whitcher, B. (2001). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press. DOI ↗ | Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. DOI ↗ |
| 别名≠ | wavelet coherence, continuous wavelet transform, time-frequency analysis, Dalgacık (Wavelet) Finansal Analiz | Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model |
| 相关 | 1 | 1 |
| 摘要≠ | Wavelet financial analysis decomposes a financial time series into different frequency bands (time scales) so short- and long-term relationships can be studied at the same time. Drawing on the treatments of Gençay, Selçuk and Whitcher (2001) and Aguiar-Conraria and Soares (2014), wavelet coherence then visualises how the relationship between two series shifts across both time and frequency. | The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/bear or high/low volatility). It is the financial application of Hamilton's MS-AR model, where an unobserved Markov state governs which parameter set is active at each point in time. |
| ScholarGate数据集 ↗ |
|
|