Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Wavelet-analyse van financiële tijdreeksen× | Markov-Regime-Schakelmodel voor Financiële Reeksen× | |
|---|---|---|
| Vakgebied | Financiering | Financiering |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 2001 | 1989 |
| Grondlegger≠ | Gençay, Selçuk & Whitcher; Aguiar-Conraria & Soares | James D. Hamilton |
| Type≠ | Time-frequency decomposition | Markov regime-switching time-series model |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen≠ | 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 |
| Verwant | 1 | 1 |
| Samenvatting≠ | 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. |
| ScholarGateGegevensset ↗ |
|
|