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| Analisi wavelet di serie storiche finanziarie× | Modello Markoviano a Regimi Variabili per Serie Finanziarie× | |
|---|---|---|
| Campo | Finanza | Finanza |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2001 | 1989 |
| Ideatore≠ | Gençay, Selçuk & Whitcher; Aguiar-Conraria & Soares | James D. Hamilton |
| Tipo≠ | Time-frequency decomposition | Markov regime-switching time-series model |
| Fonte seminale≠ | 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 ↗ |
| Alias≠ | 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 |
| Correlati | 1 | 1 |
| Sintesi≠ | 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. |
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