Methoden vergleichen

	Wavelet-Analyse von Finanzzeitreihen	Markov-Regime-Wechsel-Modell für Finanzreihen
Fachgebiet	Finanzwirtschaft	Finanzwirtschaft
Familie	Regression model	Regression model
Entstehungsjahr	2001	1989
Urheber	Gençay, Selçuk & Whitcher; Aguiar-Conraria & Soares	James D. Hamilton
Typ	Time-frequency decomposition	Markov regime-switching time-series model
Wegweisende Quelle	Gençay, R., Selçuk, F. & Whitcher, B. (2001). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Aliasnamen	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
Verwandt	1	1
Zusammenfassung	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.