Sammenlign metoder

	Markov-Switching Multifractal Model	GARCH-modell (volatilitetsprognoser)
Fagfelt	Tidsserier	Økonometri
Familie	Process / pipeline	Regression model
Opprinnelsesår	2004	1986
Opphavsperson	Luc E. Calvet	Tim Bollerslev
Type	Stochastic volatility model	Conditional volatility model
Opprinnelig kilde	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.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Alias	MSM, Markov-switching multifractal volatility	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Relaterte	3	5
Sammendrag	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.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.