Sammenlign metoder

	Langhukommelsesmodeller (ARFIMA, FIGARCH)	ARIMA (Autoregressive Integrated Moving Average) Modell	Høyfrekvente data og markeds-mikrostruktur analyse
Fagfelt	Finans	Økonometri	Finans
Familie	Regression model	Regression model	Regression model
Opprinnelsesår	1980	2015	2007
Opphavsperson	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)	Box & Jenkins (Box-Jenkins methodology)	Hasbrouck (2007); Aït-Sahalia & Jacod (2014)
Type	Fractionally integrated time series model	Univariate time-series model	Market microstructure / high-frequency econometrics
Opprinnelig kilde	Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649
Alias	ARFIMA, FIGARCH, fractionally integrated models, fractional integration	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı
Relaterte	4	5	5
Sammendrag	Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extended for high-frequency data by Aït-Sahalia and Jacod (2014).