เปรียบเทียบวิธี

	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)	แบบจำลองโคพูลา (Gaussian, t, Clayton, Gumbel, Frank)	ทฤษฎีค่าสุดขีด (Extreme Value Theory: EVT)
สาขาวิชา	เศรษฐมิติ	การเงิน	การเงิน
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	2015	1959	2001
ผู้ริเริ่ม	Box & Jenkins (Box-Jenkins methodology)	Sklar (1959); dependence-concept treatment by Joe (1997)	Coles (textbook treatment); McNeil, Frey & Embrechts
ประเภท	Univariate time-series model	Dependence model	Tail / extreme-event model
แหล่งต้นตำรับ	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	Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 229-231.	Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598
ชื่อเรียกอื่น	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank)	EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold
ที่เกี่ยวข้อง	5	5	5
สรุป	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).	Copula models are a family of functions that describe the dependence structure between variables separately from their individual (marginal) distributions. The foundation is Sklar's theorem (1959), which shows that any multivariate distribution can be split into its marginals plus a copula; Joe (1997) developed the modern catalogue of dependence concepts. They are central to portfolio risk and credit modelling.	Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold.