เปรียบเทียบวิธี

	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)	Conditional Value-at-Risk (Expected Shortfall)
สาขาวิชา	เศรษฐมิติ	การเงิน
ตระกูล	Regression model	Regression model
ปีกำเนิด	2015	2000
ผู้ริเริ่ม	Box & Jenkins (Box-Jenkins methodology)	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)
ประเภท	Univariate time-series model	Coherent tail-risk measure
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	Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41.
ชื่อเรียกอื่น	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	CVaR, expected shortfall, average value-at-risk, tail VaR
ที่เกี่ยวข้อง	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).	Conditional Value-at-Risk (CVaR), also called Expected Shortfall, is a coherent tail-risk measure that quantifies the conditional expectation of losses beyond the Value-at-Risk threshold. It was introduced for optimization by Rockafellar and Uryasev (2000) and shown to be coherent by Acerbi and Tasche (2002), and it has replaced VaR as the regulatory standard under Basel III/IV.