So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Mô hình ARIMA (Autoregressive Integrated Moving Average)× | Conditional Value-at-Risk (Expected Shortfall)× | |
|---|---|---|
| Lĩnh vực≠ | Kinh tế lượng | Tài chính |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 2015 | 2000 |
| Người khởi xướng≠ | Box & Jenkins (Box-Jenkins methodology) | Rockafellar & Uryasev (2000); Acerbi & Tasche (2002) |
| Loại≠ | Univariate time-series model | Coherent tail-risk measure |
| Công trình gốc≠ | 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. DOI ↗ |
| Tên gọi khác≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | CVaR, expected shortfall, average value-at-risk, tail VaR |
| Liên quan | 5 | 5 |
| Tóm tắt≠ | 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. |
| ScholarGateBộ dữ liệu ↗ |
|
|