विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| कंडिशनल वैल्यू-एट-रिस्क (अपेक्षित कमी)× | ऑटोरेग्रेसिव इंटीग्रेटेड मूविंग एवरेज (ARIMA) मॉडल× | |
|---|---|---|
| क्षेत्र≠ | वित्त | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 2000 | 2015 |
| प्रवर्तक≠ | Rockafellar & Uryasev (2000); Acerbi & Tasche (2002) | Box & Jenkins (Box-Jenkins methodology) |
| प्रकार≠ | Coherent tail-risk measure | Univariate time-series model |
| मौलिक स्रोत≠ | Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41. DOI ↗ | 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 |
| उपनाम≠ | CVaR, expected shortfall, average value-at-risk, tail VaR | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| संबंधित | 5 | 5 |
| सारांश≠ | 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. | 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). |
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