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| 尾部风险度量（预期短缺、谱系、期望分位数）× | 金融序列的马尔可夫状态转换模型× | |
|---|---|---|
| 领域 | 金融学 | 金融学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1999 | 1989 |
| 提出者≠ | Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall) | James D. Hamilton |
| 类型≠ | Coherent tail risk measure | Markov regime-switching time-series model |
| 开创性文献≠ | Artzner, P., Delbaen, F., Eber, J.-M. & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203–228. DOI ↗ | Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. DOI ↗ |
| 别名≠ | expected shortfall, conditional value at risk, CVaR, spectral risk measure | Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model |
| 相关≠ | 5 | 1 |
| 摘要≠ | Tail risk measures quantify the loss distribution beyond Value-at-Risk (VaR). Expected Shortfall — the expected loss given that VaR is exceeded — is the leading coherent risk measure, formalised by Artzner, Delbaen, Eber and Heath (1999) and shown to be coherent by Acerbi and Tasche (2002). Spectral and expectile-based measures generalise it. | The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/bear or high/low volatility). It is the financial application of Hamilton's MS-AR model, where an unobserved Markov state governs which parameter set is active at each point in time. |
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