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| 꼬리 위험 측정 지표 (기대 손실, 스펙트럼, 익스펙타일)× | 금융 시계열을 위한 마르코프 회귀 전환 모형× | |
|---|---|---|
| 분야 | 재무학 | 재무학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1999 | 1989 |
| 창시자≠ | Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall) | James D. Hamilton |
| 유형≠ | Coherent tail risk measure | Markov regime-switching time-series model |
| 원전≠ | 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 ↗ |
| 별칭≠ | expected shortfall, conditional value at risk, CVaR, spectral risk measure | Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model |
| 관련≠ | 5 | 1 |
| 요약≠ | 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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