手法を比較

	テールリスク指標（期待ショートフォール、スペクトル、エクスペクタイル）	金融系列のためのマルコフ・レジームスイッチングモデル
分野	ファイナンス	ファイナンス
系統	Regression model	Regression model
提唱年	1999	1989
提唱者	Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall)	James D. Hamilton
種類	Coherent tail risk measure	Markov regime-switching time-series model
原典	Artzner, P., Delbaen, F., Eber, J.-M. & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203–228.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
別名	expected shortfall, conditional value at risk, CVaR, spectral risk measure	Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model
関連	5	1
概要	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.