Linganisha mbinu

	Tail Risk Measures	Kielelezo cha Mabadiliko ya Mfumo wa Markov kwa Mfululizo wa Kifedha
Nyanja	Fedha	Fedha
Familia	Regression model	Regression model
Mwaka wa asili	1999	1989
Mwanzilishi	Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall)	James D. Hamilton
Aina	Coherent tail risk measure	Markov regime-switching time-series model
Chanzo asilia	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.
Majina mbadala	expected shortfall, conditional value at risk, CVaR, spectral risk measure	Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model
Zinazohusiana	5	1
Muhtasari	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.