Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Riska mēri astē (paredzamais trūkums, spektrālie, ekspektiles)× | Markova režīmu pārslēgšanās modelis finanšu sērijām× | |
|---|---|---|
| Nozare | Finanses | Finanses |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1999 | 1989 |
| Autors≠ | Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall) | James D. Hamilton |
| Tips≠ | Coherent tail risk measure | Markov regime-switching time-series model |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | expected shortfall, conditional value at risk, CVaR, spectral risk measure | Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model |
| Saistītās≠ | 5 | 1 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
|
|