Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Thibitisho la Thamani ya Hatari (Matarajio ya Upungufu)× | Nadharia ya Hisa Zinazotambulika na Muundo wa HAR× | |
|---|---|---|
| Nyanja | Fedha | Fedha |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2000 | 2009 |
| Mwanzilishi≠ | Rockafellar & Uryasev (2000); Acerbi & Tasche (2002) | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) |
| Aina≠ | Coherent tail-risk measure | Time-series regression of realized variance |
| Chanzo asilia≠ | Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41. DOI ↗ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196. DOI ↗ |
| Majina mbadala | CVaR, expected shortfall, average value-at-risk, tail VaR | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | Conditional Value-at-Risk (CVaR), also called Expected Shortfall, is a coherent tail-risk measure that quantifies the conditional expectation of losses beyond the Value-at-Risk threshold. It was introduced for optimization by Rockafellar and Uryasev (2000) and shown to be coherent by Acerbi and Tasche (2002), and it has replaced VaR as the regulatory standard under Basel III/IV. | Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction. |
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