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- W1805573625 abstract "This paper introduces the concept of risk parameter in conditional volatility models of the form $epsilon_t=sigma_t(theta_0)eta_t$ and develops statistical procedures to estimate this parameter. For a given risk measure $r$, the risk parameter is expressed as a function of the volatility coefficients $theta_0$ and the risk, $r(eta_t)$, of the innovation process. A two-step method is proposed to successively estimate these quantities. An alternative one-step approach, relying on a reparameterization of the model and the use of a non Gaussian QML, is proposed. Asymptotic results are established for smooth risk measures as well as for the Value-at-Risk (VaR). Asymptotic comparisons of the two approaches for VaR estimation suggest a superiority of the one-step method when the innovations are heavy-tailed. For standard GARCH models, the comparison only depends on characteristics of the innovations distribution, not on the volatility parameters. Monte-Carlo experiments and an empirical study illustrate these findings." @default.
- W1805573625 created "2016-06-24" @default.
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- W1805573625 date "2012-10-04" @default.
- W1805573625 modified "2023-10-16" @default.
- W1805573625 title "Risk-parameter estimation in volatility models" @default.
- W1805573625 hasPublicationYear "2012" @default.
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