Document Search
Add To My Bookshelf Sign in or Register Save And Annotate

GARCH sign in to follow this
quantile regression sign in to follow this
Maximum Likelihood sign in to follow this

VaR Methods sign in to follow this
--Evaluation/Comparison sign in to follow this
Discuss This Paper
Sign in to follow this page
Recent Comments
Risk-Parameter Estimation in Volatility Models
Year Of Publication: 2012
Month Of Publication: October
Resource Link: Click here to open
Pages: 43
Download Count: 0
View Count: 1798
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 10-22-2012
Publisher: Administrator
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.
This document may be downloaded without charge from MPRA by clicking on the 'Buy from Publisher' button.
Francq, C_hristian Sign in to follow this author
Zakoïan, Jean-Michel Sign in to follow this author
This document's citation network:
Similar Documents:
Close window
Sign up in one step, no personal information required. Already a Member?

Repeat Email:
User Name:
Confirm Password:

Sign Up

Welcome to GloriaMundi!
Thanks for singning up

continue or edit your profile