TY  - RPRT
CY  - Toulouse
ID  - publications16617
UR  - http://tse-fr.eu/pub/28749
A1  - Bontemps, Christian
Y1  - 2014/10/15/
N2  - In this paper, we develop moment-based tests for parametric discrete distributions. Momentbased test techniques are attractive as they provide easy-to-implement test statistics. We propose a general transformation that makes the moments of interest insensitive to the parameter estimation uncertainty. This transformation is valid for some extended families of non-differentiable moments that are of great interest in the case of discrete distributions. Considering the power function under local alternatives, we compare this strategy with the one in which parameter uncertainty is corrected. The special example of backtesting of valueat- risk (VaR) forecasts is treated in detail, and we provide simple moments that have good size and power properties in Monte Carlo experiments. Additional examples considered are discrete counting processes and the geometric distribution. We finally apply our method to backtesting of VaR forecasts derived from a T-GARCH(1,1) model estimated using foreign exchange-rate data.
PB  - TSE Working Paper
T3  - TSE Working Paper
KW  - moment-based tests
KW  - parameter uncertainty
KW  - discrete distributions
KW  - value-at-risk
KW  - backtesting
M1  - working_paper
TI  - Simple moment-based tests for value-at-risk models and discrete distribution
AV  - public
EP  - 76
ER  -