Yan, Yang
(2014)
Essays in modelling and estimatingvValue-at-risk.
PhD thesis, London School of Economics and Political Science.
Abstract
The thesis concerns semiparametric modelling and forecasting Value-at-Risk models, and the applications of these in Önancial data. Two general classes of semiparametric VaR models are proposed, the Örst method is introduced by deÖning some e¢ cient estimators of the risk measures in a semiparametric GARCH model through moment constraints and a quantile estimator based on inverting an empirical likelihood weighted distribution. It is found that the new quantile estimator is uniformly more e¢ cient than the simple empirical quantile and a quantile estimator based on normalized residuals. At the same time, the e¢ ciency gain in error quantile estimation hinges on the e¢ ciency of estimators of the variance parameters. We show that the same conclusion applies to the estimation of conditional Expected Shortfall. The second model proposes a new method to forecast one-period-ahead Value-at-Risk (VaR) in general ARCH(1) models with possibly heavy-tailed errors. The proposed method is based on least square estimation for the log-transformed model. This method imposes weak moment conditions on the errors. The asymptotic distribution also accounts for the parameter uncertainty in volatility estimation. We test our models against some conventional VaR forecasting methods, and the results demonstrate that our models are among the best in forecasting VaR.
Actions (login required)
 |
Record administration - authorised staff only |
Download statistics
Download statistics