Edith Cowan University
Place of Publication
Joondalup, Western Australia
Faculty of Business and Law
School of Accounting, Finance and Economics
Value-at-Risk (VaR) has become the universally accepted metric adopted internationally under the Basel Accords for banking industry internal control and for regulatory reporting. This has focused attention on methods of measuring, estimating and forecasting lower tail risk. One promising technique is Quantile Regression which holds the promise of efficiently calculating (VAR). To this end, Engle and Manganelli in (2004) developed their CAViaR model (Conditional Autoregressive Value at Risk). In this paper we apply their model to Australian Stock Market indices and a sample of stocks, and test the efficacy of four different specifications of the model in a set of in and out of sample tests. We also contrast the results with those obtained from a GARCH(1,1) model, the RiskMetricsTM model and an APARCH model.