A quantile double AR model: estimation and forecasting - Yuzhi Cai (Journal of Forecasting, 2013)
We develop a novel quantile double autoregressive model for modelling financial time series. This is done byspecifying a generalized lambda distribution to the quantile function of the location-scale double autoregressive modeldeveloped by Ling (2004, 2007). Parameter estimation uses Markov chain Monte Carlo Bayesian methods. A simulationtechnique is introduced for forecasting the conditional distribution of financial returns m periods ahead, and henceany for predictive quantities of interest. The application to forecasting value-at-risk at different time horizons andcoverage probabilities for Dow Jones Industrial Average shows that our method works very well in practice. Copyright© 2013 John Wiley & Sons, Ltd.