Quantile double AR time series models for financial returns
CAI, YUZHI; MONTES ROJAS, GABRIEL VICTORIO; OLMO, JOSE
JOURNAL OF FORECASTING
Lugar: New York; Año: 2013 vol. 32 p. 551 - 551
We develop a novel quantile double autoregressive model for modelling financial time series. This is done by specifying a generalized lambda distribution to the quantile function of the location-scale double autoregressive model developed by Ling (2004, 2007). Parameter estimation uses Markov chain Monte Carlo Bayesian methods. A simulation technique is introduced for forecasting the conditional distribution of financial returns m periods ahead, and hence any for predictive quantities of interest. The application to forecasting value-at-risk at different time horizons and coverage probabilities for Dow Jones Industrial Average shows that our method works very well in practice.