Quantile Double AR Time Series Models for Financial Returns
Article first published online: 29 MAY 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 32, Issue 6, pages 551–560, September 2013
How to Cite
Cai, Y., Montes-Rojas, G. and Olmo, J. (2013), Quantile Double AR Time Series Models for Financial Returns. J. Forecast., 32: 551–560. doi: 10.1002/for.2261
- Issue published online: 26 JUL 2013
- Article first published online: 29 MAY 2013
- Bayesian methods;
- density forecasts;
- generalized lambda distribution;
- quantile function;
- quantile forecasts
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. Copyright © 2013 John Wiley & Sons, Ltd.