No Cover Image

Journal article 641 views

A quantile double AR model: estimation and forecasting / Yuzhi Cai; Gabriel Montes-Rojas; Jose Olmo

Journal of Forecasting, Volume: 32

Swansea University Author: Yuzhi, Cai

Abstract

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 Mo...

Full description

Published in: Journal of Forecasting
Published: 2013
URI: https://cronfa.swan.ac.uk/Record/cronfa15289
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: 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.
Keywords: Bayesian methods; density forecasts; generalized lambda distribution; quantile function;
College: School of Management
End Page: 560