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Quantile Double AR Time Series Models for Financial Returns
Author(s) -
Cai Yuzhi,
MontesRojas Gabriel,
Olmo Jose
Publication year - 2013
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2261
Subject(s) - quantile , autoregressive model , markov chain monte carlo , econometrics , series (stratigraphy) , bayesian probability , markov chain , quantile function , value at risk , finance , computer science , mathematics , economics , statistics , probability distribution , risk management , paleontology , moment generating function , biology
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.