Journal article 1424 views
A quantile double AR model: estimation and forecasting
Yuzhi Cai 
,
Gabriel Montes-Rojas,
Jose Olmo
,
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...
| Published in: | Journal of Forecasting |
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| Published: |
2013
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa15289 |
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| 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. |
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| Keywords: |
Bayesian methods; density forecasts; generalized lambda distribution; quantile function; |
| College: |
Faculty of Humanities and Social Sciences |
| End Page: |
560 |