Journal article 1432 views
A quantile double AR model: estimation and forecasting
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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2013
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URI: | https://cronfa.swan.ac.uk/Record/cronfa15289 |
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2016-10-31T11:01:35.9341600 v2 15289 2013-07-30 A quantile double AR model: estimation and forecasting eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2013-07-30 BAF 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. Journal Article Journal of Forecasting 32 560 Bayesian methods; density forecasts; generalized lambda distribution; quantile function; 31 7 2013 2013-07-31 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2016-10-31T11:01:35.9341600 2013-07-30T10:12:24.4884007 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1 Gabriel Montes-Rojas 2 Jose Olmo 3 |
title |
A quantile double AR model: estimation and forecasting |
spellingShingle |
A quantile double AR model: estimation and forecasting Yuzhi Cai |
title_short |
A quantile double AR model: estimation and forecasting |
title_full |
A quantile double AR model: estimation and forecasting |
title_fullStr |
A quantile double AR model: estimation and forecasting |
title_full_unstemmed |
A quantile double AR model: estimation and forecasting |
title_sort |
A quantile double AR model: estimation and forecasting |
author_id_str_mv |
eff7b8626ab4cc6428eef52516fda7d6 |
author_id_fullname_str_mv |
eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai |
author |
Yuzhi Cai |
author2 |
Yuzhi Cai Gabriel Montes-Rojas Jose Olmo |
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Journal article |
container_title |
Journal of Forecasting |
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32 |
publishDate |
2013 |
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Swansea University |
college_str |
Faculty of Humanities and Social Sciences |
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Faculty of Humanities and Social Sciences |
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facultyofhumanitiesandsocialsciences |
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Faculty of Humanities and Social Sciences |
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School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance |
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description |
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. |
published_date |
2013-07-31T03:17:25Z |
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1763750387551043584 |
score |
11.035414 |