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A quantile double AR model: estimation and forecasting

Yuzhi Cai Orcid Logo, Gabriel Montes-Rojas, Jose Olmo

Journal of Forecasting, Volume: 32

Swansea University Author: Yuzhi Cai Orcid Logo

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...

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Published in: Journal of Forecasting
Published: 2013
URI: https://cronfa.swan.ac.uk/Record/cronfa15289
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first_indexed 2013-08-22T01:57:36Z
last_indexed 2018-02-09T04:47:07Z
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spelling 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
format Journal article
container_title Journal of Forecasting
container_volume 32
publishDate 2013
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 0
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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
_version_ 1763750387551043584
score 11.017797

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