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Quantile double AR time series models for financial returns

Quantile double AR time series models for financial returns
Quantile double AR time series models for financial returns
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.
bayesian methods, density forecasts, generalized lambda, distribution, quantile function, quantile forecasts
0277-6693
551-560
Cai, Y.
0d30b3eb-8aeb-46e6-87ef-7fff9fded7a1
Montes-Rojas, G.
d139fc6a-1f73-4db6-bb54-a38d14a7b030
Olmo, J.
706f68c8-f991-4959-8245-6657a591056e
Cai, Y.
0d30b3eb-8aeb-46e6-87ef-7fff9fded7a1
Montes-Rojas, G.
d139fc6a-1f73-4db6-bb54-a38d14a7b030
Olmo, J.
706f68c8-f991-4959-8245-6657a591056e

Cai, Y., Montes-Rojas, G. and Olmo, J. (2013) Quantile double AR time series models for financial returns. Journal of Forecasting, 32 (6), 551-560. (doi:10.1002/for.2261).

Record type: Article

Abstract

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.

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e-pub ahead of print date: 29 May 2013
Published date: September 2013
Keywords: bayesian methods, density forecasts, generalized lambda, distribution, quantile function, quantile forecasts
Organisations: Economics

Identifiers

Local EPrints ID: 348641
URI: https://eprints.soton.ac.uk/id/eprint/348641
ISSN: 0277-6693
PURE UUID: 68519f5c-e6fb-4694-8678-c04ca5af4f61
ORCID for J. Olmo: ORCID iD orcid.org/0000-0002-0437-7812

Catalogue record

Date deposited: 18 Feb 2013 10:22
Last modified: 26 Nov 2019 01:35

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