Journal article Open Access

Forecasting for Financial Stock Returns Using a Quantile Function Model

Yuzhi Cai


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  <identifier identifierType="DOI">10.5281/zenodo.1109383</identifier>
  <creators>
    <creator>
      <creatorName>Yuzhi Cai</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Forecasting for Financial Stock Returns Using a Quantile Function Model</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2015</publicationYear>
  <subjects>
    <subject>DJIA</subject>
    <subject>Financial returns</subject>
    <subject>predictive distribution</subject>
    <subject>quantile function model.</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2015-09-02</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1109383</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1109382</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/waset</relatedIdentifier>
  </relatedIdentifiers>
  <version>10002644</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">In this talk, we introduce a newly developed quantile
function model that can be used for estimating conditional
distributions of financial returns and for obtaining multi-step ahead
out-of-sample predictive distributions of financial returns. Since we
forecast the whole conditional distributions, any predictive quantity
of interest about the future financial returns can be obtained simply
as a by-product of the method. We also show an application of the
model to the daily closing prices of Dow Jones Industrial Average
(DJIA) series over the period from 2 January 2004 - 8 October 2010.
We obtained the predictive distributions up to 15 days ahead for
the DJIA returns, which were further compared with the actually
observed returns and those predicted from an AR-GARCH model.
The results show that the new model can capture the main features
of financial returns and provide a better fitted model together with
improved mean forecasts compared with conventional methods. We
hope this talk will help audience to see that this new model has the
potential to be very useful in practice.</description>
    <description descriptionType="Other">{"references": ["Koenker, R. (2005). Quantiles Regression. Cambridge University Press.", "Gilchrist, W.G. (2000). Statistical Modelling with Quantile Functions.\nChapman &amp; Hall/CRC.", "Cai, Y. (2015). A general quantile function model for economic and\nfinancial time series. Econometric Reviews. Accepted.", "Cai, Y. (2013). Quantile function models for survival data analysis.\nAustralian and New Zealand Journal of Statistics 55, 155-172.", "Cai, Y. (2010a). Multivariate quantile function models. Statistica Sinica\n20, 481-496.", "Cai, Y. (2010b). Polynomial power-Pareto quantile function models.\nExtremes 13, 291-314.", "Cai, Y. (2009). Autoregression with non-Gaussian Innovations.\nJournal of Time Series Econometrics, Vol.1, Iss.2, Article 2. DOI:\n10.2202/1941-1928.1016.", "Cai, Y, Montes-Rojas, G. and Olmo, J. (2013). Quantile double AR time\nseries models for financial returns. Journal of Forecasting 32, 551-560.", "Engle, R.F. and Manganelli, S. (2004). CAViaR: Conditional\nautoregressive value at risk by regression quantiles. Journal of\nBusiness and Economic Statistics 22, 367-381."]}</description>
  </descriptions>
</resource>
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