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Journal article

A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns

Abstract:: This paper presents a new approach to estimating the conditional probability distribution of multiperiod financial returns. Estimation of the tails of the distribution is particularly important for risk management tools, such as Value-at-Risk models. Using daily exchange rates, a new approach is compared to GARCH-based quantile estimates. The results suggest that the new method offers a useful alternative for estimating the conditional density.

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APA Style

Taylor, J. (2000). A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns.

MLA Style

Taylor, J. A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns. 2000.

Chicago Style

Taylor, J. 2000. A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns.
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+ Taylor, J More by this author

Role:: Author

Publication date:: 2000-07-01

UUID:: uuid:0a30bf27-3682-4c20-9cc5-c1b13aba568c
Local pid:: oai:eureka.sbs.ox.ac.uk:1729
Deposit date:: 2012-01-25
ARK identifier:: ark:/29072/ora_0a30bf2736824c209cc5c1b13aba568c

Terms of use

Copyright date:: 2000

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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