A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns - ORA - Oxford University Research Archive

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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. (2016). 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. 2016.

Chicago Style

Taylor, J. 2016. “A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns.”
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Bibliographic Details

Publication date:: 2000-07-01

Item Description

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

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Copyright date:: 2000

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

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