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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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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

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