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

Taylor, James (2000) A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns. Journal of Forecasting, 19 (4). pp. 299-311.

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.

Item Type: Article
Keywords: Volatility; Regression analysis; Forecasting
Subject(s): Management science
Centre: Faculty of Management Science
Date Deposited: 24 Jan 2012 20:24
Last Modified: 23 Oct 2015 14:06
URI: http://eureka.sbs.ox.ac.uk/id/eprint/1729

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