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.
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.
|Keywords:||Volatility; Regression analysis; Forecasting|
|Date Deposited:||24 Jan 2012 20:24|
|Last Modified:||10 Feb 2017 17:13|
Actions (login required)