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A quantile regression neural network approach to estimating the conditional density of multiperiod returns
Author(s) -
Taylor James W.
Publication year - 2000
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/1099-131x(200007)19:4<299::aid-for775>3.0.co;2-v
Subject(s) - quantile , econometrics , quantile regression , autoregressive conditional heteroskedasticity , volatility (finance) , conditional probability distribution , conditional variance , conditional expectation , range (aeronautics) , value at risk , mathematics , statistics , economics , risk management , finance , materials science , composite material
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. A popular approach is to assume a Gaussian distribution, and to use a theoretically derived variance expression which is a non‐linear function of the holding period, k , and the one‐step‐ahead volatility forecast, $\nopagenumbers\hat{\sigma}_{t+1}$\nopagenumbers\end . The new method avoids the need for a distributional assumption by applying quantile regression to the historical returns from a range of different holding periods to produce quantile models which are functions of k and $\nopagenumbers\hat{\sigma}_{t+1}$\nopagenumbers\end . A neural network is used to estimate the potentially non‐linear quantile models. Using daily exchange rates, the approach is compared to GARCH‐based quantile estimates. The results suggest that the new method offers a useful alternative for estimating the conditional density. Copyright © 2000 John Wiley & Sons, Ltd.