Neural network models for conditional quantiles of financial returns and volatility
|Author:||Mgr. Marek Hauzr|
|Year:||2016 - summer|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Finance, Financial Markets and Banking
|Awards and prizes:|
|Abstract:||This thesis investigates forecasting performance of Quantile Regression Neural Networks in
forecasting multiperiod quantiles of realized volatility and quantiles of returns. It relies on
model-free measures of realized variance and its components (realized variance, median
realized variance, integrated variance, jump variation and positive and negative
semivariances). The data used are S&P 500 futures and WTI Crude Oil futures contracts.
Resulting models of returns and volatility have good absolute performance and relative
performance in comparison to the linear quantile regression models. In the case of insample
the models estimated by Quantile Regression Neural Networks provide better
estimates than linear quantile regression models and in the case of out-of-sample they are