Cholesky-ANN models for predicting multivariate realized volatility
Andrea Bucci ()
MPRA Paper from University Library of Munich, Germany
Accurately forecasting multivariate volatility plays a crucial role for the financial industry. The Cholesky-Artificial Neural Networks specification here presented provides a twofold advantage for this topic. On the one hand, the use of the Cholesky decomposition ensures positive definite forecasts. On the other hand, the implementation of artificial neural networks allows to specify nonlinear relations without any particular distributional assumption. Out-of-sample comparisons reveal that Artificial neural networks are not able to strongly outperform the competing models. However, long-memory detecting networks, like Nonlinear Autoregressive model process with eXogenous input and long shortterm memory, show improved forecast accuracy respect to existing econometric models.
Keywords: Neural Networks; Machine Learning; Stock market volatility; Realized Volatility (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 G17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets, nep-for, nep-ore and nep-pay
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