Multi-step Prediction of Financial Asset Return Volatility Using Parsimonious Autoregressive Sequential Model

Autor:	Xiaoqian Wei, Xiangru Fan, Wu Qi, Wen Zhang, Wang Di
Rok vydání:	2020
Předmět:	Autoregressive model Financial asset Computer science business.industry Return volatility Autoregressive conditional heteroskedasticity Deep learning Econometrics Artificial intelligence Time series Volatility (finance) Sequential model business
Zdroj:	Mining Data for Financial Applications ISBN: 9783030377199 MIDAS@PKDD
Popis:	Previously, application of deep learning based sequential model drastically improved accuracy of volatility prediction in modelling of financial time series. However, unlike traditional financial time series model such as GARCH family of models, majority of deep learning based financial time series models focus solely on optimizing a single-step volatility prediction error and are not capable of conduct multi-step training and prediction of volatilities since volatility is the inherent uncertainty of the model prediction, whose multi-step prediction is drastically different from prediction of the mean of the financial time series.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::75708fdcd11ee618cda3eb6055fc7e3c https://doi.org/10.1007/978-3-030-37720-5_9 Zobrazit plný text záznamu