On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning-Reference-Cited by-同舟云学术

On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning

Published:2020-03-11 Issue:7 Volume:39 Page:1081-1097
ISSN:0277-6693
Container-title:Journal of Forecasting
language:en
Short-container-title:Journal of Forecasting

Author:

Li Zhenwei¹,Han Jing²,Song Yuping¹

Affiliation:

1. School of Finance and Business Shanghai Normal University Shanghai PR China

2. School of Finance and Management Shanghai University of International Business and Economics Shanghai PR China

Funder

Shanghai Normal University

National Natural Science Foundation of China

Publisher

Wiley

Subject

Management Science and Operations Research,Statistics, Probability and Uncertainty,Strategy and Management,Computer Science Applications,Modeling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.2677

Reference23 articles.

1. Modeling and Forecasting Realized Volatility

2. Lessons from the flash crash for the regulation of high‐frequency traders;Barrales E. O.;Fordham Journal of Corporate and Financial Law,2012

3. HFT and market quality;Biais B.;Bankers, Markets and Investors,2014

4. On the volatility–volume relationship in energy futures markets using intraday data

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