Abstract The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various markets such as …
It is unquestionable that time series forecasting is of paramount importance in many fields. The most used machine learning models to address time series forecasting tasks are …
S Aras - Expert Systems with Applications, 2021 - Elsevier
Abstract Machine learning techniques have been used frequently for volatility forecasting. However, previous studies have built these hybrid models in a form of a first-order GARCH …
P Hajek, L Hikkerova, JM Sahut - Research in International Business and …, 2023 - Elsevier
Investor sentiment is widely recognized as the major determinant of cryptocurrency prices. Although earlier research has revealed the influence of investor sentiment on cryptocurrency …
Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of …
Purpose The COVID-19 pandemic has led to global economic policy uncertainty, which has increased the need to investigate ways to mitigate the uncertainty. This study aims to …
K Cortez, MP Rodríguez-García, S Mongrut - Mathematics, 2020 - mdpi.com
In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) …
The most popular cryptocurrency used worldwide is bitcoin. Many everyday folks and investors are now investing in bitcoin. However, it becomes quite difficult to evaluate or …