On improving GARCH volatility forecasts for Bitcoin via a meta-learning approach


ARAS S.

Knowledge-Based Systems, vol.230, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 230
  • Publication Date: 2021
  • Doi Number: 10.1016/j.knosys.2021.107393
  • Journal Name: Knowledge-Based Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Computer & Applied Sciences, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA)
  • Keywords: Volatility, Bitcoin, Model Confidence Set, Combining forecasts, GARCH, REALIZED VOLATILITY, ANYTHING BEAT, CRYPTOCURRENCIES, MODELS, RETURN, DOLLAR, PRICE, GOLD
  • Dokuz Eylül University Affiliated: Yes

Abstract

© 2021 Elsevier B.V.Modelling the volatility of Bitcoin, the cryptocurrency with the largest market share, has recently attracted considerable attention from researchers, practitioners and investors in financial markets and portfolio management. For this purpose, a wide variety of GARCH-type models have been employed. However, there is no consensus in the literature on which specification arising from the volatility equation and the assumed error distribution is better in an out-of-sample performance. This study tries to fill this gap by comparing the forecasting performances of 110 GARCH-type models for Bitcoin volatility. Furthermore, it proposes a new combining method based on support vector machines (SVM). This method effectively selects the set of superior models to perform meta-learning. The results indicate that the best performing GARCH specification depends on the loss function chosen, and the proposed method leads to more accurate volatility forecasts than those of the best GARCH-type models and other combining methods investigated.