Liquidity effects on oil volatility forecasting: From fintech perspective

• Published in: PloS one Vol. 16; no. 11; p. e0260289
• Main Authors: Ding, Shusheng, Cui, Tianxiang, Zhang, Yongmin, Li, Jiawei
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 29.11.2021; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Autoregressive models; Bank technology; Biology and Life Sciences; Commerce; Commodity futures; Computer and Information Sciences; Computer science; Creativity; Economic aspects; Evolutionary algorithms; Finance; Forecasting; Fuel Oils - economics; Genetic algorithms; Heuristic methods; High frequency trading; Influence; Innovations; Liquidity; Liquidity (Finance); Modelling; Models, Economic; Mutation; Natural selection; New technology; Physical Sciences; Research and Analysis Methods; Securities markets; Social Sciences; Software; Stochastic models; Stock exchanges; Technological change; Technology; Volatility; China; United States > US; Ningbo China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0260289

Fin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further improvement of the GARCH model is still challenging. In this paper, we demonstrate how Fintech can play a part in volatility forecasting by employing a metaheuristic procedure called Genetic Programming. On the basis, we are able to develop a new volatility forecasting model, which can beat GARCH family models (including GARCH, IGARCH and TGARCH models) in a significant way. Since genetic programming is an evolutionary algorithm based on the principles of natural selection, this innovative work will be a breakthrough point in the financial area. The innovation of this paper demonstrates how GP technology can be applied in the financial field, attempting to explore the volatility forecasting area from the combination of new technology and finance, known as fintech. More importantly, when the formula of volatility forecasting is unknown as we introduce a new factor, namely, the liquidity factor, we unveil that how GP method can be helpful in determining the specific volatility forecasting model format. We thereby exhibit the liquidity effects on volatility forecasting filed from the fintech perspective.