Abstract
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.
Highlights
1.1 Motivations and aimsFin-tech is an emerging field, which drives revolutionary innovations in the financial spectrum recently
This paper gives a further annotation, suggesting that fintech involves how Artificial Intelligence (AI), such as genetic programming, can be integrated with financial modelling and this paper itself is a magnificent demonstration of finance with AI, which is a radical innovation in finance
This paper proposes the improved GARCH model, which integrates the liquidity factor
Summary
Fin-tech is an emerging field, which drives revolutionary innovations in the financial spectrum recently. It integrates advanced technology into financial area, provoking the evolutionary episode of the financial industry and research (Buchak et al 2018 [1]; Chen et al 2019 [2]). Fleming and Remolona (1999) [10] have investigated the relation between liquidity, volatility and public information in the US Treasury market. The goal of the paper is to adopt AI technologies to generate the best model which can comprise trading liquidity effect into volatility forecasting, and can be integrated into the existing fintech systems such as high frequency trading platforms and derivative trading platforms in the hedge fund industry
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