Empirical Analysis of Volatility Forecasting Model based on Genetic Programming

2017 ◽  
Author(s):  
Naoki Toriyama ◽  
Keiko Ono ◽  
Yukiko Orito
Keyword(s):  
Genetic Programming ◽  
Empirical Analysis ◽  
Forecasting Model ◽  
Volatility Forecasting ◽  
Model Based

scholarly journals Liquidity effects on oil volatility forecasting: From fintech perspective

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260289
Author(s):  
Shusheng Ding ◽  
Tianxiang Cui ◽  
Yongmin Zhang ◽  
Jiawei Li
Keyword(s):  
Natural Selection ◽  
Genetic Programming ◽  
New Technology ◽  
Garch Model ◽  
Forecasting Model ◽  
Volatility Forecasting ◽  
Innovative Work ◽  
Financial Research ◽  
Forecasting Volatility ◽  
The Garch Model

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.

Product Sales Forecasting Model Based on Robust Wavelet v-Support Vector Machine

2009 ◽  
Vol 35 (7) ◽  
pp. 1027-1032 ◽  
Author(s):  
Qi WU ◽  
Hong-Sen YAN ◽  
Bin WANG
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Forecasting Model ◽  
Model Based ◽  
Product Sales

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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