A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
Zong Ke, Jingyun Xu, Zizhou Zhang, Yu Cheng, Wenjun Wu
Abstract
This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility, Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back -propagation neural network and genetic algorithm to predict future volatility of emerging stock markets and found that the results are quite accurate with low errors.
Topics & Concepts
Computer scienceArtificial neural networkBackpropagationVolatility (finance)Genetic algorithmAlgorithmArtificial intelligenceMachine learningEconometricsMathematicsAdvanced Algorithms and ApplicationsSmart Grid and Power SystemsAdvanced Decision-Making Techniques