基于深度学习的金融市场波动率预测模型

Research and application of financial market volatility prediction based on deep learning

波动率在金融投资和风险管理中扮演着至关重要的角色,能够反映金融资产的收益和风险水平,为构建期权量化投资策略和决策以及风险控制提供重要参考指标。然而,波动率具有非线性和长期依赖性问题,如每日变化趋势不稳定,未来变化趋势与历史数据相关等。为解决这些问题,本文基于改进的Transformer构建了波动率预测模型TGC-FinTrans(TCN-BiGRU-CNN Finance Transformer)。实验结果表明,该模型在预测金融数据波动率方面优于其他基线方法,能够更加准确地预测波动率并捕捉金融市场的复杂变化,为投资者提供更为精准的决策参考。

Volatility plays a crucial role in financial investment and risk management,as it can reflect the return and risk level of financial assets,and provide an important reference indicator for constructing quantitative investment strategies and decisions on options and risk control.However,volatility has the problems of nonlinearity and long-term dependence,such as the unstable trend of daily changes and the correlation of future trends with historical data.To solve these problems,this paper constructs a volatility prediction model TGC-FinTrans(TCN-BiGRU-CNN Finance Transformer)based on an improved Transformer.The experimental results show that the model outperforms other baseline models in predicting the volatility of financial data and has significant advantages in predicting volatility more accurately and capturing the complex changes in the financial market,providing investors with more accurate decision-making references.