计算机科学
计量经济学
随机波动
隐含波动率
期权估价
非参数统计
参数统计
波动性(金融)
人工神经网络
波动微笑
布莱克-斯科尔斯模型
参数化模型
经济
机器学习
数学
统计
作者
Caio Almeida,Jianqing Fan,Gustavo Freire,Francesca Tang
标识
DOI:10.1080/07350015.2022.2099871
摘要
We introduce a novel two-step approach to predict implied volatility surfaces. Given any fitted parametric option pricing model, we train a feedforward neural network on the model-implied pricing errors to correct for mispricing and boost performance. Using a large dataset of S&P 500 options, we test our nonparametric correction on several parametric models ranging from ad-hoc Black–Scholes to structural stochastic volatility models and demonstrate the boosted performance for each model. Out-of-sample prediction exercises in the cross-section and in the option panel show that machine-corrected models always outperform their respective original ones, often by a large extent. Our method is relatively indiscriminate, bringing pricing errors down to a similar magnitude regardless of the misspecification of the original parametric model. Even so, correcting models that are less misspecified usually leads to additional improvements in performance and also outperforms a neural network fitted directly to the implied volatility surface.
科研通智能强力驱动
Strongly Powered by AbleSci AI