Scholarlink

<p>This paper presents a deep learning approach for option pricing using a long short-term memory (LSTM) neural network applied to European call options on the S&amp;P 500 index. We utilize a rolling window approach that trains 12 instances of the LSTM model, one for each month of 2021. To gain further insight into the model performance, we use explainable artificial intelligence (XAI) through SHapley Additive Explanations (SHAP). We find that the LSTM model outperforms the Black–Scholes and the Heston models and a multilayer perceptron (MLP) neural network regarding overall pricing accuracy. Most notably, the time-sequencing nature of LSTM enables the proposed model to capture sufficient short-term volatility from recently traded options. This result is still robust when controlling for time-varying volatility dynamics. Thus, the model is less prone to measurement errors in volatility.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Option pricing with deep learning: a long short-term memory approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月12日

查看全文
  • Rita Pimentel,
  • Morten Risstad,
  • Sondre Rogde,
  • Erlend S. Rygg,
  • Jacob Vinje,
  • Sjur Westgaard,
  • Cassandra Wu

摘要

This paper presents a deep learning approach for option pricing using a long short-term memory (LSTM) neural network applied to European call options on the S&P 500 index. We utilize a rolling window approach that trains 12 instances of the LSTM model, one for each month of 2021. To gain further insight into the model performance, we use explainable artificial intelligence (XAI) through SHapley Additive Explanations (SHAP). We find that the LSTM model outperforms the Black–Scholes and the Heston models and a multilayer perceptron (MLP) neural network regarding overall pricing accuracy. Most notably, the time-sequencing nature of LSTM enables the proposed model to capture sufficient short-term volatility from recently traded options. This result is still robust when controlling for time-varying volatility dynamics. Thus, the model is less prone to measurement errors in volatility.