深层结构估计及其在期权定价中的应用
Deep Structural Estimation: With an Application to Option Pricing
摘要：
我们提出了一种新的结构估计框架，在该框架中，我们用深度神经网络训练经济模型的代理。我们的方法消除了维数灾难，加快了评价和参数估计的数量级，这显着提高了一个人的能力进行分析，需要频繁的参数重新估计。作为一个实证应用，我们比较了两个流行的期权定价模型（赫斯顿和贝茨模型与双指数跳跃）对非参数随机森林模型。我们记录如下：a）平均而言，贝茨模型产生更好的样本外定价，但两种结构模型在波动率表面的大面积上都未能优于随机森林; b）随机森林在短期内更具竞争力（例如，1天），短期期权（到期日少于7天），以及流动性差的日子; c）两种结构模型在样本外Delta对冲中的表现优于随机森林; d）Heston模型的相对表现在2008年金融危机后显著恶化。
英文摘要：
We propose a novel structural estimation framework in which we train a surrogate of an economic model with deep neural networks. Our methodology alleviates the curse of dimensionality and speeds up the evaluation and parameter estimation by orders of magnitudes, which significantly enhances one's ability to conduct analyses that require frequent parameter re-estimation. As an empirical application, we compare two popular option pricing models (the Heston and the Bates model with double-exponential jumps) against a non-parametric random forest model. We document that: a) the Bates model produces better out-of-sample pricing on average, but both structural models fail to outperform random forest for large areas of the volatility surface; b) random forest is more competitive at short horizons (e.g., 1-day), for short-dated options (with less than 7 days to maturity), and on days with poor liquidity; c) both structural models outperform random forest in out-of-sample delta hedging; d) the Heston model's relative performance has deteriorated significantly after the 2008 financial crisis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
