论文标题
GAN-MC：降低导数定价的差异工具
GAN-MC: a Variance Reduction Tool for Derivatives Pricing
论文作者
论文摘要
我们提出了一个无参数模型，用于估计使用非监督学习网络和蒙特卡洛（Monte Carlo）的期权，前瞻和期货等金融衍生品的价格或估值。尽管某些基于套利的定价公式在衍生品定价上的性能很大，例如在期权定价上的黑色 - choles，但基于模型的蒙特卡洛估计（GAN-MC）将更加准确，并且在缺乏衍生品培训样本（基本的资产价格动力学）的情况下，没有范围的情况是未知的，或者是无标准的条件，或者无法实现分析。我们分析了模型的差异功能并验证定价模型的潜在价值，我们收集了现实世界市场衍生品数据，并表明我们的模型表现优于其他基于套利的定价模型和非参数机器学习模型。为了进行比较，我们使用黑色 - choles模型，普通最小二乘，径向基函数网络，多层感知回归，投影追求回归和仅蒙特卡洛模型来估计衍生物的价格。
We propose a parameter-free model for estimating the price or valuation of financial derivatives like options, forwards and futures using non-supervised learning networks and Monte Carlo. Although some arbitrage-based pricing formula performs greatly on derivatives pricing like Black-Scholes on option pricing, generative model-based Monte Carlo estimation(GAN-MC) will be more accurate and holds more generalizability when lack of training samples on derivatives, underlying asset's price dynamics are unknown or the no-arbitrage conditions can not be solved analytically. We analyze the variance reduction feature of our model and to validate the potential value of the pricing model, we collect real world market derivatives data and show that our model outperforms other arbitrage-based pricing models and non-parametric machine learning models. For comparison, we estimate the price of derivatives using Black-Scholes model, ordinary least squares, radial basis function networks, multilayer perception regression, projection pursuit regression and Monte Carlo only models.
