论文标题
神经网络的波动率模型校准了直接方法和间接方法之间的比较
Volatility model calibration with neural networks a comparison between direct and indirect methods
论文作者
论文摘要
在最近的一篇论文“深度学习波动”中，提出了一种针对粗糙波动率模型的快速2步深度校准算法：在第一步中，神经网络从模型参数到隐含的挥发性的时间消耗映射是由神经网络学到的，在第二步中，第二步标准标准solver技术用于找到最佳模型参数。 在我们的论文中，我们将这些结果与另一种直接方法进行了比较，在该方法中，神经网络近似地估算了来自市场隐含波动到建模参数的映射，而无需额外的求解器步骤。使用美白过程和对[0,1]的目标参数的投影，以便能够使用Sigmoid类型输出功能，我们发现直接方法在“深度学习波动率”中发表的数据集和方法的两步胜过。 为了实现，我们使用开源Tensorflow 2库。该论文应被理解为神经网络技术的技术比较，而不是有条不紊的新安萨兹。
In a recent paper "Deep Learning Volatility" a fast 2-step deep calibration algorithm for rough volatility models was proposed: in the first step the time consuming mapping from the model parameter to the implied volatilities is learned by a neural network and in the second step standard solver techniques are used to find the best model parameter. In our paper we compare these results with an alternative direct approach where the the mapping from market implied volatilities to model parameters is approximated by the neural network, without the need for an extra solver step. Using a whitening procedure and a projection of the target parameter to [0,1], in order to be able to use a sigmoid type output function we found that the direct approach outperforms the two-step one for the data sets and methods published in "Deep Learning Volatility". For our implementation we use the open source tensorflow 2 library. The paper should be understood as a technical comparison of neural network techniques and not as an methodically new Ansatz.
