Bibliographic Details
| Title: |
Adaptive Gradient Descent Methods for Computing Implied Volatility |
| Authors: |
Lu, Yixiao, Wang, Yihong, Yang, Tinggan |
| Publication Year: |
2021 |
| Collection: |
Quantitative Finance |
| Subject Terms: |
Quantitative Finance - Computational Finance |
| More Details: |
In this paper, a new numerical method based on adaptive gradient descent optimizers is provided for computing the implied volatility from the Black-Scholes (B-S) option pricing model. It is shown that the new method is more accurate than the close form approximation. Compared with the Newton-Raphson method, the new method obtains a reliable rate of convergence and tends to be less sensitive to the beginning point.
Comment: Our implement of Newton-Raphson iteration has defects. After correcting the code implement, we find Newton-Raphson won't be non-convergent. See https://github.com/cloudy-sfu/Newton-Raphson-Implied-Volatility for details |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2108.07035 |
| Accession Number: |
edsarx.2108.07035 |
| Database: |
arXiv |
