Options pricing has always been an important mathematical problem in Quantitative Finance. Among the traditional models, the Black-Scholes-Metron (BSM) model was considered as one of the biggest breakthroughs. While the BSM model is widely popular, and has an appreciable accuracy, it makes certain assumptions. These assumptions limit the performance of such models, and can be overcome using the modern solutions. With time, as the computational capabilities increased, and researchers set out to address the limitations of the BSM model and other traditional models, Machine Learning (ML), and now Deep Learning (DL), have been increasingly used to develop better options pricing models. This is a relatively young domain, and there is a lot of scope in the field. ML/DL solutions leverage the potential of historical data in identifying patterns in price movement. Such models are quite accurate at detecting non-linearities in price variation, which is very important in increasing the accuracy of the predictions. Further, after developing the ML/DL models, we will compare their performances with the BSM model to evaluate the differences in error and accuracy. The historical dataset preparation will include data extraction from NSE India site and pre-processing the data on our end. We will have to write functions to find values of some important parameters, which are required by our models.
Create ML/DL models for options pricing for Indian financial markets.
- Multilayer Perceptron architecture-based models using LeakyReLU activation.
- A dataset of options prices from NSE India Historical Data Website. Currently, there are no easily accessible datasets for options in Indian markets.
- BSM-based model
Identifying and analyzing trading patterns unique to India to determine additional factors affecting prices.