CHAOUACHI Wassim
Structured products are becoming more and more important in the world of investment banking, and more and more investors are incorporating this type of asset in their portfolios. There are various types of structured products, suitable for different investor profiles, including individual.
The objective of this article is to introduce new pricing methods other than Monte Carlo methods to speed up the computation time of some structured products called exotic products. We will show how we reduce computation time from 371 days to 2.11 seconds keeping a very accurate precision. First we will introduce the financial products we price, for that we will describe the environment. Second, knowing that we never used machine learning technics to price products at HSBC, one of the parts of the projects was a proof of concept on vanilla products to see if we can apply such techniques (Machine Learning) on more complex products such as Exotics. Third, We will introduce a new deep learning model for non linear interpolation to price Exotic products: Autocallables for Mono-Underlying then on Multi-Underlyings .The last part of this project was the back-testing of our model on the last months .
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