Computational finance using QuantLib-Python

Abstract

Given the complexity of over-the-counter derivatives and structured products, almost all derivatives pricing today is based on numerical methods. Large financial institutions typically have their own teams of developers who maintain state-of-the-art financial libraries, but until a few years ago, none of that sophistication was available for use in teaching and research. However, for the past decade, QuantLib, a reliable C++ open source library, has been available. In this article, the authors introduce QuantLib for pricing derivatives and document their experiences using its Python extension, QuantLib-Python, in their computational finance course at the Indian Institute of Management, Ahmedabad. The fact that QuantLib is available in Python makes it possible to harness the power of C++ with the ease of IPython notebooks for use in both the classroom and student projects.