Computational Finance Using QuantLib-Python


Given the complexity of over-the-counter derivatives and structured merchandise, nearly all derivatives pricing these days is predicated on numerical methods. Massive financial establishments usually 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 accessible to be used in teaching and analysis. But, for the past decade, QuantLib, a reliable C++ open supply library, has been offered. 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 out there in Python makes it doable to harness the ability of C++ with the ease of IPython notebooks to be used in both the classroom and student comes.

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