Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/19435
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dc.contributor.authorVarma, Jayanth R.
dc.contributor.authorVirmani, Vineet
dc.date.accessioned2017-06-22T05:08:37Z
dc.date.available2017-06-22T05:08:37Z
dc.date.issued2016
dc.identifier.citationVarma J.R., Virmani V. (2016). Computational finance using QuantLib-Python. Computing in Science and Engineering, 18(2), 78-88.en_US
dc.identifier.urihttp://hdl.handle.net/11718/19435
dc.description.abstractGiven 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.en_US
dc.language.isoen_USen_US
dc.publisherIEEE Computer Societyen_US
dc.subjectDerivatives pricingen_US
dc.subjectFinancial engineeringen_US
dc.subjectOpen source computingen_US
dc.subjectPythonen_US
dc.subjectQuantLiben_US
dc.subjectScientific computingen_US
dc.titleComputational finance using QuantLib-Pythonen_US
dc.typeArticleen_US
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