Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4715
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorGOSWAMI, ANINDYAen_US
dc.contributor.authorTANKSALE, ATHARVAen_US
dc.date.accessioned2020-06-16T05:45:11Z-
dc.date.available2020-06-16T05:45:11Z-
dc.date.issued2020-05en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4715-
dc.description.abstractFair pricing of financial instruments is at the heart of market stability. Mispricing securities can lead traders into suffering massive losses which can indirectly affect the financial health of markets. It is thus, vital to be able to derive the fair price of traded financial instruments as this indirectly leads to optimized financial portfolios. This thesis aims to present a new approach to quantify the fair price of an Option contract given the underlying asset data. The models presented here would be constraint free when contrasted with traditional Option pricing models. We attempt to achieve the stated goal by leveraging advances in computational techniques.en_US
dc.language.isoenen_US
dc.subjectOption Pricingen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectFinanceen_US
dc.subjectMarketsen_US
dc.subjectDecision Treesen_US
dc.subject2020en_US
dc.titleA Data Driven Approach to Option Pricingen_US
dc.typeThesisen_US
dc.type.degreeBS-MSen_US
dc.contributor.departmentDept. of Mathematicsen_US
dc.contributor.registration20151140en_US
Appears in Collections:MS THESES

Files in This Item:
File Description SizeFormat 
MS_Thesis_Atharva_Tanksale_20151140.pdfMS Thesis1.14 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.