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Bayesian Inference in Markov Modulated Levy processes

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dc.contributor.advisor Overbeck, Ludger
dc.contributor.author SRIVASTAVA, TRIDASH
dc.date.accessioned 2024-05-20T09:46:55Z
dc.date.available 2024-05-20T09:46:55Z
dc.date.issued 2024-05
dc.identifier.citation 105 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/8885
dc.description.abstract Levy Process are used in finance for Asset Modeling and Risk Management. Markov Modulated Levy Process(MMLP) are a more flexible class of Stochastic Processes which capture phase changes arising in economies by allowing jumps in drift and volatility, linked to hidden states of a Markov chain. Theses models have been used to model option prices, renewable energy markets as well as for risk quantification. While Bayesian inference methods exists for simpler regime-switching models, we aim to extend it to more complex MMLPs. Our approach involves applying Bayesian estimation techniques to recover the hidden states and the parameters associated with each state of the Markov Chain. We propose Markov Chain Monte Carlo algorithms to perform Bayesian inference for MMLPs. This will allow for a more data-driven analysis of asset returns with regime shifts and jumps en_US
dc.language.iso en en_US
dc.subject Mathematics, financial mathematics en_US
dc.title Bayesian Inference in Markov Modulated Levy processes en_US
dc.type Thesis en_US
dc.description.embargo No Embargo en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Mathematics en_US
dc.contributor.registration 20191226 en_US


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  • MS THESES [1713]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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