Latent State Estimation for Financial Time Series, Estimating Financial Health with MCMC Methods and Particle Filters

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/300844
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Type: Examensarbete för masterexamen
Title: Latent State Estimation for Financial Time Series, Estimating Financial Health with MCMC Methods and Particle Filters
Authors: Hermansson, Erik
Abstract: Financial Modelling allows for prudent decision making for individual business owners and other stakeholders. The Financial Health can be seen as an underlying measure which governs the companies ability to meet its obligations and make profits. Therefore Financial Health is linked to the company’s cash flow which can readily be observed. We consider the Financial Health as a dynamic latent state and infer it from the cash flow. We are estimating this latent state under the Bayesian paradigm to take stylized properties of the cash flow into account, using a Particle Filter as part of a Monte Carlo method to sample the posterior distribution of latent state and model parameters. We investigate the performance of this approach on a real data set consisting of real cash flow from small Swedish businesses.
Keywords: Bayesian Inference, Hidden Markov Models, Particle Filter, Financial Health
Issue Date: 2020
Publisher: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper
URI: https://hdl.handle.net/20.500.12380/300844
Collection:Examensarbeten för masterexamen // Master Theses



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