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:
|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
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|
|Collection:||Examensarbeten för masterexamen // Master Theses|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.