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A recurrent neural network approach to quantification of risks surrounding the Swedish property market
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

As the real estate market plays a central role in a countries financial situation, as a life insurer, a bank and a property developer, Skandia wants a method for better assessing the risks connected to the real estate market. The goal of this paper is to increase the understanding of property market risk and its covariate risks and to conduct an analysis of how a fall in real estate prices could affect Skandia’s exposed assets.This paper explores a recurrent neural network model with the aim of quantifying identified risk factors using exogenous data. The recurrent neural network model is compared to a vector autoregressive model with exogenous inputs that represent economic conditions.The results of this paper are inconclusive as to which method that produces the most accurate model under the specified settings. The recurrent neural network approach produces what seem to be better results in out-of-sample validation but both the recurrent neural network model and the vector autoregressive model fail to capture the hypothesized relationship between the exogenous and modeled variables. However producing results that does not fit previous assumptions, further research into artificial neural networks and tests with additional variables and longer sample series for calibration is suggested as the model preconditions are promising.

Place, publisher, year, edition, pages
2016. , 60 p.
Keyword [en]
Artificial neural networks, Machine learning, RNN
National Category
URN: urn:nbn:se:umu:diva-126192OAI: diva2:1014895
External cooperation
Skandia Liv
Educational program
Master of Science in Engineering and Management
Available from: 2016-10-06 Created: 2016-10-03 Last updated: 2016-10-06Bibliographically approved

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