Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257987 
Year of Publication: 
2020
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 8 [Issue:] 2 [Article No.:] 32 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-14
Publisher: 
MDPI, Basel
Abstract: 
This note revisits the ideas of the so-called semiparametric methods that we consider to be very useful when applying machine learning in insurance. To this aim, we first recall the main essence of semiparametrics like the mixing of global and local estimation and the combining of explicit modeling with purely data adaptive inference. Then, we discuss stepwise approaches with different ways of integrating machine learning. Furthermore, for the modeling of prior knowledge, we introduce classes of distribution families for financial data. The proposed procedures are illustrated with data on stock returns for five companies of the Spanish value-weighted index IBEX35.
Subjects: 
analyzing financial data
machine learning
semiparametric modeling
VaR estimation
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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