Vinnere av aktuarprisen 2021

Prisen deles ut av Den Norske Aktuarforening, og er på totalt 25.000 kr. I år ble det to prisvinnere: Kim André Arntsen og Eivind Bjørnøy.

André Arntsen, ble uteksaminert ved Universitetet i Oslo vår 2020 og har skrevet en masteroppgave innen rentemodellering med tittelen «A functional approach to forward rate modelling». Veileder har vært Prof. Fred Espen Benth. Kim André jobber nå i Codan Forsikring.

Kort om oppgaven:

The aim of this thesis is twofold. First we introduce a Hilbert-valued multi-market two-factor forward curve model satisfying the Heath-Jarrow-Morton equation. Each forward curve consists of a shared source of noise and a specific one, where the noise sources are described by linear affine stochastic differential equations with Q-Wiener noise. Also, we give some insight into the no-arbitrage condition in terms of the covariance operator in the so-called Filipovic space. Secondly, we introduce a branch of statistics called Functional Data Analysis and perform an empirical study of the historical Norwegian yield curves as Nelson-Siegel smoothed government bond observations in a functional data analysis setting. We carry out a functional test of stationarity on the Norwegian yield curves.

Eivind Bjørnøy, ble uteksaminert ved Universitet i Bergen i juni 2020 og skrev masteroppgave med tittel "Markov-switching GARCH models with application to insurance claims". Veileder har vært Prof. II ved UiB, Antonello Maruotti. Eivind jobber nå som Associate i PWC.

Kort om oppgaven:

Attempting to model insurance claim data is usually done through fitting the data to a parametric distribution, irregardless of the time in which the claims occur. We attempt to view insurance claim data as a time series, and subsequently fit Markov-switching GARCH-models on the data. The methods we consider in this thesis are applied to a well-known insurance dataset through the use of the R-package MSGARCH (Ardia, et al., 2019). The possible model specifications, and the applicability of the models to the data are discussed. We compare the fitted models to some parametric distribution models suggested in a paper by Eling (2012) on the same dataset. We also consider some tail-risk measures, and the practical evaluation of the risk measures Value-at-Risk and Expected Shortfall for the data at hand

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