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University of St. Gallen

Prof. Dr. Despoina Makariou
Research Area Catastrophe Risk and Machine Learning in Insurance

Despoina Makariou is an Assistant Professor of Risk Management and Insurance at the University of St. Gallen (HSG) and a Visiting Research Fellow in Statistics at the London School of Economics and Political Science (LSE). She has been awarded with a PhD in Statistics at the LSE for the development and application of statistical learning methods in insurance and finance followed by postdoctoral research at the Department of Philosophy, Logic, and Scientific Method, LSE. She holds a MSc in Insurance and Risk Management from Bayes (former Cass) Business School in London. She has been granted scholarships on grounds of academic excellence and research potential. She holds an Advanced Chartered Insurance Institute Diploma from the Chartered Insurance Institute in London and she has industry experience in the London Insurance Market in the areas of catastrophe risk modelling, and pricing of (re)insurance and alternative risk transfer products. Her research interests lie on machine learning in insurance and finance, insurance-linked securitisation, and catastrophe risk management.

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Research Focus
  • Machine learning in insurance and finance
  • Pricing aspects in insurance-linked securitisation
  • Catastrophe risk management (man-made and natural disasters)

Education

09/2017 – 05/2022
Statistics Department, London School of Economics ▪ London, UK
PhD in Statistics, Research Group: Probability in Finance and Insurance

09/2012 – 09/2013
Cass Business School ▪ London, UK
MSc in Insurance & Risk Management

09/2008 – 07/2012
School of Economics and Regional Studies, University of Macedonia ▪ Thessaloniki, Greece
BA in Economics and International Business

Employment

08/2022 – Present
Institute of Insurance Economics, University of St. Gallen ▪ St. Gallen, Switzerland

08/2022– Present
Statistics Department, London School of Economics ▪ London, UK
Visiting Research Fellow

01/2022 – 07/2022
Department of Philosophy, Logic and Scientific Method, London School of Economics ▪ London, UK
Postdoctoral Researcher

10/2017 – 05/2022
Statistics Department, London School of Economics ▪ London, UK
PhD Candidate in Statistics

01/2016 – 07/2017
CNA Hardy ▪ London, UK
Exposure and Catastrophe Risk Management
Business Lines: Terrorism, Direct and Facultative Reinsurance (D&F) Property, Power Generation

11/2014 – 01/2016
Argo Group ▪ London, UK
Risk Modelling
Business Lines: Terrorism, Property (D&F, Binding Authorities, Real Estate Owned), Cargo, Onshore

07/2014 – 10/2014
Lancashire Insurance ▪ London, UK
Catastrophe Risk Modelling
Business Lines: Terrorism, Political Violence, Offshore Energy, Onshore Energy

get_app George Tzougas, Despoina Makariou
Journal paper
We introduce a multivariate Poisson-Generalized Inverse Gaussian regression model with varying dispersion and shape for modeling different types of claims and their associated counts in nonlife insurance. The multivariate Poisson-Generalized Inverse Gaussian regression model is a general class of models which, under the approach adopted herein, allows us to account for overdispersion and positive correlation between the claim count responses in a flexible manner. For expository purposes, we consider the bivariate Poisson-Generalized Inverse Gaussian with regression structures on the mean, dispersion, and shape parameters. The model's implementation is demonstrated by using bodily injury and property damage claim count data from a European motor insurer. The parameters of the model are estimated via the Expectation-Maximization algorithm which is computationally tractable and is shown to have a satisfactory performance.

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get_app Despoina Makariou, Pauline Barrieu, Yining Chen
Journal paper
We introduce a random forest approach to enable spreads’ prediction in the primary catastrophe bond market. In a purely predictive framework, we assess the importance of catastrophe spread predictors using permutation and minimal depth methods. The whole population of non-life catastrophe bonds issued from December 2009 to May 2018 is used. We find that random forest has at least as good prediction performance as our benchmark-linear regression in the temporal context, and better prediction performance in the non-temporal one. Random forest also performs better than the benchmark when multiple predictors are excluded in accordance with the importance rankings or at random, which indicates that random forest extracts information from existing predictors more effectively and captures interactions better without the need to specify them. The results of random forest, in terms of prediction accuracy and the minimal depth importance are stable. There is only a small divergence between the drivers of catastrophe bond spread in the predictive versus explanatory framework. We believe that the usage of random forest can speed up investment decisions in the catastrophe bond industry both for would-be issuers and investors.

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get_app Despoina Makariou, Pauline Barrieu, George Tzougas
Journal paper
The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision-making context.

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Institute of Insurance Economics
at the University of St.Gallen (I.VW)

Prof. Dr. Despoina Makariou
Tannenstrasse 19
9000 St.Gallen
+41 71 224 7973

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