Despoina Makariou
Title
Prof. Dr.
Last Name
Makariou
First name
Despoina
Email
despoina.makariou@unisg.ch
Phone
+41 71 224 7973
10 results
Now showing 1 - 10 of 10
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters.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.Type:journal articleJournal:Risk Management and Insurance ReviewScopus© Citations 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A random forest based approach for predicting spreads in the primary catastrophe bond market.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.Type:journal articleJournal:Insurance: Mathematics and EconomicsScopus© Citations 33 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures.Type:journal articleJournal:RisksScopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mitigating systemic risk in catastrophe insurance: The role of human judgment in model diversification(2025-05-20); Orfanoudaki, AgniThis study aims to investigate the systemic risk implications of widespread reliance on standardized vendor catastrophe models in the insurance and reinsurance industry. While these models guide critical decisions around capital allocation and risk transfer, their uniform use may lead to homogenized risk assessments and increased systemic vulnerability. Using empirical analysis of model outputs, actual loss data, and human adjustments on the model output, we aim to evaluate whether expert interventions can diversify risk evaluations and mitigate systemic risk. Our findings will contribute to the growing discourse on human-in-the-loop decision-making, examining the value of expert judgment in counterbalancing algorithmic biases.Type:conference contribution - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Pricing dynamics in catastrophe bond issuance(2026-04-23); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Pricing dynamics in catastrophe bond issuance(2026-04-23); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Pricing dynamics in catastrophe bond issuance(2026-04-23); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A hybrid machine learning approach for carbon price forecasting(2025-03-11); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A hybrid machine learning approach for carbon price forecasting(2025-05-26); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Heterogeneous Market Timing: Evidence from the Catastrophe Bond Market(2024-06-25);