Michał Balcerek

Julia Kończal, Michał Balcerek, Krzysztof Burnecki

In recent years, the growing frequency and severity of natural disasters have increased the need for effective tools to manage catastrophe risk. Catastrophe (CAT) bonds allow the transfer of part of this risk to investors, offering an alternative to traditional reinsurance. This paper examines the role of climate variability in CAT bond pricing and evaluates the predictive performance of various machine learning models in forecasting CAT bond coupons. We combine features typically used in the literature with a new set of climate indicators, including Oceanic Ni{ñ}o Index, Arctic Oscillation, North Atlantic Oscillation, Outgoing Longwave Radiation, Pacific-North American pattern, Pacific Decadal Oscillation, Southern Oscillation Index, and sea surface temperatures. We compare the performance of linear regression with several machine learning algorithms, such as random forest, gradient boosting, extremely randomized trees, and extreme gradient boosting. Our results show that including climate-related variables improves predictive accuracy across all models, with extremely randomized trees achieving the lowest root mean squared error (RMSE). These findings suggest that large-scale climate variability has a measurable influence on CAT bond pricing and that machine learning methods can effectively capture these complex relationships.

Jakub Malinowski, Marcin Kostrzewa, Michał Balcerek et al.

Change point detection has become an important part of the analysis of the single-particle tracking data, as it allows one to identify moments, in which the motion patterns of observed particles undergo significant changes. The segmentation of diffusive trajectories based on those moments may provide insight into various phenomena in soft condensed matter and biological physics. In this paper, we propose CINNAMON, a hybrid approach to classifying single-particle tracking trajectories, detecting change points within them, and estimating diffusion parameters in the segments between the change points. Our method is based on a combination of neural networks, feature-based machine learning, and statistical techniques. It has been benchmarked in the second Anomalous Diffusion Challenge. The method offers a high level of interpretability due to its analytical and feature-based components. A potential use of features from topological data analysis is also discussed.