" We show that the U.S. insurance industry’s capacity to pay catastrophe losses is higher in 2020 than it was in 1997. Insurers could pay 98% of a $200 billion loss in 2020 in comparison to 81% in 1997."
"The integration of data in a geographic information system enables the visualization and spatialization of risk, but also each of its components."
"... DL [deep learning] does not outperform traditional ML [machine learning] models in the case of structured datasets with fixed-length feature vectors. Deep learning should be regarded as a powerful addition to the existing body of ML models instead of a one size fits all solution."
"There appears a gap in cyber risk modeling between engineering and insurance literature. This paper presents a novel model to capture these unique dynamics of cyber risk known from engineering and to model loss distributions based on industry loss data and a particular company's cybersecurity profile. The analysis leads to a new tool for allocating resources of the company between cybersecurity investments and loss-absorbing reserves."
"... blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general insurance liabilities."
"... capital requirements can promote growth by mitigating the risk of financial crises, possibly by encouraging... prudent lending. However, financial development and financial openness tend to mitigate the growth benefits of these policies, because of increased scope for (domestic and cross-border) regulatory arbitrage and, in the case of financial openness, greater opportunities to borrow abroad."
"Using variation across insurers within the same country, and across countries for the same insurance group, we show that market risk insurance via guaranteed return products is more prevalent in countries with more lax capital requirements. Moreover, we show that the interest rate exposure of insurance companies increased as interest rates declined in recent years, and this effect is more pronounced for companies with a larger share of guaranteed return products. "
"Experimental results over synthetic and real problems confirm the advantages of this inference approach in its ability to accurately recover the original noise and signal matrices, as well as the achieved performance improvement in comparison to other state of art MTGP approaches."
"The primary contribution of this paper is to present a unified Bayesian CAT bond pricing framework based on uncertainty quantification of catastrophes and interest rates. Our framework allows for complex beliefs about catastrophe risks to capture the distinct and common patterns in catastrophe occurrences, and when combined with stochastic interest rates, yields a unified asset pricing approach with informative expected risk premia."
"These parameters can be calibrated using public data. This new approach means not only to evaluate climate risks without picking any specific scenario but also allows to fill the gap between current one year approach of regulatory and economic capital models and the necessarily long-term view of climate risks by designing a framework to evaluate the resulting credit loss on each step (typically yearly) of the transition path. This new approach could prove instrumental in the 2022 context of central banks weighing the pros and cons of a climate capital charge."