Value at Risk of an Insurer's Portfolio

Publikasjonsdetaljer

This report describes the research related to use case 2 within pilot ♯6 of the FAME project: Embedding Climatic Predictions in Property Insurance Products. In a changing climate, it is expected that the frequency and/or intensity of nat ural disasters will change. This has impacts on several sectors of the economy, perhaps the most obvious being disaster-related damages and losses affecting the profitability of insurance companies. To explore and quantify this link, we analyze the impact of major natural disasters on the volatility of insurance stocks. Our working hypothesis is that the value at risk (VaR) of these stocks increases in the wake of a disaster as a result of an expected spike in claims, loss in value of assets held by insurers, etc. To test this hypothesis, we analyze data on the economic losses associated with major disasters in Europe since January 2000, and we build a statistical model to model their effect on the volatility (and thereby VaR) of the different stock prices. For storms, one of the two most damaging types of disasters in Europe, we also calculate projections of a loss index based on climate model simulations of daily maximum wind speeds, and we analyze how the regionally aggregated loss index is projected to change in the future. Combined with the statistical model that links storm losses to the VaR of insurance stocks, these loss index projections could give us an idea about climate-related changes in VaR. Our analysis, however, does not show a clear and unequivocal link between nat ural disasters and insurance stock volatility. While some major disasters are followed by a drop in the price of certain insurance stocks, the signal is inconsistent and not statistically significant. This is in line with other studies in the literature, which find a significant link only for some regions and some types of disasters, but it leads us to conclude that a robust projection of climate-related changes in VaR cannot be obtained with the data at hand.