Tame your paper monsters6/10/2023 ![]() ![]() ![]() We used the OECD quarterly data until 2025 and afterwards – the NGFS Delayed transition scenario values (obtained with REMIND-MAgPIE 3.0-4.4 model) that include a high chronical physical damage estimate as the future values of GDP in the EU and US. Based on the EIOPA insurance statistics and the data from Latvijas Banka's securities statistics, we assume that 24% of the impact exerted by economic development can be attributed to the Latvian market, 73% – to the EU market and 3% – to the US market. ![]() In capital evaluation, we consider the difference in premiums and claims over the previous period, as well as the economic development in Latvia, the EU and US. To evaluate the development in the solvency indicator for the insurance sector, it is necessary to model the dynamics of capital and the solvency capital requirement (SCR). One of 10 000 versions is displayed in Chart 2. As a result, the share of insured damages was calculated using the data on the share of insured properties, the natural disaster coverage and refusal rate. According to the LAA data, the refusal rate is 9–30% with the mean value of 18%. We also take into consideration that not all the claims are satisfied. These shares temporarily increase after large natural disasters following the observations made by the insurers about an increase in the number of purchased policies after such events. The respective coverage could increase to 60%, 40% and 10% by 2050. Therefore, we assumed that the insurance coverage for storms is included in 40% of policies, for rain floods – in 30% of policies and for spring floods – in 5% of policies. The LAA data show that natural disaster risks are included in 30% of real estate insurance policies and 1–6% of car insurances. We assumed that the initial share of insured properties might constitute 50% and it can further increase proportionally to GDP growth and more rapidly after significant natural disasters. We based our assumptions regarding the share of insured properties on the data of the ERGO security index and information from the Latvian Insurers Association (LAA). To evaluate the share of insured damages, we took into account the possible share of insured properties (in terms of value), risk coverage in insurance policies and the share of refused claims. To give a clearer idea, we present the results of one simulation in Chart 1. We applied the Monte Carlo simulation to assign natural disasters to particular quarters. We assumed that we can expect 1–2 large storms, 3–6 large rain floods and 7 spring floods until 2050. Our base values were 0.21% of GDP for storms, 0.06% of GDP for rain floods and 0.26% of GDP for spring floods.Īs a result, by 2050, damages can range from 1% of GDP if only mean damages are registered, to 7.6% of GDP if all three kinds of major natural disasters occur. the data on spring flood damages estimated by the LEGMC (2019) for 200-year floods: they can cause damages worth 209.1 million euro or 0.7% of GDP.įor the mean damage estimates, we used the LEGMC (2019) estimates and the estimates provided by the Ministry of Environmental Protection and Regional Development (VARAM) 2017, combining data on coastal and spring floods and wind and rain damages.the data on rain floods in Latgale (in 2017) from the EU Solidarity Fund data: they caused damages worth 380,5 million euro or 1.4% of GDP,.the data on Storm Erwin (in 2005) from the EM-DAT database: it caused damages worth 356 million euro or 2.5% of GDP,.Our maximum damage estimates are based on: In particular, we were interested in the mean damages that could be registered each year and the maximum damages that could be registered with a particular frequency/probability. We used the EM-DAT database to gain insights regarding natural disasters in Eastern and Northern Europe and a mix of local sources (disasters, wind speed, precipitation, and water levels in rivers) to evaluate the values needed for modelling. To evaluate the impact of natural disasters, first, we must know when we can expect them and how large they can be. Starting point: the damages caused by natural disasters and the share of insured damages ![]()
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