Assessing Systemic Risk of the European Insurance Industry

This paper investigates the systemic relevance of the insurance industry. We do it by analysing the systemic contribution of the insurance industry vis-á-vis other industries by applying three measures, namely the linear Granger causality test, conditional value at risk and marginal expected shortfall, to three groups, namely banks, insurers and non-financial companies listed in Europe over the last 14 years. Our evidence suggests that the insurance industry shows i) a persistent systemic relevance over time, ii) it plays a subordinate role in causing systemic risk compared to banks. In addition, iii) we do not find clear evidence on the higher systemic relevance of SIFI insurers compared to non-SIFIs

Insurance Activities and Systemic Risk

This paper investigates systemic risk in the insurance industry. We first analyze the systemic contribution of the insurance industry vis-`a-vis other industries by applying 3 measures, namely the linear Granger causality test, conditional value at risk and marginal expected shortfall, on 3 groups, namely banks, insurers and non-financial companies listed in Europe over the last 14 years. We then analyze the determinants of the systemic risk contribution within the insurance industry by using balance sheet level data in a broader sample. Our evidence suggests that i) the insurance industry shows a persistent systemic relevance over time and plays a subordinate role in causing systemic risk compared to banks, and that ii) within the industry, those insurers which engage more in non-insurance-related activities tend to pose more systemic risk. In addition, we are among the first to provide empirical evidence on the role of diversification as potential determinant of systemic risk in the insurance industry. Finally, we confirm that size is also a significant driver of systemic risk, whereas price-to-book ratio and leverage display counterintuitive results

Bayesian calibration of simple forest models with multiplicative mathematical structure: a case study with two Light Use Efficiency models in an alpine forest

Forest models are increasingly being used to study ecosystem functioning, through simulation of carbon fluxes and productivity in different biomes and plant functional types all over the world. Several forest models based on the concept of Light Use Efficiency (LUE) rely mostly on a simplified mathematical structure and empirical parameters, require little amount of data to be run, and their computations are usually fast. However, possible calibration issues must be investigated in order to ensure reliable results. Here we addressed the important issue of delayed convergence when calibrating LUE models, characterized by a multiplicative structure, with a Bayesian approach. We tested two models (Prelued and the Horn and Schulz (2011a) model), applying three Markov Chain Monte Carlo-based algorithms with different number of iterations, and different sets of prior parameter distributions with increasing information content. The results showed that recently proposed algorithms for adaptive calibration did not confer a clear advantage over the Metropolis–Hastings Random Walk algorithm for the forest models used here, and that a high number of iterations is required to stabilize in the convergence region. This can be partly explained by the multiplicative mathematical structure of the models, with high correlations between parameters, and by the use of empirical parameters with neither ecological nor physiological meaning. The information content of the prior distributions of the parameters did not play a major role in reaching convergence with a lower number of iterations. We conclude that there is a need for a more careful approach to calibration to solve potential problems when applying models characterized by a multiplicative mathematical structure. Moreover, the calibration proved time consuming and mathematically difficult, so advantages of using a computationally fast and user-friendly model were lost due to the calibration process needed to obtain reliable results

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

The uncertain climate footprint of wetlands under human pressure

Significant climate risks are associated with a positive carbon–temperature feedback in northern latitude carbon-rich ecosystems,making an accurate analysis of human impacts on the net greenhouse gas balance of wetlands a priority. Here, we provide a coherent assessment of the climate footprint of a network of wetland sites based on simultaneous and quasi-continuous ecosystem observations of CO2 and CH4 fluxes. Experimental areas are located both in natural and in managed wetlands and cover a wide range of climatic regions, ecosystem types, and management practices. Based on direct observations we predict that sustained CH4 emissions in natural ecosystems are in the long term (i.e., several centuries) typically offset by CO2 uptake, although with large spatiotemporal variability. Using a space-for-time analogy across ecological and climatic gradients, we represent the chronosequence from natural to managed conditions to quantify the “cost” of CH4 emissions for the benefit of net carbon sequestration. With a sustained pulse– response radiative forcing model, we found a significant increase in atmospheric forcing due to land management, in particular for wetland converted to cropland. Our results quantify the role of human activities on the climate footprint of northern wetlands and call for development of active mitigation strategies for managed wetlands and new guidelines of the Intergovernmental Panel on Climate Change (IPCC) accounting for both sustained CH4 emissions and cumulative CO2 exchange

Energy fluxes and evaporation mechanisms in an Atlantic blanket bog in southwestern Ireland

Water and energy fluxes control the development of northern peatlands and influence their carbon budget. Blanket bogs are peatlands that occur in temperate maritime regions where precipitation is much greater than evapotranspiration (ET). In this paper, five years (October 2002–September 2007) of ET and energy fluxes derived from eddy‐covariance measurements were analyzed in the context of the predicted climate change for Ireland. Monthly ET at the Glencar Atlantic blanket bog varied little, ranging between a minimum of 12 mm month−1 and a maximum of 56 mm month−1 over the five years, resulting in an annual ET average of 394 mm, with typical highest daily values of 2.5–3.0 mm. Compared to other peatland types, Glencar had lower summer ET and a lower ET/potential ET ratio, despite having higher precipitation and water table. The ET was limited not only by the low vapor pressure deficit and cool summer temperatures but also by the low cover of vascular plants and mosses (essential for transpiration). The energy budget was similar to other peatland types in terms of net radiation and sensible heat fluxes, but had lower latent and higher ground heat fluxes. A comparison among the five years suggests that the predicted climate change (greater winter precipitation, lower summer precipitation, and higher all year round temperatures) will probably increase winter ET, while the summer energy flux patterns will not be profoundly affected. However, if the frequency of summer rain events should diminish, the moss component of these ecosystems may become water stressed, ultimately leading to lower evapotranspiration

The impact of monetary policy interventions on the insurance industry

This paper investigates the effect of the conventional and unconventional (e.g. Quantitative Easing - QE) monetary policy intervention on the insurance industry. We first analyze the impact on the stock performances of 166 (re)insurers from the last QE programme launched by the European Central Bank (ECB) by constructing an event study around the announcement date. Then we enlarge the scope by looking at the monetary policy surprise effects on the same sample of (re)insurers over a timeframe of 12 years, also extending the analysis to the Credit Default Swaps (CDS) market. In the second part of the paper by building a set of balance sheet-based indices, we identify the characteristics of (re)insurers that determine sensitivity to monetary policy actions. Our evidences suggest that a single intervention extrapolated from the comprehensive strategy cannot be utilized to estimate the effect of monetary policy intervention on the market. With respect to the impact of monetary policies, we show how the effect of interventions changes over time. Expansionary monetary policy interventions, when generating an instantaneous reduction of interest rates, generated movement in stock prices in the same direction till September 2010. This effect turned positive during the European sovereign debt crisis. However, the effect faded away in 2014-2015. The pattern is confirmed by the impact on the CDS market. With regard to the determinants of these effects, our analysis suggests that sensitivity is mainly driven by asset allocation and in particular by exposure to fixed income assets

Systemic risk in insurance: towards a new approach

During the last IAIS Global Seminar in June 2017, IAIS disclosed the agenda for a gradual shift in the systemic risk assessment methodology from the current Entity Based Approach (EBA) to a new Activity Based Approach(ABA). The EBA, which was developed in the aftermath of the 2008/2009 financial crisis, defines a list of Global Systemically Important Insurers (G-SIIs) based on a pre-defined set of criteria related to the size of the institution. These G-SIIs are subject to additional regulatory requirements since their distress or disorderly failure would potentially cause significant disruption to the global financial system and economic activity. Even if size is still a needed element of a systemic risk assessment, the strong emphasis put on the too-big-to-fail approach in insurance, i.e. EBA, might be partially missing the underlying nature of systemic risk in insurance. Not only certain activities, including insurance activities such as life or non-life lines of business, but also common exposures or certain managerial practices such as leverage or funding structures, tend to contribute to systemic risk of insurers but are not covered by the current EBA (Berdin and Sottocornola, 2015). Therefore, we very much welcome the general development of the systemic risk assessment methodology, even if several important questions still need to be answered

INFOCARB: A Regional Scale Forest Carbon Inventory (Provincia Autonoma di Trento, Southern Italian Alps)

The aim of this inventory (acronym: INFOCARB) was to measure the organic carbon stored in the forest ecosystems of the Trento region (Provincia Autonoma di Trento, Northern Italy) in both above- and belowground pools, according to the Kyoto protocol and IPCC requirements. A total of 150 forest sampling points were selected on the entire regional area (6206 km2) with a statistical sampling approach, based on the timber volume as a proxy variable for a stratified sampling. Each sampling point was located with a GPS receiver and a 600 m2 circular plot was delimited around each point. Inside the plots, the biomass of trees, shrubs and herbaceous vegetation was measured, while litter was collected in systematically placed subplots. Topsoil (down to 30 cmdepth) was sampled with the excavationmethod on three systematically located pits, to determine the organic carbon content, the bulk density and the volume occupied by stones and roots. The inventory estimated the regional total carbon content of the forests as 71.9 5.2 Tg C, with an average carbon density of 207.01 14.5 Mg C ha1. The aboveground biomass and the soil had a similar carbon content, 43.2% and 44.6% of the total ecosystem carbon, respectively, whereas the root systems and the litter accounted for 9.6% and 2.6%, respectively. Due to the high inter-site variability, only weak statistical relationships were found between the soil carbon content and main ecosystem and climatic variables. However, when dividing the plots into different species-dominated forests, the beech sites differed significantly from the conifer sites in the carbon stock and the C/N ratio in the soil organic layers.JRC.H.2-Air and Climat