Systemic and Extreme Risks:Ways Forward for a Joint Framework

Systemic risk is getting increasing attention in the science as well as popular press not to the least due to the growing complexity of the world as well as increasing data availability. The aim of this paper is to discuss selected topics in extreme and systemic risk modelling, measuring and management approaches.We find that from a purely quantitative modelling perspective, single and systemic risk assessment can be jointly performed via the concept of copulas and therefore can be embedded within an integrated framework without major difficulties. Consequently, we see single and systemic risks as not independent but indivisible which have to be assessed jointly. However, from a risk measure perspective we see some important differences as single risk measures focus on probability distributions while systemic risk measures focus on dependency measures. Hence, we call for ensembles of risk measures which should be a superior approach for studying single and systemic risks in complex networks as different events can cause systemic risk to realize (e.g. too big to fail, too interconnected to fail, keystone species etc.). From a risk management perspective, we conclude that the inclusion of human agents causes a fundamental difference in the management of systemic risks compared to other systems as their decisions are contingent and may cause unpredictable shifts due to mutual uncertainties that can evolve. Consequently, we argue for an iterative risk management approach similar to the call from climate change and adaptation science, for example discussed in the various IPCC reports. Last but not least, the idea of collective responsibility echoes the need to target risks that threaten whole societies. That such risks are reduced is foremost in the public interest and we therefore call for an institutional change that enables the effective handling of it in the future

Modelling Dependent Risk With Copulas: An Application On Flooding Using Agent-Based Modelling

In the present work we introduce a copula approach to model dependencies between risks in large scale networks and show how this could be used to avoid underestimation of extreme events. Furthermore, we apply the approach within an agent based model to determine the macroeconomic consequences due to flood events. We show that without a copula approach only average annual losses on the country level would be available. However, with the copula approach, which includes the estimation of basin scale loss distribution through catastrophe modelling, exposure estimation through Corine land cover mapping, assessment of appropriate copulas and parameter estimation, including a algorithm to couple coupled basins as well as an upscaling procedure to the country level, the whole risk spectrum can be, for the first time on this scale, estimated. The direct loss estimates from the copula approach, separated into different risk bearers, are used to build a damage scenario generator which gives the input for the agent based model. The agent based model in turn assesses the additional indirect losses due to the event which can be much larger than the direct losses alone

Modelling Macroeconomic Effects of Natural Disaster Risk: A Large Scale Agent Based Modelling Approach Using Copulas

We introduce a copula approach to model dependencies between risks and show how this could be used to avoid underestimation of extreme events in large-scale risk assessments. We apply the approach within an extensive agent based model to determine the macroeconomic consequences due to catastrophic events. The agent based approach is capable of modelling an entire national economy with all sectors, including households, firms and banks. It is based on an input-output model with 64 industries where all goods and services are produced endogenously. We show that without a copula approach only average annual losses on the country level would be available which limits analysis on long term effects. However, with the copula approach, which includes the estimation of basin scale loss distribution through catastrophe modelling, exposure estimation through Corine land cover mapping, assessment of appropriate copulas and parameter estimation, including an algorithm to couple coupled basins as well as an upscaling procedure to the country level, the whole risk spectrum can be estimated. The direct loss estimates from the copula approach, separated into different risk bearers, are used to build a damage scenario generator which gives the input for the agent based model. The agent based model in turn assesses the additional indirect losses due to the event which can be much larger than the direct losses alone. The agent based model is calibrated to the case of Austria at a scale 1: 10, e.g. with hundreds of thousands of agents and the agents are calibrated according to micro data, including business information, balance-sheets, and income statements. We show that there can be severe effects due to large scale natural disaster events through different transmission channels, even leading to systemic risks. This detailed information should be useful for determining risk management options on various scales

Evaluating Partnerships to Enhance Disaster Risk Management using Multi-Criteria Analysis: An Application at the Pan-European Level

Disaster risk is increasingly recognized as a major development challenge. Recent calls emphasize the need to proactively engage in disaster risk reduction, as well as to establish new partnerships between private and public sector entities in order to decrease current and future risks. Very often such potential partnerships have to meet different objectives reflecting on the priorities of stakeholders involved. Consequently, potential partnerships need to be assessed on multiple criteria to determine weakest links and greatest threats in collaboration. This paper takes a supranational multi-sector partnership perspective, and considers possible ways to enhance disaster risk management in the European Union by better coordination between the European Union Solidarity Fund, risk reduction efforts, and insurance mechanisms. Based on flood risk estimates we employ a risk-layer approach to determine set of options for new partnerships and test them in a high-level workshop via a novel cardinal ranking based multi-criteria approach. Whilst transformative changes receive good overall scores, we also find that the incorporation of risk into budget planning is an essential condition for successful partnerships

Assessing the Macroeconomic Impacts of Natural Disasters: Are There Any?

There is an ongoing debate on whether disasters cause significant macroeconomic impacts and are truly a potential impediment to economic development. This paper aims to assess whether and by what mechanisms disasters have the potential to cause significant GDP impacts. The analysis first studies the couterfactual versus the observed gross domestic product. Second, the analysis assesses disaster impact as a function of hazard, exposure of assets, and, importantly, vulnerability. In a medium-term analysis (up to 5 years after the disaster event), comparing counterfactual with observed gross domestic product, the authors find that natural disasters on average can lead to negative consequences. Although the negative effects may be small, they can become more pronounced depending mainly on the size of the shock. Furthermore, the authors test a large number of vulnerability predictors and find that greater aid and inflows of remittances reduce adverse macroeconomic consequences, and that direct losses appear most critical

User Interface of the CatSim Model and Practical Guidelines

This user manual should help performing a CatSim analysis for a specific country. The policy applications of the CatSim tool can provide insights on the development and use of indicators of vulnerability, resilience, coping capacity and other concepts important for policy interventions with regard to disasters and other global-change phenomena. The CatSim tool relies on quantitative indicators. Risk is estimated making use of historical statistics and exposure; financial resilience is estimated with an index of observations on the financial preparedness of the government; financial vulnerability is a composite of the two and is measured in terms of the financing gap. Clearly financial vulnerability of the public sector presents only one aspect, albeit an import one, of vulnerability to natural hazards. Other indicators are necessary in order to complement this concept. Furthermore, participation and transparency in the design, estimation and use of vulnerability indicators is essential for their legitimacy. As there is a substantial degree of uncertainty in estimates of disasters risks and financial vulnerability, it is important that users have full participation in their design, estimation and use. CatSim has been created as a participatory, interactive tool for building capacity of policy makers by sensitizing them to the tradeoffs inherent in planning for disasters

Changes in fiscal risk against natural disasters due to Covid-19

The coronavirus pandemic caused serious social and economic impacts around the world. Governments implemented massive fiscal stimulus and protection packages to counteract these severe consequences which lead them into weak fiscal positions and elevated debt. This affects the fiscal risk to natural hazards governments are exposed to as well. To shed light on this issue we compare fiscal risk due to natural disaster events pre-Covid and for today to indicate the magnitude of change. This is done by applying the so-called CatSim model which combines natural disaster risk and corresponding losses a government is exposed to with financial resources it has available to finance them. While only indicative due to data limitations our results can be interpreted as a warning call to not underestimate disaster risks that can realize any moment and that will be much more difficult to be efficiently responded to compared to the pre-Covid era. Especially the poor are now in a significant weaker position than before. We suggest some possible ways forward how to enable a more integrated perspective and to track progress of fiscal risks over time

A methodological framework to operationalize Climate Risk Management: Managing sovereign climate-related extreme event risk in Austria

Despite considerable uncertainties regarding the exact contribution of anthropogenic climate change to disaster risk, rising losses from extreme events have highlighted the need to comprehensively address climate-related risk. This requires linking climate adaptation to disaster risk management (DRM), leading to what has been broadly referred to as climate risk management (CRM). While this concept has received attention in debate, important gaps remain in terms of operationalizing it with applicable methods and tools for specific risks and decision-contexts. By developing and applying a methodological approach to CRM in the decision context of sovereign risk (flooding) in Austria we test the usefulness of CRM, and based on these insights, inform applications in other decision contexts. Our methodological approach builds on multiple lines of evidence and methods. These comprise of a broad stakeholder engagement process, empirical analysis of public budgets, and risk-focused economic modelling. We find that a CRM framework is able to inform instrumental as well as reflexive and participatory debate in practice. Due to the complex interaction of social-ecological systems with climate risks, and taking into account the likelihood of future contingent climate-related fiscal liabilities increasing substantially as a result of socioeconomic developments and climate change, we identify the need for advanced learning processes and iterative updates of CRM management plans. We suggest that strategies comprising a portfolio of policy measures to reduce and manage climate-related risks are particularly effective if they tailor individual instruments to the specific requirements of different risk layers. (authors' abstract