Abrupt Climate Change: Lessons from Integrated Catastrophic Risk Management

This paper is an extended version of the talks "Uncertainty and Robust Solutions: Lessons from IIASA Case Studies on Catastrophic Risk Management and Economic Growth under Shocks" given on 12 June 2002 and "Sink Technologies and Climate Risk Management" given on 15 May 2002 at IIASA's Greenhouse Gas Initiative seminars (see web site: www.iiasa.ac.at/~marek/ggi/). Risks of disaster arise out of the combination of natural hazards and human activities. We argue that by divorcing the natural disaster issues from social and economic development, half of this disaster equation is ignored. The current pace of disaster development is undermining the markets and safety nets not only of developing countries. Far greater policy coherence is needed between economists, development planners, natural scientists and disaster managers in order to prevent catastrophic losses to human lives, livelihoods, and natural and economic assets. In this paper we present an integrated approach to catastrophic risk management that aims at more coherence and comprehensiveness. The models presented take into account spatial and temporal heterogeneity of catastrophes as well as institutional heterogeneity within a model of economic growth. Loss and gains profiles are functions of various strategies/requirements/goals of agents such as individuals, governments, producers, insurers and investors. GIS-based catastrophe models and stochastic optimization methods allow to guide policy analyses with respect to location specific risk exposures

Integrated Modeling of Spatial and Temporal Heterogeneities and Decisions Induced by Catastrophic Events

This paper discusses an integrated model capable of dealing with spatial and temporal heterogeneities induced by extreme events, in particular weather related catastrophes. The model can be used for quite different problems which take explicitly into account the specifics of catastrophic risks: highly mutually dependent losses, inherent capacity of information, the need for long-term perspectives (temporal heterogeneity) and geographically explicit analyses (spatial heterogeneity) with respect to losses and gains of various agents such as individuals, governments, farmers, products, consumers, insurers, investors, and their decisions on coping with risks. We illustrate emerging challenging decision-making problems with a case study of severe floods in a pilot region in the Upper Tisza River. Special attention is given to the evaluation of a flood loss-spreading program taking explicitly into account location specific distributions of agricultural and structural losses. This enables us to evaluate premiums, insurance coverage, and governmental compensation schemes minimizing, in a sense, the risk of locations to overpay actual losses, risks of bankruptcy/insolvency for insurers, and overcompensation of losses by the government. GIS-based catastrophe models and stochastic optimization methods are used to guide policy analysis with respect to location-specific risk exposures. We use special risk functions in order to convexity discontinuous insolvency constrains

Design of Flood-loss Sharing Programs in the Upper Tisza Region, Hungary: A dynamic multi-agent adaptive Monte Carlo approach

Losses from human-made and natural catastrophes are rapidly increasing. The main reason for this is the clustering of people and capital in hazard-prone areas as well as the creation of new hazard-prone areas, a phenomenon that may be aggravated by a lack of knowledge of the risks. This alarming human-induced tendency calls for new integrated approaches to catastrophic risk management. This paper demonstrates how flood catastrophe model and adaptive Monte Carlo optimization can be linked into an integrated Catastrophe Management Model to give insights on the feasibility of a flood management program and to assist in designing a robust program. As a part of integrated flood risk management, the proposed model takes into account the specifics of the catastrophic risk management: highly mutually dependent losses, the lack of information, the need for long-term perspectives and geographically explicit models, the involvement of various agents such as individuals, governments, insurers, reinsurers, and investors. Therefore, the integrated catastrophe management model turns out to be an important mitigation measure in comprehending catastrophes. As a concrete case we consider a pilot region of the Upper Tisza river, Hungary. Specifically, we analyze the demand of the region in a multipillar flood-loss sharing program involving a partial compensation by the central government, a voluntary private property insurance, a voluntary private risk-based insurance GIS-based catastrophe models and specific stochastic optimization methods are used to guide policy analysis with respect to location-specific risk exposures. To analyze the stability of the program, we use economically sound risk indicators

Data Challenges in High-Performance Risk Analytics

Risk Analytics is important to quantify, manage and analyse risks from the manufacturing to the financial setting. In this paper, the data challenges in the three stages of the high-performance risk analytics pipeline, namely risk modelling, portfolio risk management and dynamic financial analysis is presented

Catalogue and Toolbox of Risk Assessment and Management Tools

The ENHANCE project is concerned with analysing and working towards improved public-private partnerships for managing risks from natural hazards. An important issue for such partnerships is the methods, tools and processes available for assessing risk and risk management options. Risk analysis has long provided useful input to decision-making. At the same time, the field of risk analysis is in motion and an enhanced framing of risk analysis and risk management is being embraced following an iterative cycle organized around notions of learning, innovation and transformation. This broadened vision on risk analysis is a key issue for the ENHANCE project as well, which takes many and different perspectives on analysing, understanding, communicating and managing risk. This report lays out the status quo at the outset of the project regarding risk analytical tools, methods and data that are currently used by project partners in ENHANCE. The task overall develops a catalogue of existing risk assessment and management tools and methods to describe the concepts of iterative risk management and further sets up a toolbox, containing individual models and tools to be used by the case studies in their analyses. While work in the cases study, including methodological development, is in process, we find that ENHANCE partners and cases employ a multitude of models, tools and data ranging from impact analysis, different risk modelling techniques to various decision-support methods. A number of tools that encapsulate these methods are also available with the consortium. We suggest the tools and methods in use can be useful starting points for working towards a broader vision of iterative risk management. While the work so far, and this deliverable, have focussed on populating the technical stages of the risk analytical cycle (visually identified as the inner circle), we suggest in the next phase of ENHANCE, additional efforts should be dispensed to better understand adaptive management aspects associated with using these methods and tools, such as learning, innovation and transformation, which we exhibit visually in an outer circle. This report proceeds as follows: We start with laying out key elements of risk analysis and management in section 2, which also describes the new framing organized around the iterative risk-management concept. Methods for assessing risk and evaluating risk management are discussed in section 3. Then we consider methods, models and datasets that are in use in the ENHANCE case studies at the moment (section 4), before section 5 concludes. Finally and importantly, the annex lists more information on cases studies, for which detailed information was received from the project partners

Measurement and Pricing of Risk in Insurance Markets

The theory and practice of risk measurement provides a point of intersection between risk management, economic theories of choice under risk, financial economics, and actuarial pricing theory. This article provides a review of these interrelationships, from the perspective of an insurance company seeking to price the risks that it underwrites. We examine three distinct approaches to insurance risk pricing, all being contingent on the concept of risk measures. Risk measures can be interpreted as representations of risk orderings, as well as absolute (monetary) quantifiers of risk. The first approach can be called an “axiomatic” one, whereby the price for risks is calculated according to a functional determined by a set of desirable properties. The price of a risk is directly interpreted as a risk measure and may be induced by an economic theory of price under risk. The second approach consists in contextualizing the considerations of the risk bearer by embedding them in the market where risks are traded. Prices are calculated by equilibrium arguments, where each economic agent's optimization problem follows from the minimization of a risk measure. Finally, in the third approach, weaknesses of the equilibrium approach are addressed by invoking alternative valuation techniques, the leading paradigm among which is arbitrage pricing. Such models move the focus from individual decisiontakers to abstract market price systems and are thus more parsimonious in the amount of information that they require. In this context, risk measures, instead of characterizing individual agents, are used for determining the set of price systems that would be viable in a market

Global Changes: Facets of Robust Decisions

The aim of this paper is to provide an overview of existing concepts of robustness and to identify promising directions for coping with uncertainty and risks of global changes. Unlike statistical robustness, general decision problems may have rather different facets of robustness. In particular, a key issue is the sensitivity with respect to low-probability catastrophic events. That is, robust decisions in the presence of catastrophic events are fundamentally different from decisions ignoring them. Specifically, proper treatment of extreme catastrophic events requires new sets of feasible decisions, adjusted to risk performance indicators, and new spatial, social and temporal dimensions. The discussion is deliberately kept at a level comprehensible to a broad audience through the use of simple examples that can be extended to rather general models. In fact, these examples often illustrate fragments of models that are being developed at IIASA

Upgrading investment regulations in second pillar pension systems : a proposal for Colombia

The passivity of the demand for pension products is one of the striking features of mandatory pension systems. Consequently, the provision of multiple investment alternatives to households (multifund schemes) does not ensure that contributions are invested efficiently. In addition, despite the theoretical findings that short term return maximization is not conductive to long-term return maximization, the regulatory framework of pension fund management companies puts excessive emphasis on short-term maximization. Therefore, it is not obvious that typical regulatory framework of pension funds is conductive to optimal pensions. By establishing a set of default options on investment portfolios, this paper proposes a mechanism to align the incentives of the pension fund management companies with the long-term objectives of the contributors. The paper provides a methodology, which is subsequently applied to Colombia.Debt Markets,Emerging Markets,Financial Literacy,Mutual Funds,Investment and Investment Climate

Polyhedral Coherent Risk Measures, Portfolio Optimization and Investment Allocation Problems

The class of polyhedral coherent risk measures that could be used in decision- making under uncertainty is studied. Properties of these measures and invariant operations are considered. Portfolio optimization problems on the return -risk ratio using these risk measures are analyzed. The developed mathematical technique allows to solve large-scale portfolio problems by standard linear programming methods as an example of applications, investment allocation problems under risk of catastrophic floods are considered