A shapley value approach to pricing climate risks

This paper prices the risk of climate change by calculating a lower bound for the price of a virtual insurance policy against climate risks associated with the business as usual (BAU) emissions path. In analogy with ordinary insurance pricing, this price depends on the current risk to which society is exposed on the BAU emissions path and on a second emissions path reflecting risks that society is willing to take. The difference in expected damages on these two paths is the price which a risk neutral insurer would charge for the risk swap excluding transaction costs and profits, and it is also a lower bound on society's willingness to pay for this swap. The price is computed by (1) identifying a probabilistic risk constraint that society accepts, (2) computing an optimal emissions path satisfying that constraint using an abatement cost function, (3) computing the extra expected damages from the business as usual path, above those of the risk constrained path, and (4) apportioning those excess damages over the emissions per ton in the various time periods. The calculations follow the 2010 US government social cost of carbon analysis, and are done with DICE2009

Climate Change and Risk Management: Challenges for Insurance, Adaptation, and Loss Estimation

Adapting to climate change will not only require responding to the physical effects of global warming, but will also require adapting the way we conceptualize, measure, and manage risks. Climate change is creating new risks, altering the risks we already face, and also, importantly, impacting the interdependencies between these risks. In this paper we focus on three particular phenomena of climate related risks that will require a change in our thinking about risk management: global micro-correlations, fat tails, and tail dependence. Consideration of these phenomena will be particularly important for natural disaster insurance, as they call into question traditional methods of securitization and diversification.tail dependence, micro-correlations, fat tails, damage distributions, climate change

Climate Change Uncertainty Quantification: Lessons Learned from the Joint EU-USNRC Project on Uncertainty Analysis of Probabilistic Accident Consequence Codes

Between 1990 and 2000 the U.S. Nuclear Regulatory Commission and the Commission of the European Communities conducted a joint uncertainty analysis of accident consequences for nuclear power plants. This study remains a benchmark for uncertainty analysis of large models involving high risks with high public visibility, and where substantial uncertainty exists. The study set standards with regard to structured expert judgment, performance assessment, dependence elicitation and modeling and uncertainty propagation of high dimensional distributions with complex dependence. The integrated assessment models for the economic effects of climate change also involve high risks and large uncertainties, and interest in conducting a proper uncertainty analysis is growing. This article reviews the EU-USNRC effort and extracts lessons learned, with a view toward informing a comparable effort for the economic effects of climate change.uncertainty analysis, expert judgment, expert elicitation, probabilistic inversion, dependence modeling, nuclear safety

The Unholy Trinity: Fat Tails, Tail Dependence, and Micro-Correlations

Recent events in the financial and insurance markets, as well as the looming challenges of a globally changing climate point to the need to re-think the ways in which we measure and manage catastrophic and dependent risks. Management can only be as good as our measurement tools. To that end, this paper outlines detection, measurement, and analysis strategies for fat-tailed risks, tail dependent risks, and risks characterized by micro-correlations. A simple model of insurance demand and supply is used to illustrate the difficulties in insuring risks characterized by these phenomena. Policy implications are discussed.risk, fat tails, tail dependence, micro-correlations, insurance, natural disasters

EEMCS final report for the causal modeling for air transport safety (CATS) project

This document reports on the work realized by the DIAM in relation to the completion of the CATS model as presented in Figure 1.6 and tries to explain some of the steps taken for its completion. The project spans over a period of time of three years. Intermediate reports have been presented throughout the project’s progress. These are presented in Appendix 1. In this report the continuous‐discrete distribution‐free BBNs are briefly discussed. The human reliability models developed for dealing with dependence in the model variables are described and the software application UniNet is presente

Precursor Analysis for Offshore Oil and Gas Drilling: From Prescriptive to Risk-Informed Regulation

The Oil Spill Commission’s chartered mission—to “develop options to guard against … any oil spills associated with offshore drilling in the future” (National Commission 2010)—presents a major challenge: how to reduce the risk of low-frequency oil spill events, and especially high-consequence events like the Deepwater Horizon accident, when historical experience contains few oil spills of material scale and none approaching the significance of the Deepwater Horizon. In this paper, we consider precursor analysis as an answer to this challenge, addressing first its development and use in nuclear reactor regulation and then its applicability to offshore oil and gas drilling. We find that the nature of offshore drilling risks, the operating information obtainable by the regulator, and the learning curve provided by 30 years of nuclear experience make precursor analysis a promising option available to the U.S. Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE) to bring cost-effective, risk-informed oversight to bear on the threat of catastrophic oil spills.catastrophic oil spills, quantitative risk analysis, risk-informed regulation

Convolution of Scale Invariant Continuous Ranked Probability Scores for Testing Experts' Statistical Accuracy

Computable solutions for expectations of Continuous Ranked Probability Scores are presented. After deriving a scale invariant version of these scores, a closed form for the convolutions of scores is presented. This closed form enables the testing experts' statistical accuracy. Results are compared with tests using a familiar Chi-square goodness of fit test using a recent data set of 6,761 expert probabilistic forecasts for which true values are known