In the aftermath of the recent financial crisis, the federal government has pursued significant regulatory reforms, including proposals to measure and monitor systemic risk. However, there is much debate about how this might be accomplished quantitatively and objectively—or whether this is even possible. A key issue is determining the appropriate trade-offs between risk and reward from a policy and social welfare perspective given the potential negative impact of crises. One of the first books to address the challenges of measuring statistical risk from a system-wide persepective, Quantifying Systemic Risk looks at the means of measuring systemic risk and explores alternative approaches. Among the topics discussed are the challenges of tying regulations to specific quantitative measures, the effects of learning and adaptation on the evolution of the market, and the distinction between the shocks that start a crisis and the mechanisms that enable it to grow.
The perception, assessment and management of risk are increasingly important core principles for determining the development of both policy and strategic responses to civil and environmental catastrophes. Whereas these principles were once confined to some areas of activity i.e. financial and insurance, they are now widely used in civil and environmental engineering. Comprehensive and readable, Civil and Environmental Risk: Mitigation and Control, provides readers with the mathematical tools and quantitative methods for determining the probability of a catastrophic event and mitigating and controlling the aftermath. With this book engineers develop the required skills for accurately assessing risk and formulating appropriate response strategies. The two part treatment starts with a clear and rigorous exposition of the quantitative risk assessment process, followed by self-contained chapters concerning applications. One of the first books to address both natural and human generated disasters, topics include events such as pandemic diseases, climate changes, major hurricanes, super earthquakes, mega tsunamis, volcanic eruptions, industrial accidents and terrorist attacks. Case studies appear at the end of the book allowing engineers to see how these principles are applied to scenarios such as a super hurricane or mega tsunamis, a reactor core melt down in a nuclear plant, a terrorist attack on the national electric grid, and an abrupt climate change brought about by a change in the ocean currents in the North Atlantic. Written by the current Chairman of the U.S. Nuclear Waste Technical Review Board, Environmental risk managers will find this reference a valuable and authoritative guide both in accurately calculating risk and its applications in their work. - Mathematical tools for calculating and Controlling Catastrophic Risk - Presents a systematic method for ranking the importance of societal threats - Includes both Natural and Industrial Catastrophes - Case studies cover such events as pandemic diseases, climate changes, major hurricanes, super earthquakes, mega tsunamis, volcanic eruptions, industrial accidents, and terrorist attacks
This book is used in many university courses for SOA Exam MLC preparation. The Fifth Edition is the official reference for CAS Exam LC. The Sixth Edition of this textbook presents a variety of stochastic models for the actuary to use in undertaking the analysis of risk. It is designed to be appropriate for use in a two or three semester university course in basic actuarial science. It was written with the SOA Exam MLC and CAS Exam LC in mind. Models are evaluated in a generic form with life contingencies included as one of many applications of the science. Students will find this book to be a valuable reference due to its easy-to-understand explanations and end-of-chapter exercises. In 2013 the Society of Actuaries announced a change to Exam MLC's format, incorporating 60% written answer questions and new standard notation and terminology to be used for the exam. There are several areas of expanded content in the Sixth Edition due to these changes. Six important changes to the Sixth Edition: WRITTEN-ANSWER EXAMPLES This edition offers additional written-answer examples in order to better prepare the reader for the new SOA eam format. NOTATION AND TERMINOLOGY CONFORMS TO EXAM MLC MQR 6 fully incorporates all standard notation and terminology for exam MLC, as detailed by the SOA in their document Notation and Terminology Used on Exam MLC. MULTI-STATE MODELS Extension of multi-state model representationt to almost all topics covered in the text. FOCUS ON NORTH AMERICAN MARKET AND ACTUARIAL PROFESSION This book is written specifically for the multi-disciplinary needs of the North American Market. This is reflected in both content and terminology. PROFIT TESTING, PARTICIPATING INSURANCE, AND UNIVERSAL LIFE MQR 6 contains an expanded treatment of these topics. THIELE'S EQUATION Additional applications of this important equation are presented, to more fully prepare the reader for exam day. A separate solutions manual with detailed solutions to all of the text exercises is also available. Please see the Related Items Tab for a direct link I selected Models for Quantifying Risk as the text for my class. Given that the syllabus had changed quite dramatically from prior years, I was looking for a text that would cover all the material in the new syllabus in a way that was rigorous, easy to understand, and would prepare students for the May 2012 MLC exam. To me, the text with the accompanying solutions manual does precisely that. --Jay Vadiveloo, Ph.D., FSA, MAAA, CFA, Math Department, University of Connecticut I found that the exposition of the material is thorough while the concepts are readily accessible and well illustrated with examples. The book was an invaluable source of practice problems when I was preparing for the Exam MLC. Studying from it enabled me to pass this exam." -- Dmitry Glotov, Math Department, University of Connecticut "This book is extremely well written and structured." -- Kate Li, Student, University of Connecticut "Overall, the text is thorough, understandable, and well-organized. The clear exposition and excellent use of examples will benefit the student and help her avoid 'missing the forest for the trees'. I was impressed by the quality and quantity of examples and exercises throughout the text; students will find this collection of problems sorted by topic valuable for their exam preparation. Overall, I strongly recommend the book." -- Kristin Moore, Ph.D., ASA, University of Michigan
Using the factor analysis of information risk (FAIR) methodology developed over ten years and adopted by corporations worldwide, Measuring and Managing Information Risk provides a proven and credible framework for understanding, measuring, and analyzing information risk of any size or complexity. Intended for organizations that need to either build a risk management program from the ground up or strengthen an existing one, this book provides a unique and fresh perspective on how to do a basic quantitative risk analysis. Covering such key areas as risk theory, risk calculation, scenario modeling, and communicating risk within the organization, Measuring and Managing Information Risk helps managers make better business decisions by understanding their organizational risk. - Uses factor analysis of information risk (FAIR) as a methodology for measuring and managing risk in any organization. - Carefully balances theory with practical applicability and relevant stories of successful implementation. - Includes examples from a wide variety of businesses and situations presented in an accessible writing style.
The rates of globalization and growth of the human population puts ever increasing pressure on the agricultural sector to intensify and grow more complex, and with this intensification comes an increased risk of outbreaks of infectious livestock diseases. At the same time, and for the same reasons, the detrimental effect that humans have on other species with which we share the environment has never been more apparent, as the current rates of species loss from ecological communities rival those of ancient mass extinction events. In order to find ways to lessen the effects of and eventually solve such problems we need ways to quantify the risks involved, something that can be difficult when for instance the sheer size or sensitivity of the systems makes practical experimentation unsuitable. For these situations mathematical models have become invaluable tools due to their flexibility and noninvasiveness. This thesis presents four works involving the quantification of risk in livestock epidemic and ecological contexts using mathematical models. Two of them deal with extinctions of species within model ecological communities, and how species interactions play a role in the identity of the lost species following perturbations to specific species (Papers I and II). The other two regard how the spatial layout of the underlying population of livestock premises affect the risk of foot and mouth disease outbreaks among farms in the USA, and how models of such outbreaks can be optimized to improve their usefulness (Papers III and IV). Ecological communities consist of species and the often intricate pattern of interactions between them. These interspecies connections can propagate effects caused by disturbances in one end of the network, through the community via the links, to other parts of the network. In some cases, a reduction in the abundance of one species can cause the extinction of a second species before the first species disappears, something called functional extinction. Despite this, many conservation efforts revolve around simply keeping populations of single species at a high enough level for their own survival. In a model setting, the study of Paper I explores and attempts to quantify how common such functional extinctions are in relation to the alternative outcome that a perturbed species itself becomes extinct. This is done by first constructing stable model food webs describing predator-prey interactions of up to 50 species, parameterized through allometric relationships between metabolic processes and body size. Then the smallest amount of extra mortality that can be applied to each and every species in the web before any species become extinct is determined. The study shows that in these model communities, more often than not (>80%) another species, rather than the species that is subjected to the additional mortality will be the one to become extinct first. The approach of Paper I is taken further in Paper II by applying the same methodology to ecological networks that include mixtures of both antagonistic (predator-prey) and mutualistic (e.g. pollination and seed dispersal) interactions. The results further reinforce the findings of Paper I, and show that ecological networks containing a mixture of antagonistic and mutualistic interactions are more sensitive to functional extinctions than purely antagonistic or purely mutualistic ones, an important finding considering the diversity of interaction types in natural systems. Furthermore, the type of species found to have the lowest threshold before becoming functionally extinct were those with a mixture of interaction types, such as pollinating insects. Both Paper I and II consolidate the notion that when doing conservation work it is important to have the entire community in mind by considering the population sizes that are viable from a multi-species perspective, rather than just focusing on the minimum population sizes that are viable for the individual species. In Papers III and IV the focus changes somewhat, from models of ecological systems to models of how infectious livestock disease spread between farms in spatially explicit contexts. For this kind of model, information about the spatial distribution of the hosts is of course crucial, but not always readily available. In the USA, the only available information about livestock premises demography is aggregated at the county scale, meaning that the spatial distribution of the premises within each county is unknown. However, a method exists to simulate realistic stochastic spatial configurations of premises using a set of predictor variables, such as topology, climate and roads. An alternative approach that have been used previously is to assume a uniformly random spatial distribution of premises within each county. But to what extent does the choice between these two methods affect the model’s evaluation of the risk of disease outbreaks? In Paper III, this is analyzed specifically for foot and mouth disease. Through simulated outbreaks and by looking at the reproductive ratio of the disease, the outbreak dynamics within the two different spatial configurations of premises are compared. The results show that there is a clear difference in the risk of outbreaks between them, with the non-uniform distributions showing a general pattern of higher outbreak risk. However this difference is dependent on the size and geographic location of the county that the outbreak start in with larger counties in the west of the US showing a stronger effect. When running numerical simulations with large scale models such as the one used in Paper III, a considerable amount of replication is usually necessary in order to account for the high degree of stochasticity inherent to the problem. Even further replication is required when performing sensitivity analyses of model parameters or when exploring different scenarios, for instance when trying to determine the optimal control strategy for a disease. For this reason, the amount and quality of results that can be produced by such studies can quickly become limited by the availability of computational resources. Finding ways to optimize the computations involved with regard to simulation time is therefore of great value as it can be directly related to the robustness of the results. In Paper IV, an efficient optimization method for the kind of kernel-based local disease spread model used in paper III is presented. The method revolves around constructing a grid structure that is overlaid on top of the farm landscape and dividing the infection process into two steps, first evaluating if any farms within one of the grid squares can become infected given an over-estimation of the probability of infection, and then only if so, evaluate actual infection of a subset of the farms within the receiving square. The method is compared to similar published methods and is shown to be more efficient in most cases, while also being easy to implement and understand. Furthermore, while other methods often involve approximations of the transmission process in order to improve computational speed, the method of Paper IV is shown to be exact. This is a major advantage, since with an approximative method the extent to which the results are affected by the simplification is unknown unless the effect of the approximation is explicitly quantified. In most cases, such quantification would require extensive simulations with the unsimplified approach, something which of course may not be feasible.
Singh introduces valuable techniques for weighing and evaluating alternatives in decision making with a focus on risk analysis for identifying, quantifying, and mitigating risks associated with construction projects.
Risk Analysis concerns itself with the quantification of risk, the modeling of identified risks and how to make decisions from those models. Quantitative risk analysis (QRA) using Monte Carlo simulation offers a powerful and precise method for dealing with the uncertainty and variability of a problem. By providing the building blocks the author guides the reader through the necessary steps to produce an accurate risk analysis model and offers general and specific techniques to cope with most modeling problems. A wide range of solved problems is used to illustrate these techniques and how they can be used together to solve otherwise complex problems.
Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Increasingly, such analyses are being applied to a wider class of systems in which problems such as lack of data, complexity of the systems, uncertainty about consequences, make a classical statistical analysis difficult or impossible. The authors discuss the fundamental notion of uncertainty, its relationship with probability, and the limits to the quantification of uncertainty. Drawing on extensive experience in the theory and applications of risk analysis, the authors focus on the conceptual and mathematical foundations underlying the quantification, interpretation and management of risk. They cover standard topics as well as important new subjects such as the use of expert judgement and uncertainty propagation. The relationship of risk analysis with decision making is highlighted in chapters on influence diagrams and decision theory. Finally, the difficulties of choosing metrics to quantify risk, and current regulatory frameworks are discussed.
