Salīdzināt metodes

	Probable Maximum Loss Estimation	Catastrophe Risk Modeling
Nozare	Disaster Studies	Disaster Studies
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2005	2005
Autors	Patricia Grossi & Howard Kunreuther; Kirsten Mitchell-Wallace et al.	Patricia Grossi & Howard Kunreuther; Kirsten Mitchell-Wallace et al.
Tips	Tail (return-period) loss metric read from a loss exceedance distribution	Event-based stochastic loss-simulation pipeline
Pirmavots	Grossi, P., & Kunreuther, H. (Eds.) (2005). Catastrophe Modeling: A New Approach to Managing Risk. Springer. ISBN: 9780387241050	Mitchell-Wallace, K., Jones, M., Hillier, J., & Foote, M. (Eds.) (2017). Natural Catastrophe Risk Management and Modelling: A Practitioner's Guide. Wiley-Blackwell. ISBN: 9781118906040
Citi nosaukumi	Probable Maximum Loss (PML), Return-Period Loss, Tail Loss Estimation, Catastrophe Value-at-Risk	Cat Modeling, Catastrophe Loss Modeling, Natural Catastrophe Modelling, Event-Based Loss Modeling
Saistītās	4	4
Kopsavilkums	Probable maximum loss (PML) estimation reads a tail loss, the loss associated with a chosen rare return period or exceedance probability, from the loss exceedance curve produced by a probabilistic risk or catastrophe model. Where average annual loss summarizes the mean of the loss distribution, PML characterizes its extreme: a 1-in-250-year PML is the loss level exceeded with one percent probability in a year (a 0.4 percent probability for 1-in-250). Patricia Grossi and Howard Kunreuther's 2005 volume sets out PML and the exceedance-probability curve as core catastrophe-model outputs, and Kirsten Mitchell-Wallace and colleagues' 2017 practitioner's guide details how the industry computes and uses PML, including the crucial distinction between occurrence and aggregate exceedance. PML is the metric that drives solvency capital, reinsurance purchase, risk appetite, and regulatory stress tests, because catastrophe risk is about surviving the rare bad year, not the average one. It is a percentile (value-at-risk) of the loss distribution and therefore inherits both the power and the fragility of tail estimation. Defining it precisely, return period, occurrence versus aggregate, and uncertainty, is essential to using it responsibly.	Catastrophe risk modeling estimates the probability distribution of losses from natural perils, such as hurricanes, earthquakes, and floods, by simulating large stochastic sets of plausible events and pushing each through hazard, exposure, vulnerability, and financial modules. It exists because catastrophe losses are rare, severe, and spatially correlated, so historical loss data alone cannot reveal the tail risk that insurers and governments must plan for; instead the model synthesizes thousands of years of possible events. Patricia Grossi and Howard Kunreuther's 2005 volume systematized the four-module structure and its use in managing risk, while Kirsten Mitchell-Wallace and colleagues' 2017 practitioner's guide is the standard modern reference for how the industry builds and uses these models. The defining output is the loss exceedance curve, from which average annual loss, return-period losses, and probable maximum loss are read. Catastrophe models are the engine of property catastrophe insurance, reinsurance pricing, and increasingly public disaster-risk finance. They turn the physics of rare hazards into the financial metrics needed to price and transfer extreme risk.