Posts Tagged ‘Models’
Guillermo Franco, Head of Catastrophe Risk Research - EMEA
It seems reasonable to expect a degree of uncertainty in catastrophe model results. It is not uncommon, however, for models to produce results that differ by several factors. In order to assess how much of this uncertainty is epistemic, due to our incomplete knowledge of the physical phenomena involved, this existing uncertainty needs to be quantified.
Guy Carpenter’s MetaRisk® Reserve™ Awarded Patent as Breakthrough Innovation in Reserve Risk Modeling
Guy Carpenter & Company has been awarded a patent for MetaRisk Reserve by the U.S. Patent Office for creating a unique and easy-to-use predictive model for the analysis of reserve risk.
Flood risk is poorly modeled at a global level, particularly in developing countries where flooding is a regular occurrence.
Floods are among the most destructive hazards and as more people move to urban areas, flooding is having a growing impact on larger populations and economic losses.
Here we review recent GC Capital Ideas stories that have touched on issues relating to the Solvency II regime.
Here we highlight recent GC Capital Ideas stories authored by Andrew Cox, Head of Advisory, EMEA, at Guy Carpenter.
Guy Carpenter today announced the release of MetaRisk® 7.1, the latest version of the firm’s premier risk and capital management decision making tool. The platform offers access to a variety of new features and enhancements that will improve usability, increase overall functionality and enable the development of more accurate and efficient risk and capital models.
Here we repeat our popular series authored by John Major, which focuses on the issues and challenges in managing catastrophe model uncertainty.
Managing Catastrophe Model Uncertainty, Issues and Challenges: Part I, Executive Summary: Uncertainty is ever present in the insurance business, and despite relentless enhancements in data gathering and processing power, it is still a large factor in risk modeling and assessment. This realization, driven home by model changes and recent unexpected natural catastrophes, can be disconcerting - even frightening - to industry participants. But companies that understand the vagaries of model uncertainty and take a disciplined, holistic approach to managing the catastrophe modeling process are well positioned to adapt and outperform the competition.
Managing Catastrophe Model Uncertainty, Issues and Challenges: Part II, Natural Cat Modeling, Uncertainty in Cat Model Results: Computerized simulation modeling of the potential impact and risk of natural disasters - from multiple perils - was pioneered by Dr. Don G. Friedman at the Travelers Insurance Company in the 1960s. Figure 2, below, is an example of one of his simulated wind speed maps, circa 1974. In 1987, Karen Clark founded the first cat modeling firm, AIR, and three more firms, RMS, EQECAT and ARA, came on the scene in 1988, 1994 and 1999, respectively. By the early 1990s Guy Carpenter had become a “power user” of cat models and augmented its capabilities by acquiring the intellectual property - and hiring some colleagues of the retiring Dr. Friedman.
Managing Catastrophe Model Uncertainty, Issues and Challenges: Part III, Using Cat Models: Scenario analysis has a long history in risk management. By examining a set of hypothetical extreme events and asking “what if this were to happen?” management can begin to get a sense of vulnerabilities in the business. But it is hard to assess how realistic a particular scenario might be. Using historical events as the basis for scenarios incorporates the fact that those events did, in fact, occur. They are realistic by definition. And their relative occurrence over time gives a sense of probability.
Managing Catastrophe Model Uncertainty, Issues and Challenges: Part IV, How Guy Carpenter Can Help: As Karen Clark, founder of AIR and now an independent consultant, has said, “the black box started out as a useful tool for decision making, but then it grew to be very big and very powerful; the black box now makes the decisions.” While somewhat hyperbolic, there is also much truth to this aphorism. Models are tools, and a good tool user understands the strengths and limitations of the tool.