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In 2016 Allan D. Grody and Peter J. Hughes proposed a method and system termed 'Risk Accounting', an integrated financial and risk accounting framework. Risk Accounting incorporates a novel operational risk exposure quantification technique based on the Risk Unit (RU), a new common additive metric designed to express all forms of operational risk in banks. In this paper, we report on initial tests of the inherent predictiveness of the RU. The test focused on the period leading up to the global financial crisis of 2007-8 and involved the restatement into RUs of publicly available accounting data in the United States relative to a subset of large US banks. We contend that the RU's inherent predictiveness could be concluded if it is demonstrated that an accelerated increase in trended operational risk RUs and subsequent material unexpected losses are positively correlated. We further describe how a monetary value can be stochastically derived and assigned to the RU over time. The inclusion of valued RUs in accounting systems will potentially enable the systematic adjustment of financial performance and condition relative to accepted nonfinancial risks to complement the accounting treatment already applied to financial (credit and market) risks. The resulting harmonisation of the accounting treatment applied to both financial and nonfinancial risks based on stochastic modelling will enable risk-adjusted economic profit to be adopted as the primary business performance metric and economic capital as the primary method of determining both operating and regulatory capital requirements. The real-time or near-real-time production of portfolio views of operational risk exposures based on the RU adds analytical rigour to their management and causes risk mitigation to become both a risk reduction and a profit optimisation initiative. The more effective management, oversight and governance of exposures to operational risks is the anticipated outcome. [ABSTRACT FROM AUTHOR]

Abstract: Natural disturbances including wildfire, insects and disease are a growing threat to the remaining late successional forests in the Pacific Northwest, USA. These forests are a cornerstone of the region''s ecological diversity and provide essential habitat to a number of rare terrestrial and aquatic species including the endangered northern spotted owl (Strix occidentalis caurina). Wildfires in particular have reduced the amount of late successional forests over the past decade, prompting land managers to expand investments in forest management in an attempt to slow losses and mitigate wildfire risk. Much of the emphasis is focused specifically on late successional reserves established under the Northwest Forest Plan to provide habitat for spotted owls. In this paper, we demonstrate a probabilistic risk analysis system for quantifying wildfire threats to spotted owl habitat and comparing the efficacy of fuel treatment scenarios. We used wildfire simulation methods to calculate spatially explicit probabilities of habitat loss for fuel treatment scenarios on a 70,245ha study area in Central Oregon, USA. We simulated 1000 wildfires with randomly located ignitions and weather conditions that replicated a recent large fire within the study area. A flame length threshold for each spotted owl habitat stand was determined using the forest vegetation simulator and used to predict the proportion of fires that resulted in habitat loss. Wildfire modeling revealed a strong spatial pattern in burn probability created by natural fuel breaks (lakes and lava flows). We observed a non-linear decrease in the probability of habitat loss with increasing treatment area. Fuels treatments on a relatively minor percentage of the forested landscape (20%) resulted in a 44% decrease in the probability of spotted owl habitat loss averaged over all habitat stands. The modeling system advances the application of quantitative and probabilistic risk assessment for habitat and species conservation planning. [Copyright &y& Elsevier]