Description: An introduction to the Goals-Progress-Strategy (GPS) strategic risk management framework: an original synthesis of scenario based ERM, the Logical Framework Approach, and new metrics and methods. GPS improves strategic planning and execution and enables practical management of the risks a company faces in pursuit of its business goals. The framework provides a portfolio view of a company’s strategic objectives, a return on risk-adjusted capital measure, risk-intelligent decision making, and risk-adjusted performance measurement/compensation.

Description: When companies engage in risk management activities, they can be seen as modifying their distributions of future outcomes. Often, this takes the form of trading off a sure cost for a decrease in some potential risk of loss. Companies may carry out these activities with several goals in mind, such as reduction in earnings volatility or preservation of capital. This paper will demonstrate a method for evaluating possible strategies by dividing the effects on costs and risks into three tranches: earnings, impact and estate. The tranches can be associated with different interests of the various stakeholders. This trifurcation analysis can help to explain the risk-reward trade-offs of different risk treatment alternatives and can aid in risk-related decisionmaking.

Description: A sound enterprise risk management (ERM) framework functions as an ecosystem by balancing the interdependencies of its elements while constantly adapting to an ever‐changing, external environment. Each stakeholder has a unique role to play in forming a thriving ERM ecosystem by integrating risk‐conscientiousness in decision‐making. A resilient ERM framework has a single well‐ understood risk language and culture to promote healthy risk‐taking behaviors. The central focus of such a framework is concentrated around all stakeholders and everyone is held accountable. Every day, external forces can potentially disrupt how we operatively conduct business. Unforeseen changes in regulation, market trends, terrorist’s attacks and extreme natural disasters are often categorized as extremely adverse. How then will your ERM ecosystem survive the one‐in‐200 event and continue to flourish through dynamically changing environments?

Description: ERM has methodology for quantifying the risk to capital, but to determine optimal capital, the impact of capital level on firm value is needed. This gets into the realm of valuation. Also capital allocation provides risk quantification for business units, but comparing return among units is fundamentally a risk-pricing exercise. When considering risk pricing, optimal returns on allocated capital are not necessarily constant across business units. The current ERM methodology, as well as actuarial literature on valuation and pricing, are reviewed, and possible directions for application of pricing and valuation methods to risk management problems are outlined.

Description: People naturally observe risk in the form of the range of experienced gains and losses which is usually represented in statistical terms by standard deviation. In most cases, there are not enough observed values to reliably discern the true amount of losses that are possible in extreme situations. Statistical techniques that have long been applied to insurance company risks are being used to extrapolate from observed risk to extreme tails of the distribution of gains and losses. These processes are essentially an extrapolation from the “known” risk of volatility near the mean to “unknown” risk of extreme losses. “Fat tails” is a term often applied to risks where the likely losses in rare situations significantly exceeds the predictions of a Gaussian (“thin tailed”) distribution. This paper will look at the relationship between the observed risk and the extreme risk in statistical insurance r isk models and discuss how we might create a discussion among model users about the appropriateness of that relationship for specific types of risks.

Description: Increasing uncertainties and volatility in financial as well as real goods markets increasingly reveal that controlling systems based on deterministic and pseudo-exact values are insufficiently eligible to manage the business activities. This raises the question how management systems can be modified to generate the adequate management impulses in volatile periods. Risk management deals from identifying, evaluating and aggregating chances and risks, which again determine the potential fluctuation range of relevant performance indicators. Everyday corporate life shows that risk management is considered important; but the corresponding processes to identify, evaluate and report chances and risks are seldom part of established decision criteria. This “paradox” lets assume that risks are managed in various differing and unsystematic ways. The combination of traditional controlling methods and risk management is not primarily a methodological challenge. The behavior of the whole organization, practiced over a long-year period, has to be transferred into a new corporate culture of transparent dealing with chances and risks. This paper describes the necessary change management process, which has to come along with necessary methodological modifications.

Description: This paper presents the output of in-house research on an emerging area of risk appetite management and our global implementation best practices. It intends to provide readers with detail implementation of risk appetite framework and assessment across an organization. It provides the conceptual risk appetite framework based model- developed by the author, implementation plan, and global best practices of a risk appetite assessment across an organization. It identifies the challenges and exceptions in the risk appetite assessment with an approach to manage it effectively, and helps organizations with cost-benefit analysis. It also includes an illustrative case study.

Description: This paper demonstrates the use of Quantile Regression in the development and understanding of conditional value at risk (VaR) models for enterprise risk management.

Description: Project portfolio risk management and risk analysis form one of the critical components of enterprise risk management. Organizations measure and analyze risks associated with projects, project portfolios, and programs. The important step of the process is quantitative risk analysis of project schedules using event chain methodology (ECM). ECM is a stochastic modeling technique for schedule risk analysis. All risks, including schedule and nonschedule-related risks, are assigned to a particular project and within this project to the particular activity or resource. Further, ECM allows one to model the relationship between project risks by defining risks that cause or trigger other risks. All risks and relationships between them will be presented on the project or portfolio Gantt charts using event chain diagrams. After risks are assigned to project and portfolio schedules, Monte Carlo simulation of the project schedule is performed. ECM simplifies complex risk analysis process.