Description: Introduction and table of contents of essay collection.

Description: The costly, time-consuming and complicated process of enterprise risk management (ERM) can be improved in many companies and made less tedious for managers by using reasonable data and templates obtained from peer group entities. The models used to calculate economic capital (EC) often underestimate its value because they do not consider decision-maker perception about risk. We assume here that managers, as the decision-makers, have appropriate business understanding and they can provide substantial information about risk characteristics regarding all business processes. Therefore we are focused, in this paper, on collecting data from managers across the different businesses to derive the appropriate knowledge about risky events, the importance of particular types of risk, the relationship between the risk outcomes and the level of risk control in a particular industry. We conclude that the collected data has a high potential for use as a benc hmarking reference and analysis for improving ERM models for individual businesses.

Description: In a very general setting this paper describes a straightforward, non-parametric approach to aggregate "stand-alone" or marginal distributions with desired correlations without imposing additional assumptions on those marginals. We then develop two optimizations for the enterprise, one based on maximizing return on economic capital and the other based on a mean -semivariance efficient frontier from the investor point of view.

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: Is Risk/Return a sound tool for decision-making? No. It is not consistent, not objective, distorts decision and degrades governance. Why? What shall we do then?

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: A collection of essays that discuss the principles and ideas underpinning the ORSA initiative and the risk metrics used for decision making.

Description: Statistics were invented to describe heterogeneity and are now applied to tackle alea. It may not be appropriate. Let's clarify their field of relevance.