Description: Spreadsheets have become ubiquitous in business and forecasting. However, they do not usually have the same level of control that any other complex program would require. As a result, we often get erroneous results which can have far reaching, negative consequences.

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: Many top-performing businesses aren’t just risk aware, but rather risk intelligent. Such a posture helps prepare companies for a major event and can demonstrate to a regulator or auditor that an appropriately strong control framework is in place. Better still, companies gain real strategic value from a “risk adjusted” understanding not only of their threats and vulnerabilities, but also the upside opportunities associated with various risks. It starts with having both qualitative and quantitative information to identify, assess and manage risks. Strategic risk management, at its core, is about creating value by increasing transparency and accountability, and quality of information. More than a mere compliance exercise, effective risk management enables more informed strategic decision making, increased accountability and transparency, a greater likelihood of organizational success, and – ultimately – competitive advantage, as borne out by many studies that correlate risk management and financial results.

Description: The five words for ERM are transparency, discipline, alignment, adaptability and resilience. This article focuses on transparency, discipline and resilience.

Description: The paper o ers a simple framework for ranking the common reinsurance struc- tures in practice with the theory of stochastic orders. The basic idea is to slice the space of reinsurance structures into groups by expected loss cost to facilitate the comparisons within the group and between groups. Given the standard risk aversion assumption in economics, a spectrum of reinsurance structures with the same expected loss cost can be compared analytically with one another and sequenced based on their risk coverages under the convex order. The paper then expands the dimension of the comparison to groups of reinsurance structures with di erent expected loss costs, which can be ranked under the increasing convex order and the usual stochastic order. As such, the paper maps out the ordering for the entire space of reinsurance structures and presents it in a ma- trix format for quick reference. The implication

Description: Feature article that defines strategic risk management (SRM) and discusses ways to design and optimize a company’s SRM process.

Description: Many top-performing businesses aren’t just risk aware, but rather risk intelligent. Such a posture helps prepare companies for a major event and can demonstrate to a regulator or auditor that an appropriately strong control framework is in place. Better still, companies gain real strategic value from a “risk adjusted” understanding not only of their threats and vulnerabilities, but also the upside opportunities associated with various risks. It starts with having both qualitative and quantitative information to identify, assess and manage risks. Strategic risk management, at its core, is about creating value by increasing transparency and accountability, and quality of information. More than a mere compliance exercise, effective risk management enables more informed strategic decision making, increased accountability and transparency, a greater likelihood of organizational success, and – ultimately – competitive advantage, as borne out by many studies that correlate risk management and financial results.

Description: Risk management and capital budgeting are two critical components of the corporate decision process. They often need to be considered jointly at the corporate level because of their natural interaction through the dependent risk exposures and other synergetic relationships within an intricate corporate structure in a dynamic business environment. . This paper develops an integrated framework that aligns these two important corporate strategies across business divisions to optimize the streamlined enterprise strategic goal in a multi-period setting. The proposed integrated framework can serve as a practical guideline for corporate executives to optimally coordinate capital budgeting and risk management at the enterprise level.

Description: Abstract: 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: The paper o ers a simple framework for ranking the common reinsurance struc- tures in practice with the theory of stochastic orders. The basic idea is to slice the space of reinsurance structures into groups by expected loss cost to facilitate the comparisons within the group and between groups. Given the standard risk aversion assumption in economics, a spectrum of reinsurance structures with the same expected loss cost can be compared analytically with one another and sequenced based on their risk coverages under the convex order. The paper then expands the dimension of the comparison to groups of reinsurance structures with di erent expected loss costs, which can be ranked under the increasing convex order and the usual stochastic order. As such, the paper maps out the ordering for the entire space of reinsurance structures and presents it in a ma- trix format for quick reference. The implication of this stochastic ordering to reinsurance pricing is also investigated.