Description: This article provides an introduction to group supervision and group capital and its importance in today's increasingly global insurance market. Additionally, the article examines the various methods used in practice for calculating group capital and the factors that companies should consider as they develop their group capital management strategy.

Description: Modeling Approaches and Challenges focuses on approaches to real world simulation and the challenges and considerations in real world simulation. Topics include: A frequency domain approach to build realistic multi-horizon scenarios. (Ortec approach) Cascade models structure linking models for major asset classes and economic variables. (Moody’s approach) Overview of models, calibrations & approaches within the context of different use cases. Deterministic & stochastic approaches. Importance of model re-calibration flexibility. We will also discuss specific insights into considerations and challenges in real world simulation, which include topics like: Setting capital market assumptions and/or calibration targets. Assumptions about correlation and causality. Calibrating the model to capital markets assumptions. Validating the model against capital market assumptions. Role of history, market data and stylized facts. Importance of back testing. Calibrating the model to multiple investment horizons. Inclusion of alterative asset classes such as private equity, infrastructure, and direct real estate. Associated issues of limited data, illiquidity and greater uncertainty about calibration targets.

Description: Duration matching is a risk management strategy, commonly used in the insurance industry, where the duration (price sensitivity to a yield curve movement) of an asset portfolio is adjusted to be made equal to the duration of the liability. However, this approach has flaws. In this essay, I note some key failures of duration matching and offer a potential alternative.

Description: This study examines and describes parameter uncertainty. The paper provides structures to incorporate parameter uncertainty in quantifying risk.

Description: Advanced Topics in Corporate Finance and Enterprise Risk Management (ERM) covers a broad range of corporate finance concepts. As part of a 10-year strategic plan exercise, you'll apply opportunity engineering to help guide senior management in introducing a new product to market. You'll also focus on three applications of Extreme Value Theory (EVT), which are covered and tested in a business context.

Description: Risk management techniques do not include system science. System science requires that the uncertainty of the world is incorporated into the results. The articles explains the difference between risk and uncertainty. Learn how risk practices create an overoptimistic view of future outcomes.

Description: Article discussing the benefits of the Chartered Enterprise Risk Analyst, CERA, credential.

Description: This report looks at identifying drivers of systemic risk in China’s insurance industry and develops a measurement method for systemic risk as well as systemic risk dependence between the insurance industry and other industries.

Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

Description: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.