Description: How insurers can include a liquidity score and liquidity stress scenarios as part of their asset liability management (ALM) protocol.

Description: In this webinar, the presenters will give a behind the scenes look at how the NAIC ESG was parameterized. They will also share the results of its most recent calibration.

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: Emerging Applications of ESG Methods offers ground-up insights on cutting edge applications of economic scenario generation. The course will focus on the incorporation of climate change risk in scenario modeling and investment applications of ESGs. The following topics will be covered: Approaches to climate risk modeling. Climate risk is very different than classical financial economic risk. Radical uncertainty is amenable to scenario analysis and lack of data. Building special deterministic scenarios that go beyond NGFS requirements as well as bespoke scenarios. Holistic Approach to Climate Risk Modeling. Overview of different Climate Risk Modeling Applications. Use of ESG scenarios for Strategic Asset Allocation (SAA). Liquidity risk management.

Description: By establishing insurance risk as an asset class, participants will provide the insurance industry with vast pools of capital to mitigate catastrophic risk through the use of new nontraditional reinsurance products. From Securitization of Insurance Risk: The 1995 Bowles Symposium, M-FI97-1.

Description: To discuss the principles to be followed in the determination of dividend distribution scheme, how to ensure the fairness and sustainability of dividend distribution, and the specific issues to be considered in the practice of dividend distribution.

Description: This presentation discussed the Copula Methodology, which is the most popular methodology in multivariate modeling in finance and insurance. The definition, advantages, limitations, and uses of the Copula Methodology were presented. Some other multivariate models were discussed as well. From Actuarial Research Clearing House, 2012, Vol. 1.

Description: Appendix 2 from the research report entitled 'Exploration of Reputational Risk from the Perspective of a Variety of Stakeholders.'

Description: The thrust of this article is that while ERM starts out as a compliance and corporate governance tool, well managed companies use ERM as a strategic tool that will help them increase shareholder value. To do so requires a synthesis of the actuarial techniques of insurance and the capital markets perspectives of corporate finance.

Description: Following recent market volatility and regulatory changes, market data and market-consistent economic scenarios are at the core of the valuation of liabilities and market risk benefits under regimes such as IFRS 17 and Long Duration Targeted Improvements. This webcast will explore options available to insurance companies to establish stable long-term volatility assumptions and discuss how the choice of financial data can impact the calibration and the simulation of credit spread scenarios.