Description: Actuaries are increasingly being asked to incorporate Economic Scenario Generators into their work. Whether it be a Risk Neutral model for pricing or a Real-World version for Economic Capital modelling, using these tools can help produce a much more robust analysis. Unfortunately, these models and their underpinnings are also very complicated, which can lead to confusion. The presenters in this session will try to demystify some of this. In particular, they will address the following questions: • What is the difference between Real-World and Risk Neutral scenarios? • When should I use one or the other? • How are these models calibrated? • What are some of the misconceptions about these models?

Description: Economic Scenario Generator - Investment Section Web Series Session #2 - Real World This session will cover the following points: 1. NAIC ESG 2. Strategic Asset Allocation and Optimization Techniques 3. Covid-19 Postmortem: Modeling and calibration changes because of recent events

Description: This article describes how insurers can expand the reach of their efforts with regard to Economic Capital EC into the broader enterprise risk management process. Some experts contend that can be done by shifting the focus towards value. EC has the potential to shed new light on valuable opportunities for insurers.

Description: This paper presents a value-at-risk platform for integration of assets and liabilities in a rigorous risk management framework. The model measures all major risks faced by a P&C company. The paper demonstrates how this new technology can be used to assist in maximizing capital efficiency and migrating from a risk management environment governed almost exclusively by ad-hoc rules of thumb to one governed by true risk management principles. From 2006 Enterprise Risk Management Symposium, M-AS06-1.

Description: The author makes 10 predictions of how risk management will change over the next decade.

Description: This presentation offers an overview of enterprise risk management with topics covering benefits of risk management for an insurance company, establishing an ERM framework, risk reporting, and predictions about the future of ERM.

Description: Economic Scenario Generator - Investment Section Web Series - Risk Neutral

Description: Economic risk analysis has two dimensions: time and frequency. Asset return varies by time because of economic cycles and economic structural changes. Risk is higher during economic recessions and smaller during economic expansions. In addition, different economic structures may exist at different time scales. Risk measures calculated based on daily, weekly, monthly and yearly historical data can be very different. The appropriate frequency depends on the time horizon of the analysis. Therefore, it is important to measure economic risk at both the time level and the frequency level. However, time series analysis and statistical analysis that have been widely adopted in economic risk analysis focus on only one of the two dimensions. In this paper, wavelet models are used to enhance both time‐invariant analysis and time‐variant analysis. Wavelet models can systematically analyze risk by time and frequency and provide richer information than time series models. Using wavelet models, risk measurement can easily be adjusted based on time horizon in a consistent way. Equity VaR estimation is selected to demonstrate the application of wavelet analysis in risk management.

Description: This report examines the use of a copula approach to build a predictive model to estimate the amount of economic capital a life insurance company needs to protect itself against an adverse movement in interest rates, mortality, and other risk drivers.

Description: Pricing is a central activity of actuaries to assure solvency and to manage risk. Life insurance is also a social good, not just a product, and the US population is massively underinsured. Pricing must strike a balance. Unlike a simple 4x4 matrix to produce pricing, today's technology offers the ability to evaluate literally thousands of variables, enabling a kind of precision pricing. It also heightens the possibility of introducing bias and discrimination through the use of unregulated third-party data.