Description: The speaker will discuss the training of neural networks to compute future Greeks of liabilities (or options) without the use of nested stochastics.

Description: Feature article discussing quantitative impact studies, QIS5, on Solvency II.

Description: Summary of April 2020 Investment Section Town Hall and come additional commentary.

Description: Risk management techniques such as stress testing and setting capital targets are fairly well established for life insurers; however, the implementation of these techniques can vary considerably. A fundamental question is how to manage multiple valuation perspectives, and the list of possibilities keeps growing. Companies can potentially consider some or all of the following: U.S. reporting measures (Stat, GAAP), internal measures (EV, VNB, economic capital), international reporting measures (IFRS), rating agency models or other developing standards (ICS, NAIC group capital). This session will begin with a quick overview of the defining characteristics of different valuation frameworks. We will then provide more focused and practical considerations for managing risk and capital under different frameworks, using specific examples to highlight key trade offs.

Description: Today's actuarial teams dedicate substantial time and resources to maintain strong frameworks for risk management and control. Current practices typically endure this as a sunken cost, yielding minimal value beyond SOX/MAR/COSO compliance and basic error prevention. With a small up-front time investment, and a willingness to approach these activities with an open mind, the functionality of existing compliance activities can be completely transformed. Compliance activities, once a sunken time cost, can be elevated to deliver more insightful analysis to stakeholders.

Description: This article is about defining risks at the proper level. To do so one must distinguish between risks and the outcome of other risks. The so-called reputation risk is an example of something that is not a source of risk, but rather an outcome of other risks. In addition to defining risks at the source, is also important to categorize risks with consistent levels of granularity.

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: 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: 2011 Enterprise Risk Management Symposium, Chicago. This paper highlights the impact that a change in conditional data would have on a variety of risk assessment models, with specific emphasis on investment forecasting.

Description: This session at the 1998 Valuation Actuary Symposium gives an overview of risk management. Two example company programs are described. The use of value at risk as a measure is discussed.