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: This paper introduces actuaries and others to dynamic stochastic general equilibrium (DSGE) models, a type of macroeconomic model. Authored by Kailan Shang, the paper illustrates how DSGE models can be used in economic scenario generation to reflect the cause and effect relationships that occur in the economic system.

Description: With the credit interest rate floor of a cash balance plan—a simple type of option identified by the Project Oversight Group—as a model, this research uses three valuation and risk analysis approaches to explore the existing techniques to value embedded options on an economic basis.

Description: This paper explores areas where fuzzy logic models may be applied to improve risk assessment and risk decision-making. It discusses the methodology, framework and process of using fuzzy logic systems for risk management. With the help of practical examples, it is hoped that it will encourage wise application of fuzzy logic models to risk modeling.

Description: This article introduces a non-traditional type of economic scenario generator that relies on dynamic stochastic general equilibrium (DSGE) models are complex macroeconomics models and have been increasingly popular in central banks for analyzing monetary policies.

Description: This article discusses how unemployment and underemployment information can help improve economic forecasts, insurance assumption setting and risk management. It summarizes the current modeling practices of unemployment which shows a deficiency of using unemployment/ underemployment information in actuarial works. It also gives three examples of using unemployment/underemployment information for economic assumption setting, new business growth prediction and stress scenarios related to the labor market.

Description: This article explores the methods of determining the optimal timing for risk management projects.

Description: Many risk management decisions involve the timing of implementing risk strategies. The cost of hedging, capital raising and securitization changes with the economic environment. The timing of de-risking and investment in new risk management functions affect the cost and benefit of these projects as well. Like investment timing, it is important to consider the timing in risk management decisions to maximize the gain of risk management projects. This paper discusses methods of determining the appropriate timing of implementing a risk management strategy or investing in risk management projects. It explains the human biases that may lead to inferior timing decisions. It also covers the costs and benefits of risk management projects and the impact of new information on the decision-making process. Timing considerations for financial risk hedging, insurance risk hedging and investment in new risk management projects are explained. Factors used for timing decisions include economic conditions, market volatility, credit rating, borrowing cost, capital adequacy and financial resources.

Description: This paper discusses some key challenges of embedding guarantees in DC plans, including setting an appropriate guarantee level, designing effective cost funding and risk management strategies, and fair treatment of plan participants with different investment strategies.