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 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 explores the methods of determining the optimal timing for risk management projects.

Description: The Society of Actuaries announces the release of a new report that will provide a resource for the insurance industry to learn about image recognition technology. Authored by Kailan Shang, the report explores the current status of image recognition technology, models for image recognition, and various possible applications of image recognition to insurance.

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: 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.

Description: This is the R programs used to develop the examples for the research report “Extreme Events for Insurers: Correlation, Models, and Mitigation by Kailan Shang and Marc Vincelli

Description: A primer on extreme event modeling and mitigation for practitioners, it covers the complete cycle of extreme event risk management, from identifying experience data sources, to extreme event detection, to modeling and dependence analysis, and finally to monitoring and mitigation.

Description: This article discusses challenges in achieving ERM stakeholder buy- in and ways to improve stakeholder engagement.