Description: Wavelet models are used to analyze risk by time and frequency and provide richer information than time series models.

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 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: Enterprise risk management (ERM) has grown fast in the 21st century, thankful due to a variety of extreme yet unfortunate risk events and regulatory changes. However, challenges continue with many other initiatives competing with ERM for resources within an organization. In this month’s newsletter, we revisit this old yet still important topic and bring a few relevant reports to your attention.

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

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 discusses challenges in achieving ERM stakeholder buy- in and ways to improve stakeholder engagement.

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: This research report studies the social media response to three extreme tornado outbreaks: May 22, 2011 in Joplin, MO, November 17, 2013 in Washington, IL and April 9, 2015 in Rochelle, IL. It uses Twitter data with a 14‐day period for each tornado event with a focus on a 24‐hour period surrounding the event. It starts with an overview of the data followed by analysis of geolocation, insurance related user influence, social network, and sentiment.