Description: This paper proposes a new type of financial derivative named contingent liquidity swap (CLS) to address the liquidity issue. CLS is a swap agreement that the buyer of the swap pays cash in exchange for less-liquid assets in the event of a liquidity event. Those less-liquid assets may include stocks or high-yield bonds. The seller of the CLS makes a series of payments for the contingent liquidity. The payment should be lower than the equity risk premium to reduce the cost of funding but high enough to attract investors.

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

Description: The essay describes an application of “Do Jumps Matter in the Long Term? A Tale of Two Horizons,” by Jean-François Bégin and Mathieu Boudreault, published in the North American Actuarial Journal online Jan 5, 2021

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

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: 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: How to use using risk culture and behaviors to assess an ORSA.

Description: This Effective Enterprise Risk Management (ERM) Stakeholder Engagement report examines practices for gaining acceptance of a proposed ERM strategy. The researcher has identified the factors, processes and practices that lead to both poor and strong levels of stakeholder acceptance.

Description: The report studies the impact of unemployment and underemployment on insurance business, introducing concepts, theoretical background, and patterns of both.

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.