Description: The author uses football to demonstrate the axiom of downside risk, and how it can be applied in business situations when considering whether to use a conservative or aggressive approach.

Description: Methodology for identifying principles based economic capital.

Description: The academy generator is widely applied in actuarial practice. Initially confined to regulatory reserving, companies are increasingly using the AAA/SOA generator over an ever widening range of applications testing the limits of the academy generator's intended function. The session will give a brief overview of the academy generator and the asset classes it can be used to model. It will also analyze the statistical properties of the academy scenarios and discuss the scenarios relative to standard validation criterion. Some of the challenges that actuaries face in using this framework will be reviewed and options for addressing these challenges will be offered.

Description: The article provides an update on the 2019 ERM Symposium with regard to date, location and content (General Sessions and Concurrent Sessions).

Description: Applications of Stress Testing

Description: This abstract describes a paper that studies Gerber-Shiu functions and dividend payments in an insurance risk model driven by a spectrally negative Levy process of bounded variation.

Description: “Investments in securities are traditionally evaluated on a standalone basis by using ratios that summarize the relation¬ship between anticipated risks and rewards. These ratios could also be used to better quantify the relationship between the standalone risks and rewards of life and annuity products and thus enhance the capital allocation process. These ratios will be discussed and critiqued.” The article describes how “the . . . ratios can help tie modeled product risks to modeled rewards.”

Description: The mismatch between how interest rate impacts the value of assets and liabilities could have material negative impact to companies’ earnings and surplus. To quantify and mitigate the impact of such mismatches, companies have used matching a combination of duration and convexity. I will review these measurement techniques and their limitations.

Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.

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