Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).

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: 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: Article describing the most recent Risk Management Section research updates

Description: This report studies the impact of unemployment and underemployment on insurance business. It introduces the concepts, theoretical background, and patterns of unemployment and underemployment. It also touches on economic policies that have been used to improve labor market conditions and maintain the market’s stability.

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 research paper is intended to be 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. This paper explores extreme events from the perspective of a global insurance company, with an emphasis on market risk, credit risk, insurance risk, liquidity risk, and business risk. Extreme risk concepts and models are introduced from a practitioner’s perspective without unnecessary theoretical details. This report is not intended to be all-encompassing, but rather to focus on the key aspects of extreme risk analysis and its application. The input data and R programs underlying the numerical examples herein are available in the supporting files accompanying this report

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.

Description: 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.