ORSA Insight

Asset optimization which focuses only on the distributional characteristics of an investment portfolio will fail to achieve an optimal portfolio from the perspective of value creation for a life insurance firm. In this paper we show how this issue can be resolved through the application of Least Squares Monte Carlo techniques.

Authors: Romain Lombard, Alexis Bailly
Date: January 13, 2014
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In this paper, we describe how the capability to efficiently produce robust and accurate proxy functions for CTE(70) run-off reserve behavior across a wide range of multi-timestep, multi-risk-factor scenarios can significantly enhance a firm's forward solvency projection analytics. This can be extremely useful for firms when projecting their balance sheets, as well as reserving and capital requirements as part of ORSA and other business planning requirements.

Authors: Steven Morrison, Craig Turnbull, and Naglis Vysniauskas
Date: July 17, 2013
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Insurers need a modeling capability that meets two key requirements: to determine appropriate multi-year scenarios (deterministic stress tests or stochastic) in which to project the insurer’s business and to accurately assess the capital requirements that would be created within these scenarios. In this report, we provide you with an overview of the fundamental challenges of ORSA and practical guidance on the approach you might take.  

Authors: Craig Turnbull and Andy Frepp
Date: June 7, 2013
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This presentation offers an overview of ORSA and the quantitative modeling requirements needed. You will learn more about the Least Squares Monte Carlo (LSMC) approach, which is an important component of a firm’s ORSA in the context of solvency capital projection. In addition, it examines multi-year proxy function applications based on stochastic projections, reverse stress testing, and scenario testing.

Author: Gavin Conn
Date: May 24, 2013
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