Senior Director, Insurance Research Group
Dr. Steven Morrison is a senior director in Moody’s Analytics insurance research group. Steven joined Barrie & Hibbert in 2001 and played a leading role in the design and development of B&H's Economic Scenario Generator software. His recent research and advisory work has focused on applications of the Least Squares Monte Carlo (LSMC) proxy modeling technique to multi-period problems, in particular capital projection and projection of dynamic hedge programs in order to assess hedge effectiveness. He pioneered the use of LSMC as a tool for projection of insurance liability values and measurement of economic capital, publishing in industry journals on this topic. He has an MSc in financial mathematics and a PhD in theoretical physics.
Steven Morrison’s second whitepaper, Profit Emergence under IFRS 17, turns its attention to the Variable Fee Approach (VFA). Explore his practical insights on financial risk and its impact on contracts with participation features.
An emerging business requirement for North American insurers is the ability to project forward stochastic reserve and capital requirements under various planning scenarios to a specific future date. In this paper we consider applying proxy functions to this task, using function fitting techniques described in our previous research paper Fitting Proxy Functions for Conditional Tail Expectation: Comparison of Methods.
The ability to project financial statements to understand their sensitivity to market risks, insurance risks, and methodology decisions is critical for an effective IFRS 17 implementation.
This paper details alternative methods for fitting proxy functions to CTE, employing quantile regression in combination with OLS among other techniques. We compare methods according to quality of fit for an example portfolio of variable annuities.
In this paper, we have considered the use of proxy models as a way of overcoming some of the operational and computational challenges associated with measuring future solvency under different market conditions and ALM assumptions.
This paper provides an introduction to various techniques for efficient calculation of the market-consistent value of a portfolio of insurance policies. Two standard approaches to portfolio valuation are considered: (1) Use of different scenarios through different policies; (2) Portfolio compression through the use of model points. Additionally, the use of proxy functions is introduced as a novel approach to valuation of individual policies.
In this paper, we show a practical application to forecasting capital requirements for real portfolios of participating whole life and annuity business, carried out in a joint research project between Moody's Analytics and New York Life Insurance Company.
The challenge of projecting dynamic hedge portfolios for blocks of Variable Annuities (VA) with complex guarantees has proven to be extremely computationally demanding but also essential for obtaining hedging credit in reserves or capital calculations. Our previous research has argued in favor of proxy function methods such as Least Squares Monte Carlo as alternatives to full nested stochastic calculations, and we have demonstrated the successful application of these methods for hedging in simple option examples including path-dependent options. This paper extends previous work by considering actual VA products with guarantees of the kind offered by insurers in North America and Europe.
Quantitative Insurance Research - The unintended consequences of scenario post-processing in the valuation of insurance liabilities
In this paper we explore the use of scenario re-weighting as a method for post-processing scenario sets to reflect calibration targets without having to recalibrate the model. While post-processing techniques can be quite flexible in their ability to match targets, they may result in unintended changes to distributional assumptions that are not included in the set of calibration targets. Using simple examples, we demonstrate how a scenario set's ability to match a set of vanilla asset prices does not uniquely define the resulting prices of more exotic liabilities (or assets).
In this paper, we discuss the validation of proxy models, commonly used in the insurance industry to replace valuations that would otherwise require Monte Carlo simulation. In practice, proxy model validation inevitably involves a certain amount of subjectivity and is specific to the exact problem at hand. We do not attempt to provide a prescriptive recipe for how validation should be carried out, but rather suggest some general ideas and principles based on our experience implementing proxy models with our clients.