Aubrey Clayton is a part of the Moody’s Analytics Insurance Research group. His recent work has focused on applications of economic scenario generators and Least Squares Monte Carlo (LSMC) proxy techniques to multi-period problems, particularly the projection of dynamic hedge programs and economic capital. Aubrey has a PhD in Mathematics from The University of California, Berkeley with a specialty in stochastic modeling and dynamical systems.
Economic Scenarios: Moody's Analytics provides internally and globally consistent economic, regulatory, and custom scenarios.
Economic Capital : Moody’s Analytics insurance economic capital solution provides critical insights that help evaluate solvency positions and risk-based decision making.
Insurance Asset and Liability Management : Moody's Analytics insurance asset and liability management (ALM) solution provide scenario-based asset and liability modeling for insurers.
Scenario Generation: Mathematical model simulating possible paths of economic and financial market variables.
Liability Valuation: Process of valuing a company's liabilities for financial reporting purposes.
Capital Measurement & Projection: Approach for the projection of assets and liabilities for a business block to future time.
Developed techniques to calibrate proxy functions for Conditional Tail Expectation metrics, improving efficiency for reserve/capital projections
Used LSMC and neural network methods to forecast Greeks for complex Variable Annuity portfolios, enabling fast projection of dynamic hedges
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.
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.
Financial institutions are seeking ways to gain a better understanding of their credit portfolios' risk dynamics, allowing them to foresee and to prepare for potential increases in capital requirements resulting from economic shocks.
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.
In this paper we consider a framework for evaluating real-world probabilistic forecasts of economic variables, particularly nominal interest rates over quarterly time horizons.