Efficient nested simulation for conditional tail expectation of variable annuities

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<title>Efficient nested simulation for conditional tail expectation of variable annuities</title>
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<namePart>Feng, Mingbin </namePart>
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<namePart>Hardy, Mary R.</namePart>
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<abstract displayLabel="Summary">Monte Carlo simulationsin particular, nested Monte Carlo simulationsare commonly used in variable annuity (VA) risk modeling. However, the computational burden associated with nested simulations is substantial. We propose an Importance-Allocated Nested Simulation (IANS) method to reduce the computational burden, using a two-stage process. The first stage uses a low-cost analytic proxy to identify the tail scenarios most likely to contribute to the Conditional Tail Expectation risk measure. In the second stage we allocate the entire inner simulation computational budget to the scenarios identified in the first stage. Our numerical experiments show that, in the VA context, IANS can be up to 30 times more efficient than a standard Monte Carlo experiment, measured by relative mean squared errors, when both are given the same computational budget.</abstract>
<note type="statement of responsibility">Ou Dang, Mingbin Feng, Mary R. Hardy</note>
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<topic>Cálculo actuarial</topic>
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<title>North American actuarial journal</title>
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<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
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<text>01/06/2020 Tomo 24 Número 2 - 2020 , p. 187-210</text>
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