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Nested stochastic modeling has been on the rise in many fields of thefinancial industry. Such modeling arises whenever certain components of astochastic model are stochastically determined by other models. There are atleast two main areas of applications, including (1) portfolio risk managementin the banking sector and (2) principle-based reserving and capitalrequirements in the insurance sector. As financial instrument values oftenchange with economic fundamentals, the risk management of a portfolio (outerloop) often requires the assessment of financial positions subject to changesin risk factors in the immediate future. The valuation of financial position(inner loop) is based on projections of cashflows and risk factors into thedistant future. The nesting of such stochastic modeling can be computationallychallenging.  Most of existing techniques to speed up nested simulations are based on curvefitting. The main idea is to establish a functional relationship between innerloop estimator and risk factors by running a limited set of economic scenarios,and, instead of running inner loop simulations, inner loop estimations are madeby feeding other scenarios into the fitted curve. This paper presents anon-conventional approach based on the concept of sample recycling. Its essenceis to run inner loop estimation for a small set of outer loop scenarios and tofind inner loop estimates under other outer loop scenarios by recycling thoseknown inner loop paths. This new approach can be much more efficient whentraditional techniques are difficult to implement in practice.

Sample Recycling Method -- A New Approach to Efficient Nested Monte Carlo Simulations