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

TL;DR

This paper introduces a novel sample recycling method to improve the efficiency of nested Monte Carlo simulations, especially useful when traditional curve fitting techniques are hard to apply.

Contribution

The paper proposes a new sample recycling approach that reduces computational effort in nested stochastic modeling by reusing inner loop paths across different outer scenarios.

Findings

Significantly reduces computational time for nested simulations.

Effective in scenarios where curve fitting methods are challenging to implement.

Applicable to financial risk management and insurance modeling.

Abstract

Nested stochastic modeling has been on the rise in many fields of the financial industry. Such modeling arises whenever certain components of a stochastic model are stochastically determined by other models. There are at least two main areas of applications, including (1) portfolio risk management in the banking sector and (2) principle-based reserving and capital requirements in the insurance sector. As financial instrument values often change with economic fundamentals, the risk management of a portfolio (outer loop) often requires the assessment of financial positions subject to changes in risk factors in the immediate future. The valuation of financial position (inner loop) is based on projections of cashflows and risk factors into the distant future. The nesting of such stochastic modeling can be computationally challenging. Most of existing techniques to speed up nested simulations are based on curve fitting. The main idea is to establish a functional relationship between inner loop estimator and risk factors by running a limited set of economic scenarios, and, instead of running inner loop simulations, inner loop estimations are made by feeding other scenarios into the fitted curve. This paper presents a non-conventional approach based on the concept of sample recycling. Its essence is to run inner loop estimation for a small set of outer loop scenarios and to find inner loop estimates under other outer loop scenarios by recycling those known inner loop paths. This new approach can be much more efficient when traditional techniques are difficult to implement in practice.