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Monte Carlo simulation methods are commonly used as a risk management tool to estimate the risk exposure of financial asset portfolios. However, the traditional brute-force Monte Carlo (BFMC) method is often very timeconsuming, which makes it difficult to serve the risk management needs of modern insurance industry. An alternative approach, the least-squares Monte Carlo (LSMC) method, could substantially reduce the computation cost by fitting a proxy function of liabilities using simple nonlinear regression methods. However, the LSMC does not work well in capturing the true risk properties of hedged assets and some other variable annuities. To improve the LSMC method, we propose to use neural network method during the curve fitting process. Since the neural network method allows more curve-fitting flexibility, the proxy functions of liabilities and Greeks are expected to be improved. By using the improved proxy functions to predict the liabilities and Greeks, the risk exposure of financial portfolios can be more properly quantified.
Aon Securities Inc.
University of Saskatchewan
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