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Macroeconomic Simulation Analysis for Multi-asset Class Portfolio Returns

by Community Manager on ‎05-23-2016 02:13 PM (228 Views)

Macroeconomic Simulation Analysis provides an in-depth insight to a portfolio's performance spectrum. Conventionally, portfolio and risk managers obtain macroeconomic scenarios from third parties, such as the Federal Reserve, and determine portfolio performance under the provided scenarios.


In this paper, the authors propose a technique to extend scenario analysis to an unconditional simulation capturing the distribution of possible macroeconomic climates and hence, the true multivariate distribution of returns. In general, this is referred to as a top-down approach. They start with forecasting nearly 30 macroeconomic variables using Bayesian Vector Autoregression (BVAR). BVAR is used to avoid overfitting of the forecasting model. Using the model residuals, they fit copula over the marginal error distributions of each of the factors, thus taking into account the uncertainty and tail risk.


After fitting the BVAR, marginal error distributions, and copula, they simulate the distribution of macroeconomic scenario paths. By using Monte Carlo simulation, coupled with asset class return regression models, they approximate the full unconditional multivariate return distribution for the asset classes. In this case, the variance and correlation in returns comes from two sources—the macroeconomic path simulation and the uncertainty in asset class returns under said macroeconomic path. This technique gives a portfolio manager the understanding of the extreme tail risk and asset class relationships, the macroeconomic variables uncertainties and correlations. The proposed methodology adds value to the existing scenario analysis tools and can be used to determine which types of macroeconomic climates have the most adverse outcomes for the portfolio. This provides a broader perspective on value at risk measures, thereby allowing more robust investment decisions to be taken.


In this SAS Global Forum 2016 paper, SAS employees Srikant Jayaraman, Joe Burdis, and Lokesh Nagar explain the use of SAS procedures like VARMAX, COPULA (in SAS/IML software), and OPTMODEL to perform the entire analysis in a few steps.

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