### Retirement Income Modeling Risks

**February 5, 2015** - Big Ideas

How much can the (in)accuracy of retirement portfolio modeling assumptions affect conclusions about the safety of retirement income? In their December 2014 paper entitled “How Risky is Your Retirement Income Risk Model?”, Patrick Collins, Huy Lam and Josh Stampfli examine potential weaknesses in the following retirement income modeling approaches:

- Theoretically grounded formulas – often complex with rigid assumptions.
- Historical backtesting – the future will be like the past, requiring long samples.
- Bootstrapping (reshuffled historical returns) – provides alternate histories but does not preserve return time series characteristics (such as serial correlation), and requires long samples.
- Monte Carlo simulation with normal return distributions – sensitive to changes in assumed return statistics and often does not preserve empirical return time series characteristics.
- Monte Carlo simulation with non-normal return distributions – complex and often does not preserve empirical return time series characteristics.
- Vector autoregression – better reflects empirical time series characteristics and can incorporate predictive variables, but requires estimation of regression coefficients and is difficult to implement.
- Regime-switching simulation (multiple interleaved return distributions representing different market states) – complex, requiring estimation of many parameters, and typically involves small samples in terms of number regimes.

They focus on retirement withdrawal sustainability (probability of shortfall) as a risk metric and risks associated with modeling (future asset returns), inflation and longevity assumptions. They employ a series of examples to demonstrate how an overly simple model may distort retirement income risk. Based on analysis and this series of examples, *they conclude that:* Keep Reading