In this paper, we formally show that the cross-sectional variance of stock returns is a consistent and asymptotically efficient estimator for aggregate idiosyncratic volatility. This measure has two key advantages: It is model free and observable at any frequency. Previous approaches have used monthly model-based measures constructed from time series of daily returns. The newly proposed cross-sectional volatility measure is a strong predictor for future returns on the aggregate stock market at the daily frequency. Using the cross section of size and book-to-market portfolios, we show that the portfolios’ exposures to the aggregate idiosyncratic volatility risk predict the cross section of expected returns.

Effective risk management in pension funds requires the use of appropriate long-term risk and performance indicators. However, Defined Contribution (DC) pension plans currently rely on short-term metrics that don't align with the retirement income goals of beneficiaries. Against this backdrop, we introduce a funding ratio measure for DC plans defined as the plan assets divided by accrued benefits derived from any given (defined) stream of contributions. The funding ratio's denominator is the present value of the total retirement income achievable if all contributions had been invested in fully amortizing fixed-income portfolios called retirement bonds. We also use the retirement bonds to introduce a class of target-income strategies that can effectively reduce income risk as retirement approaches, but also secure minimum funding ratio levels. These simple asset allocation rules strongly dominate the standard target-date fund strategies routinely used by DC plans, in terms of retirement outcomes for beneficiaries.