WHAT WILL HAPPEN TO FINANCIAL MARKETS WHEN THE BABY BOOMERS RETIRE?

This paper explores whether changes in the age distribution have significant effects on financial markets that are rational and forward-looking. It presents a stationary overlapping generations model in which agents save for retirement while working, making a portfolio decision over risky equity and riskless bonds in zero net supply. The model features only aggregate uncertainty, a technology shock to production and random population growth, and is solved numerically using the parameterized expectations approach (PEA) following Den Haan and Marcet (1990). The methodology of Den Haan and Marcet (1994) is used to test the accuracy of the solution. Although agents' degree of risk aversion is constant over time, they invest as if increasingly risk averse with age: young workers short the riskless asset in order to hold equity, while old workers hold mostly the riskless asset. This portfolio behavior stems from an implicit holding of a nontradable asset, human capital. Using the PEA solution to simulate a baby boom-baby bust reveals that the return differential between equity and bonds rises significantly when boom turns to bust. At this point a large cohort of old workers trades with a smaller cohort of young workers, resulting in excess demand for the riskless asset, which pushes down the riskfree rate relative to the return on equity. There is thus cohort-specific demographic risk in financial markets that are rational and forward-looking, with returns to baby boomers significantly below steady state returns. The paper also demonstrates that a simple pay-as-you-go pension scheme fails to insure agents against this cohort-specific risk, concluding that only government can insure agents against it, by varying borrowing over time and thereby making transfers of wealth across non-overlapping generations.An appendix solves the model using a "projection" PEA, which uses a nonlinear equation solver to find the coefficients at which the approximating function equals the numerically computed conditional expectation. It compares the efficiency and accuracy of this solution method to that of the Monte Carlo PEA.