This paper examines a canonical stochastic overlapping generations model with dynamically complete markets. Belief differences lead agents to place bets against each other and so wealth shifts across agents and across generations. Such changes in the wealth distribution strongly affect prices of...

Many bond portfolio managers argue that bond laddering tends to outperform other bond investment strategies because it reduces both market price risk and reinvestment risk for a bond portfolio in the presence of interest rate uncertainty. Despite the popularity of bond ladders as a strategy for...

DeMiguel, Garlappi, and Uppal (Review of Financial Studies, 22 (2009), 1915-1953) showed that in the stock market, it is difficult for an optimized portfolio constructed using mean-variance analysis to outperform a simple equally-weighted portfolio because of estimation error. In this paper, we...

In this paper we examine the effect of collateral requirements on the prices of long-lived assets. We consider a Lucas-style infinite-horizon exchange economy with heterogeneous agents and collateral constraints. There are two trees in the economy which can be used as collateral for short-term...

Most standard asset-pricing models assume that all shocks to consumption are permanent. We relax this assumption and allow also for non-permanent shocks. In our specification, the long-run mean of consumption growth is constant; consumption levels are subject to short-run deviations from their...

This paper shows that the latest generation of asset pricing models with long-run risk exhibits economically significant nonlinearities, and thus the ubiquitous Campbell--Shiller log-linearization can generate large numerical errors. These errors in turn translate to considerable errors in the...

Replicating portfolios have recently emerged as an important tool in the life insurance industry, used for the valuation of companies' liabilities. This paper presents a replicating portfolio (RP) model for approximating life insurance liabilities as closely as possible. We minimize the L1 error...