Research

This paper studies the possibilities that residential real estate investment brings to lifecycle portfolios by incorporating the long-run relationship between house prices and income into the model. Labor income is at the core of lifecycle models and a significant body of literature argues that income is cointegrated with house prices through rents. Under this hypothesis, residential real estate investment can be used to hedge regional income changes on top of hedging rents. Regional income is very much related to the cost of labor intensive services such as elderly care, health care or education, and these services together with housing can constitute a sizable portion of household budgets. What makes investment in real estate attractive is thus the potential to hedge both housing and labor intensive services by exposing the portfolio to income changes. Preliminary results can rationalize the traditional role of housing, and not stocks, as the primary savings vehicle of households.

with Anne Balter and Nikolaus Schweizer
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This paper explores the potential of leveraged Exchange Traded Funds (LETFs) for long-term investors and lifecycle portfolios. Leverage can increase welfare by enabling strategies that match the risk appetite of risk-tolerant investors, or by increasing financial wealth exposure to compensate for the illiquidity of human capital. We find LETFs to be suitable for both purposes with a caveat: risks associated to LETFs make it worthwhile typically only if the investor is sufficiently risk-tolerant. We also solve a dynamic portfolio optimization problem taking leverage costs and limits into account. We find that the optimal leverage target is fairly insensitive to typical leverage costs, and that welfare gains of relaxing leverage constraints are sizeable for risk tolerant investors. In our suitability analysis we study the risks of modelling discretely leveraged returns with geometric Brownian motion, as well as the probability of LETFs crashing over horizons of up to 40 years derived from extreme value theory and historical data.

with Anne Balter and Nikolaus Schweizer
Latest version available at Netspar

When heterogeneous agents apply the same collective strategy to invest in a financial market, their individual risk preferences and labor market prospects need to be aggregated to come up with a strategy that properly accounts for heterogeneity. In this paper, we consider this collective investment problem for a planner who seeks to minimize the maximal certainty-equivalent regret across agents, thus bringing all agents as closely as possible to their individual optimum. The motivating application is the investment problem of a pension provider in a defined-contribution pension system who needs to account for heterogeneity among participants. To our knowledge, our paper is the first to address the joint problem of clustering participants into groups with similar characteristics while optimizing lifecycle strategies within each cluster.

For the financial investment problem, we assume leverage constraints, discrete trading and a restriction to deterministic lifecycle strategies. In order to compute the regret-minimizing investment strategy for a given group of agents, we apply a recent technique based on evolutionary dynamics and population games. This gives an efficient algorithm for finding optimal dynamic compromises between hundreds of agents. We find that even with relatively few clusters – say, four or five – near-optimal welfare can be achieved for all agents in our setting. Moreover, it turns out that the optimal clustering is mostly a clustering by the coefficient of risk aversion which does not depend strongly on heterogeneity in wage trajectories.