infonet-economy

This paper studies an overlapping generations model with stochastic production and incomplete markets to assess whether the introduction of an unfunded social security system leads to a Pareto improvement. When returns to capital and wages are imperfectly correlated a system that endows retired households with claims to labor income enhances the sharing of aggregate risk between generations. Our quantitative analysis shows that, abstracting from the capital crowding-out effect, the introduction of social security represents a Pareto improving reform, even when the economy is dynamically effcient. However, the severity of the crowding-out effect in general equilibrium tends to overturn these gains.

Both institutional and private investors often have only limited flexibility in timing their investment decision. They look for investments that will ideally be independent of the timing decision. In this article, a new class of derivative products whose payoff is linked to the trend of the underlying instrument is introduced. By linking the trend to the payoff, the timing of the decision becomes less important. Therefore, trend derivatives offer some time-diversification benefits. How trend derivatives are designed and priced is shown. Due to their peculiar features, trend derivatives offer some interesting applications such as executive stock option plans.

We apply the recurrent reinforcement learning method of Moody, Wu, Liao, and Saffell (1998) in the context of the strategic asset allocation computed for sample data from US, UK, Germany, and Japan. It is found that the optimal asset allocation deviates substantially from the fixed-mix rule. The investor actively times the market and he is able to outperform it consistently over the almost two decades we analyze.

We develop an algorithm to compute asset allocations for Kahneman and Tversky’s (Econometrica, 47(2), 263–291, 1979) prospect theory. An application to benchmark data as in Fama and French (Journal of Financial Economics, 47(2), 427–465, 1992) shows that the equity premium puzzle is resolved for parameter values similar to those found in the laboratory experiments of Kahneman and Tversky (Econometrica, 47(2), 263–291, 1979). While previous studies like Benartzi and Thaler (The Quarterly Journal of Economics, 110(1), 73–92, 1995), Barberis, Huang and Santos (The Quarterly Journal of Economics, 116(1), 1–53, 2001), and Grüne and Semmler (Asset prices and loss aversion, Germany, Mimeo Bielefeld University, 2005) focussed on dynamic aspects of asset pricing but only used loss aversion to explain the equity premium puzzle our paper explains the unconditional moments of asset pricing by a static two-period optimization problem. However, we incorporate asymmetric risk aversion. Our approach allows reducing the degree of loss aversion from 2.353 to 2.25, which is the value found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992) while increasing the risk aversion from 1 to 0.894, which is a slightly higher value than the 0.88 found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992). The equivalence of these parameter settings is robust to incorporating the size and the value portfolios of Fama and French (Journal of Finance, 47(2), 427–465, 1992). However, the optimal prospect theory portfolios found on this larger set of assets differ drastically from the optimal mean-variance portfolio.

In this paper I demonstrate how one can generalize finitely many examples to statements about (infinite) classes of economic models. If there exist upper bounds on the number of connected components of one-dimensional linear subsets of the set of parameters for which a conjecture is true, one can conclude that it is correct for all parameter values in the class considered, except for a small residual set, once one has verified the conjecture for a predetermined finite set of points. I show how to apply this insight to computational experiments and spell out assumptions on the economic fundamentals that ensure that the necessary bounds on the number of connected components exist.
I argue that these methods can be fruitfully utilized in applied general equilibrium analysis. I provide general assumptions on preferences and production sets that ensure that economic conjectures define sets with a bounded number of connected components. Using the theoretical results, I give an example of how one can explore qualitative and quantitative implications of general equilibrium models using computational experiments. Finally, I show how random algorithms can be used for generalizing examples in high-dimensional problems.