Organizations & Markets

We propose an exogenous measure of a country's growth opportunities by interacting the country's local industry mix with global price to earnings (PE) ratios. First, we find that these exogenous growth opportunities strongly predict future changes in real GDP and investment in a large panel of countries. This relation is strongest in countries that have liberalized their capital accounts, equity markets, and banking systems. Second, we re-examine the link between financial development, external finance dependence, investor protection, capital allocation, and growth.

We examine the effects of both equity market liberalization and capital account openness on real consumption growth variability. We show that financial liberalization is mostly associated with lower consumption growth volatility. Our results are robust, surviving controls for business-cycle effects, economic and financial development, the quality of institutions, and other variables. Countries that have more open capital accounts experience a greater reduction in consumption growth volatility after equity market openings.

Everyone who has studied international equity returns has noticed the episodes of high volatility and unusually high correlations coinciding with a bear market. We develop quantitative models of asset returns that match these patterns in the data and use them in two quantitative asset allocation analyses. First, we show that the presence of regimes with different correlations and expected returns is difficult to exploit with within a global asset allocation framework focussed on equities. The benefits of international diversification dominate the costs of ignoring the regimes.

Gallant, Hansen, and Tauchen (1990)Go show how to use conditioning information optimally to construct a sharper unconditional variance bound (the GHT bound) on pricing kernels. The literature predominantly resorts to a simple but suboptimal procedure that scales returns with predictive instruments and computes standard bounds using the original and scaled returns. This article provides a formal bridge between the two approaches. We propose an optimally scaled bound that coincides with the GHT bound when the first and second conditional moments are known.

We argue that the root cause behind the recent corporate scandals associated with CEO pay is the technology bubble of the latter half of the 1990s. Far from rejecting the optimal incentive contracting theory of executive compensation, the recent evidence on executive pay can be reconciled with classical agency theory once one expands the framework to allow for speculative stock markets.

The telecommunications industry is a fragmented market, characterized by a tremendous amount of customer heterogeneity. This paper shows how such customer heterogeneity dramatically affects nonlinear pricing strategies: (i) First, if there are unbalanced calling patterns between different customer types, networks make larger profits on the least attractive customers. In addition, the nature of the calling pattern substantially affects how networks discriminate implicitly between different customer types.

We present a simple model where a firm will commit to a strictly positive hurdle rate on investment proposals by managers even though the two parties are symmetrically informed about the investments' profitability. Facing a positive hurdle rate, a manager who derives partial benefits from the investment profits will have more incentive to collect information about the projects. The optimal hurdle rate trades off the benefit of more information with the cost of foregoing ex post positive Net Present Value (NPV) projects.

We propose a dynamic equilibrium model of asset prices and trading volume when agents face fixed transactions costs. We show that even small fixed costs can give rise to large "no-trade" regions for each agent's optimal trading policy. The inability to trade more frequently reduces the agents' asset demand and in equilibrium gives rise to a significant illiquidity discount in asset prices.

This paper considers the problem of sequential parameter and state estimation in stochastic volatility jump diffusion models. We describe the existing methods, the particle and practical filter, and then develop algorithms to apply these methods to the case of stochastic volatility models with jumps. We analyze the performance of both approaches using both simulated and S and P 500 index return data. On simulated data, we find that the algorithms are both effective in estimating jumps, volatility, and parameters.