Efficient markets and Bayes’ rule

Summary. A series of financial anomalies motivated the development of new theories that modify the rational expectations ideal. Two possibilities have been systematically explored. The literature on behavioral finance relaxes the assumption that agents form beliefs according to the laws of probability and assume, instead, that simpler heuristic rules are used. Another stream of the literature assumes that agents process information according to Bayes rule, but do not posses sufficient information to know the true data generating process. In this paper, Bayesian and Behavioral agents coexist and trade in a standard dynamic asset pricing model. A long-standing conjecture is demonstrated. It is shown that, under suitable assumptions, Bayesian agents drive Behavioral, non-Bayesian agents out of the market. Hence, asset prices are eventually determined under the Bayesian paradigm.Received: 3 June 2004, Revised: 17 September 2004, JEL Classification Numbers: D83.Preliminary versions have circulated under the titles Markets Favor Bayesian Models and Market Selection of Empirical Models under Limited Information. I thank Larry Blume, David Easley, Larry Epstein, Armando Gomes, Bruce Hansen, Lars Hansen, Richard Kihlstrom, Grace Koo, George Mailath, Werner Ploberger, Andrew Postlewaite and Shakeeb Khan for useful comments. I also thank participants at the NBER GE meetings, Evolutionary Finance conference in Zurich, Latin American Meetings of the Econometric Society, Stanford Institute for Theoretical Economics, the Instituto de Matematica Pura e Aplicada, the theory seminar at Brown, Chicago, Harvard-MIT, Minnesota, Penn and Wisconsin. I gratefully acknowledge the financial support from the National Science Foundation Grant SES 0109650.     

Testing the significance and the non-linearity of the Phillips trade-off in the Euro Area

This paper tests the significance and the non-linearity of the Phillips trade-off in the aggregate Euro Area, in an unobserved components model of stochastic NAIRU and trend output featuring the Phillips equation and the Okun law as main identifying equations, with quarterly data for 1970:I-2002:III. The Phillips curve turns out to be linear and its trade-off statistically significant, while non-linearity shows up in the Okun relation. The results prove to be robust to alternative lag length structures of the model, and to alternative non-linear functional forms. The trend-cycle decompositions estimated with the model capture the main features of the Euro Area’s recent macroeconomic record.First version received: 1 September 2003 / Final version received: June 2004CEMPRE - Centro de Estudos Macroeconómicos e Previsão - is supported by the Fundação para a Ciência e a Tecnologia, Portugal, through the Programa Operacional Ciência, Tecnologia e Inovação (POCTI) of the Quadro Comunitário de Apoio III, which is financed by FEDER and Portuguese funds.We thank comments on earlier versions by Fabio Canova, Miguel St Aubyn, Alvaro Almeida, Pete Richardson, Kevin Ross, and two anonymous referees. We acknowledge James D. Hamilton’s help with the confidence bands. The usual disclaimer applies.     

Learning by fund-raising

From experience, fund-raisers become more efficient solicitors. This paper offers a full characterization of the optimal solicitation strategy when there are learning economies. The characterization is recursive, and does not require equilibrium computations. Our main results are that (1) the fund-raiser may now solicit a “ net free-rider”—a donor whose contribution falls below the marginal solicitation cost; (2) the number of solicitations is non-monotonic in the learning rate; and (3) a solicitation technology with high overhead but low marginal costs is likely to be optimal if income distribution in the population is relatively homogenous. The complementary case with a decreasing returns to scale technology is also considered.     

Financialization of Housing in Brazil: New Frontiers

The financialization of housing has been increasingly identified as an important driver of social and economic change in contemporary capitalism. Focusing on the Brazilian context, this article considers the extent to which recent changes in housing regulations, policies and markets confirm or challenge narratives about the financialization of housing in the international academic debate. I argue that while many of the trends stressed in the literature are apparent, more extreme processes of financialization within the Brazilian housing sector remain limited––not only because of institutional and regulatory constraints, path dependence or political resistance, but also because of fundamental structural conditions of Brazil's position as a peripheral economy. Three different but mutually reinforcing processes are scrutinized in order to evaluate the financialization of housing and its limits in Brazil: the re‐regulation of the real estate financial sector initiated in the 1990s; the changing funding patterns among real estate companies since the mid‐2000s; and the increasing commodification of housing induced by a large‐scale and heavily subsidized housing program launched in 2009.     

Modelling Electricity Prices: International Evidence*

This article analyses the evolution of electricity prices in deregulated markets. We present a general class of models that simultaneously takes into account several factors: seasonality, mean reversion, GARCH behaviour and time‐dependent jumps. The models are applied to daily equilibrium spot prices of eight electricity markets. Eight different nested models were estimated to compare the relative importance of each factor in each of the eight markets. We find strong evidence that electricity equilibrium prices are mean‐reverting, with volatility clustering (GARCH) and with jumps of time‐dependent intensity, even after adjusting for seasonality.     

R&D Collaborations and Product Innovation*

This paper studies the relative impact on product innovation of research and development (R&D) collaborations with universities, suppliers, customers, and competitors. It argues that each type of R&D collaboration differs in terms of the breadth of new knowledge provided to the firm and in the ease of access of this new knowledge, resulting in a different impact on product innovation. As a result, it proposes that R&D collaborations with universities are likely to have the highest impact on product innovation, followed by R&D collaborations with suppliers, customers, and, finally, competitors. These arguments are tested on the R&D collaborations undertaken by a sample of 781 manufacturing firms during 1998–2002. The tests find that R&D collaborations with suppliers have the highest positive impact on product innovation, followed by collaborations with universities. Surprisingly, R&D collaborations with customers do not appear to affect product innovation, and collaborations with competitors appear to harm it. Moreover, the positive influence of R&D collaborations with universities and suppliers is sustained over the long‐term, but the negative influence of R&D collaborations with competitors is, fortunately, short‐lived. These findings indicate that ease of knowledge access, rather than breadth of knowledge, appears to drive the success of R&D collaborations for product innovation. R&D collaborations with suppliers or universities, which are characterized by relatively easy knowledge access, have a positive influence on product innovation, whereas R&D collaborations with customers or competitors, which are characterized by reduced ease in knowledge access, are not related or are even negatively related to product innovation. Moreover, to achieve product innovation with the help of R&D collaborations, it appears that the collaboration must first have mechanisms in place to facilitate the transfer of knowledge; once these are in place, it is better if the partner has a relatively narrow knowledge base. Thus, while R&D collaborations with both suppliers and universities are positively related to product innovation, the narrow knowledge base provided by collaborations with suppliers appears to have a larger positive impact on product innovation than the wider knowledge base provided by collaborations with universities. These arguments and findings are important and novel. The paper is one of the first to theoretically explain and empirically show that various types of collaborations have a differential influence on product innovation. It goes beyond previous literature by providing a theoretical logic for ranking the likely impact of types of collaborations on product innovation. The study also suggests to managers to carefully select the partners for their firms' R&D collaborations. Collaborations with suppliers appear to be the most promising for product innovation, followed by collaborations with universities, whereas collaborations with competitors may be detrimental to product innovation.     

From posteriors to priors via cycles

We present new necessary and sufficient conditions for checking if a set of players' posteriors may come from a common prior. A simple diagrammatic device calculates the join and meet of players' knowledge partitions. Each cycle in the diagram has a corresponding cycle equation. Posteriors are consistent with a common prior if and only if all cycle equations are satisfied. We prove that in games of two players, where the join partition has only singletons, a common prior exists if each player's distribution of beliefs over the elements of her opponent's partition is independent of her own private information.     

Measuring Value‐at‐Risk in Project Finance Transactions

Despite the remarkable importance of project finance in international financial markets, no quantitative models to measure and quantify the risk associated with a deal for the project's lenders have been developed yet. The topic has recently become crucial, since the New Basle Capital Accord gives banks a choice of whether to adopt simpler (but possibly higher) standard capital requirements or to develop internal rating models for project finance transactions. The paper proposes how Monte Carlo simulations may be used to derive a Value‐at‐Risk estimate for project finance deals and discusses the critical issues that must be considered when developing such a model.