RISK MEASURES: RATIONALITY AND DIVERSIFICATION

When there is uncertainty about interest rates (typically due to either illiquidity or defaultability of zero coupon bonds) the cash‐additivity assumption on risk measures becomes problematic. When this assumption is weakened, to cash‐subadditivity for example, the equivalence between convexity and the diversification principle no longer holds. In fact, this principle only implies (and it is implied by) quasiconvexity. For this reason, in this paper quasiconvex risk measures are studied. We provide a dual characterization of quasiconvex cash‐subadditive risk measures and we establish necessary and sufficient conditions for their law invariance. As a byproduct, we obtain an alternative characterization of the actuarial mean value premium principle.     

Firm Size and Cooperation: The Determinants of Cooperative Agreements in Information Technology Industries

This paper aims to shed light on factors that lead firms to resort to collaborative ventures. Specific attention is devoted to the role of firm size. The study relies on an extensive econometric analysis of a representative sample of firms in three Information Technology industries (semiconductor, data processing, and telecommunications). Data relating to the number and type of agreements concluded by firms, provided by the ARPA database developed at Politecnico di Milano, are correlated with firm–, industry– and country–specific variables. The findings of the econometric estimates point to the importance of firm size, scope of activity within the industries considered, diversification in related industries, and, to a less extent, degree of internationalization in explaining use of agreements. Moreover, the study provides preliminary evidence in support of a complementary relation between interfirm linkages and R&D expenditures. A positive correlation with growth rates also seems to emerge. In contrast, the s regarding the relation between internal capital expenditures and recourse to agreements are mixed.     

Firm size and the adoption of flexible automation

Data on the diffusion of flexible automation systems in the Italian metalworking industry are used to point out the role of size for the adoption of complex, systemic innovations. Plant and firm sizes turn out to be positively correlated to adoption rates. Moreover, adoption is more likely, ceteris paribus, in plants which are part of large industrial groups. This pattern applies in general, and is particularly remarkable for more advanced solutions. In this light, guidelines for industrial policies that focus on financial support, the diffusion of industrial standards, and the correction of asymmetries in the availability of know-how and skills are proposed.Financial support for this paper was provided by the Ministero della Pubblica Istruzione (MPI 40% research funds). Grants from the Fondazione Mauro and the CNR research project Tecnologie Meccaniche are also acknowledged. The research took advantage of the FLAUTO database, set up at the Laboratorio di Economia dei Processi di Automazione, MIP-Politecnico di Milano.     

Alternative econometric implementations of multi-factor models of the U.S. financial markets

This paper analyzes the empirical performance of two alternative ways in which multi-factor models with time-varying risk exposures and premia may be estimated. The first method echoes the seminal two-pass approach introduced by Fama and MacBeth (1973). The second approach is based on a Bayesian latent mixture model with breaks in risk exposures and idiosyncratic volatility. Our application to monthly, 1980–2010 U.S. data on stock, bond, and publicly traded real estate returns shows that the classical, two-stage approach that relies on a nonparametric, rolling window estimation of time-varying betas yields results that are unreasonable. There is evidence that most portfolios of stocks, bonds, and REITs have been grossly over-priced. On the contrary, the Bayesian approach yields sensible results and a few factor risk premia are precisely estimated with a plausible sign. Predictive log-likelihood scores indicate that discrete breaks in both risk exposures and variances are required to fit the data.     

A DYNAMIC ENTRY AND PRICE GAME WITH CAPACITY INDIVISIBILITY

Strategic market interaction is here modelled as a two‐stage game in which potential entrants choose capacities and next active firms compete in prices. Due to capital indivisibility, the capacity choice is made from a finite grid and there are economies of scale. In the simplest version of the model with a single production technique, the equilibrium turns out to depend on the ratio between the level of total output at the long‐run competitive equilibrium and the firm's minimum efficient scale: if that ratio is sufficiently large (the market is sufficiently ‘large’), then the competitive price emerges at a subgame‐perfect equilibrium of the capacity and price game; if not, then the firms randomize in prices on the equilibrium path. The role of the market size for the competitive outcome is shown to be even more important if there are several available production techniques.     

Market Organization and Propagation of Shocks: The Furniture Industry in Germany and Italy

In this paper we study the furniture industry in two European countries, Germany and Italy. Although the two industries are characterized by very similar output and technology, they differ widely in terms of market organization, most notably the distribution of firms by size, and the organization of retail. We find some evidences that these differences have an influence on the dynamic behavior of industry output, prices and exports in the two countries.     

High equity premia and crash fears - Rational foundations

Summary. We show that in a Lucas endowment economy in which the process for dividends is described by a lattice tree subject to infrequent but observable structural breaks, in equilibrium recursive rational learning may inflate the equity risk premium and reduce the risk-free interest rate for low levels of risk aversion. The key condition for these results to obtain is the presence of sufficient initial pessimism. The relevance of these findings is magnified by the fact that under full information our artificial economy cannot generate asset returns matching the empirical evidence for any positive relative risk aversion.Received: 20 September 2003, Revised: 27 April 2005, JEL Classification Numbers: D83, E44, G12.I am indebted to Mordecai Kurz (the co-Editor), one anonymous Referee, Gabriele Camera, Sadayuki Ono, Olesia Verchenko, and Allan Timmermann for many useful comments. I also think seminar paticipants at the European Economic Association meetings in Stockholm (August 2003) and the Midwest Economics Association in St. Louis (March 2003).     

Efficiency Measurement in Network Industries: Application to the Swiss Railway Companies

The persistence of increasingly high government subsidies in Switzerland’s railroads has led the federal and cantonal authorities to discussing the possibility of high-powered incentive contracts such as those based on cost efficiency benchmarking. Railways are however, characterized by a high degree of unobserved heterogeneity that could bias the efficiency estimates. This paper examines the performance of several panel data models to measure cost efficiency in network industries. The unobserved firm-specific effects and the resulting biases are studied through a comparative study of several stochastic frontier models, applied to a panel of 50 railway companies operating over a 13-year period.* The authors wish to thank Michael Crew and two anonymous referees for their helpful suggestions. Aurelio Fetz provided an excellent assistance, which is gratefully acknowledged. Any remaining errors are the responsibility of the authors.This revised version was published in June 2005 with corrections in the author affiliations.     

Monotone continuous multiple priors

Summary. In a multiple priors model á la Gilboa and Schmeidler (1989), we provide necessary and sufficient behavioral conditions ensuring the countable additivity and non-atomicity of all priors.Received: 25 November 2002, Revised: 24 June 2004, JEL Classification Numbers: D81.We thank Roko Aliprantis, Maristella Botticini, Erio Castagnoli, Larry Epstein, Paolo Ghirardato, Itzhak Gilboa, Luigi Montrucchio, David Schmeidler, Marciano Siniscalchi, an Associate Editor, and an anonymous referee for helpful discussions. Fabio Maccheroni and Massimo Marinacci gratefully acknowledge the financial support of MIUR and NOMOS Sistema (Milano).     

APPLICATION OF PANEL DATA MODELS IN BENCHMARKING ANALYSIS OF THE ELECTRICITY DISTRIBUTION SECTOR*

ABSTRACT ** : This paper explores the application of several panel data models in measuring productive efficiency of the electricity distribution sector. Stochastic Frontier Analysis has been used to estimate the cost‐efficiency of 59 distribution utilities operating over a nine‐year period in Switzerland. The estimated coefficients and inefficiency scores are compared across three different panel data models. The results indicate that individual efficiency estimates are sensitive to the econometric specification of unobserved firm‐specific heterogeneity. This paper shows that alternative panel models such as the ‘true’ random effects model proposed by Greene (2005) could be used to explore the possible impacts of unobserved firm‐specific factors on efficiency estimates. When these factors are specified as a separate stochastic term, the efficiency estimates are substantially higher suggesting that conventional models could confound efficiency differences with other unobserved variations among companies. On the other hand, refined specification of unobserved heterogeneity might lead to an underestimation of inefficiencies by mistaking potential persistent inefficiencies as external factors. Given that specification of inefficiency and heterogeneity relies on non‐testable assumptions, there is no conclusive evidence in favour of one or the other specification. However, this paper argues that alternative panel data models along with conventional estimators can be used to obtain approximate lower and upper bounds for companies' efficiency scores.