An extension of the multi-output state-contingent production model

This paper presents new developments on the state-contingent theory of production under uncertainty with stochastic prices. Our main purpose is to generalize the usual finite discrete state-contingent production model to infinite dimensional, possibly uncountable spaces which look like a more realistic framework. Usual duality results are established in this general context, shedding some light on the links between risk-neutral probabilities and shadow prices. A direct generalized production risk premium is defined and is shown to be independent of the inputs level when the technology is output translation homothetic. In such a case, the technology exhibits constant absolute riskiness. We thank Bob Chambers for his helpful comments.     

The possibilities and limits of telework in a bureaucratic environment: lessons from the public sector

By modifying the spatial and temporal framework of work, telework involves a reorganisation of the conduct of work that affects the employment relationship in different ways. In this paper, we focus on control issues. Based on the empirical study of two experiences led in the public sector, we illustrate the necessity to redefine the rules of control and to recast managerial practices and role.     

A view of the European Union as an evolving country portfolio

We apply portfolio theory to assess the consequences in terms of risk sharing of the evolution of the industry mix of European Union countries between 1986 and 1997, and of the changes in the membership configurations of both economic and monetary unions. We compute a measure of risk-return performance for EU countries, by considering countries as collections of industries, and observe that risk-return performance improved in most countries. We find that the EU9 is marginally more efficient than the other historical EU groupings and that the Euro Zone might slightly benefit from the inclusion of the United Kingdom.     

Equal opportunity and poverty reduction: How should aid be allocated?

This paper analyzes a model of aid allocation equalizing the opportunity between recipient countries to reach a common poverty reduction goal. We propose a fair and efficient aid allocation based on a multicriteria principle. The model considers structural handicaps in recipient countries in terms of lack of human capital and economic vulnerability, their initial poverty, and the natural gap between the growth rate required to reach a development goal and the observed one. We show that our proposed aid allocation favors poor and vulnerable countries with our multicriteria principle. It substantially differs from the observed allocation. Analyses also shed light on the impact of the donors' aversion to the low natural growth gap in recipient countries on the optimal aid allocation and the marginal efficiency of aid.     

The pernicious effects of contaminated data in risk management

Banks hold capital to guard against unexpected surges in losses and long freezes in financial markets. The minimum level of capital is set by banking regulators as a function of the banks’ own estimates of their risk exposures. As a result, a great challenge for both banks and regulators is to validate internal risk models. We show that a large fraction of US and international banks uses contaminated data when testing their models. In particular, most banks validate their market risk model using profit-and-loss (P/L) data that include fees and commissions and intraday trading revenues. This practice is inconsistent with the definition of the employed market risk measure. Using both bank data and simulations, we find that data contamination has dramatic implications for model validation and can lead to the acceptance of misspecified risk models. Moreover, our estimates suggest that the use of contaminated data can significantly reduce (market-risk induced) regulatory capital.     

Robust estimation of intraweek periodicity in volatility and jump detection

Opening, lunch and closing of financial markets induce a periodic component in the volatility of high-frequency returns. We show that price jumps cause a large bias in the classical periodicity estimators and propose robust alternatives. We find that accounting for periodicity greatly improves the accuracy of intraday jump detection methods. It increases the power to detect the relatively small jumps occurring at times for which volatility is periodically low and reduces the number of spurious jump detections at times of periodically high volatility. We use the series of detected jumps to estimate robustly the long memory parameter of the squared EUR/USD, GBP/USD and YEN/USD returns.