Philippe de Peretti

TESTING THE SIGNIFICANCE OF THE DEPARTURES FROM UTILITY MAXIMIZATION

This paper introduces a general procedure that tests the significance of the departures from utility maximization, departures defined as violations of the general axiom of revealed preference (GARP). This general procedure is based on (i) an adjustment procedure that computes the minimal perturbation in order to satisfy GARP by using the information content in the transitive closure matrix and (ii) a test procedure that checks the significance of the necessary adjustment. This procedure can be easily implemented and programmed, and we run Monte Carlo simulations to show that it is quite powerful.

A Comparison Of Two Methods For Testing The Utility Maximization Hypothesis When Quantity Data Are Measured With Error

The Generalized Axiom of Revealed Preference (GARP) can be violated because of random measurement errors in the observed quantity data. We study two tests proposed by Varian (1985) and de Peretti (2004), which test GARP within an explicit stochastic framework. Both tests compute adjusted quantity data that are compliant with GARP. We compare and contrast the two tests in theoretical terms and in an empirical application. The empirical application is based on testing a large group of monetary assets for the United States over multiple sample periods spanning 1960-1992. We found that both tests provided reasonable results and were largely consistent with each other.

Are critical slowing down indicators useful to detect financial crises

In this article, we consider financial markets as complex dynamical systems, and check whether the critical slowing down indicators can be used as early warning signals to detect a phase transition. Using various rolling windows, we analyze the evolution of three indicators: i) First-order autocorrelation, ii) Variance, and iii) Skewness. Using daily data for ten European stock exchanges plus the United States, and focusing on the Global Financial Crisis, our results are mitigated and depend both on the series used and the indicator. Using the main log-indices, critical slowing down indicators seem weak to predict the Global Financial Crisis. Using cumulative returns, for almost all countries, an increase in variance and skewness does precede the crisis. However, first-order autocorrelations of both log-indices and cumulative returns do not provide any useful information about the Global Financial Crisis. Thus, only some of the reported critical slowing down indicators may have informational content, and could be used as early warnings.