The great increase in relative wage volatility in the United States

Over the past 25 years, real average hourly wages in the United States have become substantially more volatile relative to output. Microdata from the Current Population Survey (CPS) is used to show that this increase in relative volatility is predominantly due to increases in the relative volatility of hourly wages across different groups of workers. Compositional changes of the workforce, by contrast, account for only a small fraction of the increase in relative wage volatility. Simulations with a Dynamic Stochastic General Equilibrium (DSGE) model illustrate that the observed increase in relative wage volatility is unlikely to come from changes outside of the labor market (e.g. smaller exogenous shocks or more aggressive monetary policy). By contrast, greater flexibility in wage setting due to deunionization and a shift towards performance-pay contracts as experienced by the U.S. labor market is capable of accounting for a substantial fraction of the observed increase in relative wage volatility. Greater wage flexibility also decreases the magnitude of business cycle fluctuations, suggesting an interesting new explanation for the Great Moderation.     

Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach

We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit. Our goal is to shed light on the dependence between the severity distribution of these losses and a set of macroeconomic, financial, and firm‐specific factors. To do so, we use generalized Pareto regression techniques, where both the scale and shape parameters are assumed to be functions of these explanatory variables. We perform the selection of the relevant covariates with a state‐of‐the‐art penalized‐likelihood estimation procedure relying on L₁‐penalty terms. A simulation study indicates that this approach efficiently selects covariates of interest and tackles spurious regression issues encountered when dealing with integrated time series. Lastly, we illustrate the impact of different economic scenarios on the requested capital for operational risk. Our results have important implications in terms of risk management and regulatory policy.     

Conjectural variations, symmetric equilibria and economic policy

In this paper, we consider conjectural variations in a simple static general equilibrium model under oligopolistic competition. The modeling of conjectures captures the role played by beliefs in a micro-founded model. So, the economy may have three kinds of symmetric general equilibria. Furthermore, these equilibria can be Pareto-ranked by the conjectural variation parameter. Finally, we consider the implementation of a tax on the strategic behaviors in case of balanced-budget rule. The comparative statics illustrates the idea according to which the effectiveness of the multiplier mechanism to mitigate the market distortions depends on the symmetric equilibrium considered. Therefore, the effect of the tax on the prices and economic activity depends on the degree of market power which is conjectured by the agents.     

Volatility equicorrelation: A cross-market perspective

This paper contains the first empirical application of the Dynamic Equicorrelation (DECO) model to a cross-market dataset composed of equities, bonds, foreign exchange rates and commodities during 1983–2013. The originality of our approach consists of examining the volatility equicorrelations, by updating the concept of ‘volatility surprise’. We document that the average volatility equicorrelation across markets is around 15%, while being time-varying with regime shifts before/after September 2005 and with a low mean-reversion level.     

Extreme value statistics for censored data with heavy tails under competing risks

This paper addresses the problem of estimating, from randomly censored data subject to competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in a heavy-tail framework. Asymptotic normality of the proposed estimator is established. This estimator has the form of an Aalen-Johansen integral and is the first estimator proposed in this context. Estimation of extreme quantiles of the cumulative incidence function is then addressed as a consequence. A small simulation study exhibits the performances for finite samples.