When did the 2001 recession really start?

The paper develops a non-parametric, non-stationary framework for business-cycle dating based on an innovative statistical methodology known as Adaptive Weights Smoothing (AWS). The methodology is used both for the study of the individual macroeconomic time series relevant to the dating of the business cycle as well as for the estimation of their joint dynamic. Since the business cycle is defined as the common dynamic of some set of macroeconomic indicators, its estimation depends fundamentally on the group of series monitored. We apply our dating approach to two sets of US economic indicators including the monthly series of industrial production, nonfarm payroll employment, real income, wholesale-retail trade and gross domestic product (GDP). We find evidence of a change in the methodology of the NBER's Business- Cycle Dating Committee: an extended set of five monthly macroeconomic indicators replaced in the dating of the last recession the set of indicators emphasized by the NBER's Business-Cycle Dating Committee in recent decades. This change seems to seriously affect the continuity in the outcome of the dating of business cycle. Had the dating been done on the traditional set of indicators, the last recession would have lasted one year and a half longer. We find that, independent of the set of coincident indicators monitored, the last economic contraction began in November 2000, four months before the date of the NBER's Business-Cycle Dating Committee.business cycle, non-parametric smoothing, non-stationarity

Consistency of Hill's Estimator for Dependent Data

This paper published in "Computational Optimization and Applications" 3 (1994) 305-31

Asymptotic Behavior of Hill's Estimator for Autoregressive Data

Asymptotic Behavior of Hill's Estimator for Autoregressive Dat

Smoothing the Hill Estimator

Smoothing the Hill Estimato

Limit theory for the sample autocorrelations and extremes of a GARCH (1,1) process

The asymptotic theory for the sample autocorrelations and extremes of a GARCH(I, 1) process is provided. Special attention is given to the case when the sum of the ARCH and GARCH parameters is close to 1, that is, when one is close to an infinite Variance marginal distribution. This situation has been observed for various financial log-return series and led to the introduction of the IGARCH model. In such a situation, the sample autocorrelations are unreliable estimators of their deterministic counterparts for the time series and its absolute values, and the sample autocorrelations of the squared time series have nondegenerate limit distributions. We discuss the consequences for a foreign exchange rate series

The cost of sustainability in optimal portfolio decisions

We examined the impact of including sustainability-related constraints in optimal portfolio decision-making. Our analysis covered an investment set containing the components of the S&P500 index from 1993 to 2008. Optimizations were performed according to the classic mean–variance approach, while sustainability constraints were introduced by eliminating, from the investment pool, those assets that do not comply with the given social responsibility criteria (screening). We compared the efficient frontiers with and without screening. The analysis focused on the three main dimensions of sustainability, namely the environmental, social and governance ones. We found that socially responsible screening gives rise to a small loss in terms of the Sharpe ratio even though it has a great impact on the market capitalization of the optimal portfolio. The spanning test showed that the ex-post differences between the two frontiers, when short selling is not allowed, are significant only in the case of environmental screening