if this message does not display correctly, click here | Table of Contents Gianluca Cubadda, University of Rome Tor Vergata - Department of Economics and Finance
Francesco Giancaterini, Maastricht University
Alain Hecq, Maastricht University - Department of Quantitative Economics
Joann Jasiak, York University - Department of Economics Alberto Bucci, University of Milan - Department of Business Policy and Economics
Lorenzo Carbonari, Università di Roma "Tor Vergata"
Giovanni Trovato, University of Rome Tor Vergata - Faculty of Economics
Pedro Trivin, University of Milan - Department of Economics, Management and Quantitative Methods (DEMM) Tommaso Proietti, University of Rome II - Department of Economics and Finance | |
CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Furio Camillo Rosati - Director "Optimization of the Generalized Covariance Estimator in Noncausal Processes" 
CEIS Working Paper No. 574 GIANLUCA CUBADDA, University of Rome Tor Vergata - Department of Economics and Finance
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FRANCESCO GIANCATERINI, Maastricht University
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ALAIN HECQ, Maastricht University - Department of Quantitative Economics
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JOANN JASIAK, York University - Department of Economics
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This paper investigates the performance of routinely used optimization algorithms in application to the Generalized Covariance estimator (GCov) for univariate and multivariate mixed causal and noncausal models. The GCov is a semi-parametric estimator with an objective function based on nonlinear autocovariances to identify causal and noncausal orders. When the number and type of nonlinear autocovariances included in the objective function are insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise. These issues result in local minima in the objective function, which correspond to parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point and the optimization algorithm employed, the algorithm can converge to a local minimum. The paper proposes the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that SA performs well in its application to mixed causal and noncausal models, successfully eliminating the effects of local minima. The proposed approach is illustrated by an empirical study of a bivariate series of commodity prices. "Human Capital-based Growth with Depopulation and Class-size Effects: Theory and Empirics"
CEIS Working Paper No. 575 ALBERTO BUCCI, University of Milan - Department of Business Policy and Economics
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LORENZO CARBONARI, Università di Roma "Tor Vergata"
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GIOVANNI TROVATO, University of Rome Tor Vergata - Faculty of Economics
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PEDRO TRIVIN, University of Milan - Department of Economics, Management and Quantitative Methods (DEMM)
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Building on Lucas (1988), we develop a model in which the impact of population dynamics on per capita GDP and human capital depends on the balance of intertemporal altruism effects towards future generations and class-size effects on an individual’s education investment. We show that there is a critical level of the class-size effect that determines whether a decline in population growth will lead to a decrease or an increase in a country’s long-run growth rate of real per capita income. We take the model to OECD data, using a semi-parametric technique. This allows us to classify countries into groups based on their long-term growth trajectories, revealing patterns not captured by previous studies on the topic. "Ups and (Draw)Downs"
CEIS Working Paper No. 576 TOMMASO PROIETTI, University of Rome II - Department of Economics and Finance
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The concept of drawdown quantifies the potential loss in the value of a financial asset when it deviates from its historical peak. It plays an important role in evaluating market risk, portfolio construction, assessing risk-adjusted performance and trading strategies. This paper introduces a novel measurement framework that produces, along with the drawdown and its dual (the drawup), two Markov chain processes representing the current lead time with respect to the running maximum and minimum, i.e., the number of time units elapsed from the most recent peak and trough. Under relatively unrestrictive assumptions regarding the returns process, the chains are homogeneous and ergodic. We show that, together with the distribution of asset returns, they determine the properties of the drawdown and drawup time series, in terms of size, serial correlation, persistence and duration. Furthermore, they form the foundation of a new algorithm for dating peaks and troughs of the price process delimiting bear and bull market phases. The other contributions of this paper deal with out-of-sample prediction and robust estimation of the drawdown. | | ^top
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