A sovereign risk index for the Eurozone based on stochastic dominance

Abstract

We propose a new method to assess sovereign risk in Eurozone countries using an approach that relies on consistent tests for stochastic dominance e¢ ciency. The test statistics and the estimators are computed using mixed integer programming methods. Our analysis is based on macroeconomic fundamentals and their importance in accounting for sovereign risk. The results suggest that net international investment position/GDP and public debt/GDP are the main contributors to country risk in the Eurozone. We also conduct ranking analysis of countries for …scal and external trade risk. We …nd a positive correlation between our rankings of the most vulnerable countries and the S&P's ratings, whereas the correlation for other countries is weaker.JEL Classi…cations: C12; C13; C14; C15; G01

Measuring the presence of organized crime across Italian provinces: a sensitivity analysis

“…In a related literature in finance, a more general, multivariate problem is that of testing whether a given portfolio is stochastically efficient relative to all mixtures of a discrete set of alternatives (Post 2003;Kuosmanen 2004;Roman et al 2006), while others address this problem with various proposed SDE tests (Post and Versijp 2007;Scaillet and Topaloglou 2010;Linton et al 2014;Arvanitis and Topaloglou 2017;Fang and Post 2017;Post and Poti 2017). These SDE tests are used to examine the existence of alternative ways of combining assets that dominate the benchmark market or welfare index to obtain best-and worst-case scenarios of wellbeing (e.g., Pinar et al 2013Pinar et al , 2015Pinar et al , 2017Pinar et al , 2019Agliardi et al 2015;Pinar 2015;Mehdi 2019) and risk indices (see e.g., Agliardi et al 2012Agliardi et al , 2014. For instance, Pinar et al (2013) used SDE methodology to obtain the best-case scenario combination of dimensions of the Human Development Index (HDI), where a full diversification of weights of HDI dimensions were used to obtain the most optimistic measurement of HDI among countries.…”

Sensitivity of University Rankings: Implications of Stochastic Dominance Efficiency Analysis

“…For instance, Saisana et al (2011) use three alternative weighting (i.e., factor analysis derived weights, equal weighting and "university-specific weighting" that maximizes that university's performance relative to all other universities).4 The most popular use of the pair-wise SD analysis in the literature is to determine which population is better in terms of a given well-being dimension compared to the other (see e.g.,Atkinson, 1970; Shorrocks, 1983;Kakwani, 1984;Atkinson, 1987;Foster and Shorrocks, 1988;Ravallion, 1994;Davidson and Duclos, 2000;Barrett and Donald, 2003; Agliardi et al, 2017 among many others). This SD comparisons has moved to a multivariate one by analyzing various welfare dimensions and portfolios (see e.g.,Post, 2003;Kuosmanen, 2004;Linton et al, 2005;Duclos et al, 2006;Agliardi et al, 2012;Pinar et al, 2013;Delgado and Escanciano, 2013;Agliardi et al, 2014;Gonzalo and Olmo, 2014;Linton et al, 2014; Yalonetzky, 2014;Agliardi et al, 2015;Pinar, 2015;Pinar et al, 2015;Pinar et al, 2017 among many others).5 For instance,Bazen and Moyes (2012), and Carayol and Lahatte (2014) use pair-wise SD tests to compare the distribution of publication performance of sta↵ members to rank these institutes. In their application, both the quality and quantity of publications are taken into account and all possible pairs of institutes are compared based on the publication performance.…”

Stochastic dominance spanning and augmenting the human development index with institutional quality

“…In a related literature in finance, a more general, multivariate problem is that of testing whether a given portfolio is stochastically efficient relative to all mixtures of a discrete set of alternatives (Kuosmanen, 2004;Post, 2003;Roman et al, 2006), while others address this problem with various proposed SD efficiency (SDE) tests (Post & Versijp, 2007;Scaillet & Topaloglou, 2010;Lizyayev, 2012;Linton et al, 2014;Arvanitis & Topaloglou, 2017;Fang &Post &Poti, 2017). These SDE tests are used to examine the existence of alternative ways of combining assets that dominate the benchmark market or welfare index to obtain best-and worst-case scenarios of well-being (e.g., Pinar et al, 2013;Agliardi et al, 2015;Pinar et al, 2015;Pinar et al, 2017;Mehdi, 2019) and risk indices (see e.g., Agliardi et al, 2012Agliardi et al, , 2014. Furthermore, recent studies also obtain forecast combinations that offered a lower expected loss than other forecast combinations for all permissible loss functions (see e.g., Jin et al, 2017;Post et al, 2019), and for different quantiles of the expected loss function (see e.g., Corradi & Swanson, 2013;Pinar et al, 2018).…”