MULTIDIMENSIONAL DISTANCE‐TO‐COLLAPSE POINT AND SOVEREIGN DEFAULT PREDICTION

By focusing on sovereign defaults, this paper introduces a multidimensional distance‐to‐collapse point based on a two‐step procedure. The first step is nonparametric and provides an early warning system that signals a potential crisis whenever preselected leading indicators exceed specific thresholds. The second is parametric and incorporates the first‐step country default predictors within a probit specification. Such a two‐step procedure generalizes the distance‐to‐default à la Merton within a multidimensional setting, wherein we care about the distance of each indicator from its threshold. Empirical evidence about debt crises of emerging markets over the period 1975–2002 proves that our methodology predicts 80% of the total defaults and non‐defaults in and out of sample. Copyright © 2012 John Wiley & Sons, Ltd.     

The Impact of Innovation on Employment in Services: Evidence from Italy

This article investigates the employment impact of innovation in services, using the data gathered through the 1993-95 Italian innovation survey. The empirical evidence shows that the impact of innovation on employment varies greatly across industries and according to the level of qualification of the labour force. Among small firms and in less than a half of the service sectors considered, the employment impact of innovation is positive, particularly in industries that have a strong scientific and technological base. A negative impact of innovation on employment is, on the contrary, found among large firms, capital-intensive industries and in all financial-related sectors (banking, insurance and other financial services). In these industries the labour-saving effect of innovation seems to be linked to the widespread use of Information and Communication Technologies (ICTs) which displace the least qualified employees. In the case of Italy, an overall negative impact of innovation on employment is found. It is argued that this result is affected by the Italian economy's specialisation in the most traditional service industries.     

On the Relation between Money and Derivatives and its Application to the International Monetary Market

The paper deals with the problem of defining money in a system with derivatives. We conclude that derivatives have to be included in the definition of money, and support our conclusions with an econometric test on the New York Stock Exchange (NYSE) and Chicago Board of Trade indexes. We focus on the direct relationship between derivatives' supply and the interest rate, the analytical basis of speculative money demand introduced by Keynes and the foundation of the Fratianni-Savona model to single out the international monetary base. Consequently, monetary aggregates measured by international institutions, such as the Bank for International Settlements, underestimate the actual offshore market size. Derivatives are the primary instruments used by speculators. There is money, mainly in reserve currencies, that is not controlled and that may cause systemic instability (e.g., the recent Asian crisis).     

Tax-induced Dissimilarities Between Domestic and Foreign Mutual Funds in Italy

Using data from Italy over the period 1998–2002, this study investigates whether tax effects can account for differences in return patterns between domestic and foreign mutual funds, and if this dissimilarity translates into performance. The paper presents evidence that much of the difference between domestic and foreign funds is explained by the different tax systems. The asymmetry between the two groups, due to the fact that domestic funds are obliged to pay taxes on a daily basis while foreign funds are taxed when capital gains are collected, also affects performance. We prove that comparing pre-tax returns, Italian funds are virtually indistinguishable from their foreign counterparts in terms of risk-adjusted returns, while when comparing after-tax returns, foreign funds outperform.     

Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach

Focusing on credit risk modelling, this paper introduces a novel approach for ensemble modelling based on a normative linear pooling. Models are first classified as dominant and competitive, and the pooling is run using the competitive models only. Numerical experiments based on parametric (logit, Bayesian model averaging) and nonparametric (classification tree, random forest, bagging, boosting) model comparison shows that the proposed ensemble performs better than alternative approaches, in particular when different modelling cultures are mixed together (logit and classification tree). Copyright © 2016 John Wiley & Sons, Ltd.     

Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals

In this article, we try to realize the best compromise between in‐sample goodness of fit and out‐of‐sample predictability of sovereign defaults. To do this, we use a new regression‐tree based approach that signals impending sovereign debt crises whenever pre‐selected indicators exceed specific thresholds. Using data from emerging markets and Greece, Ireland, Portugal and Spain (GIPS) over the period 1975–2010, we show that our model significantly outperforms existing competing approaches (logit, stepwise logit, noise‐to‐signal ratio and regression trees), while balancing in‐ and out‐of‐sample performance. Our results indicate that illiquidity (high short‐term debt to reserves) and default history, together with real GDP growth and US interest rates, are the main determinants of both emerging market country defaults and the recent European sovereign debt crisis.     

Summary of Findings of the Guido Carli Association's Second International Conference

Open Economies Review -