HASSE Jean Baptiste

In this paper, we reexamine the predictive power of the yield spread across countries and over time. Using a dynamic panel/dichotomous model framework and a unique dataset covering 13 OECD countries over a period of 45 years, we empirically show that the yield spread signals recessions. This result is robust to different econometric specifications, controlling for recession risk factors and time sampling. Using a new cluster analysis methodology, we present empirical evidence of a partial homogeneity of the predictive power of the yield spread. Our results provide a valuable framework for monitoring economic cycles.

This paper thesis is composed of four self-contained chapters, contributing to the field of nonlinear econometrics. The first chapter focuses on the contribution of nonlinear econometrics through the measurement of financial performance using a dichotomous variable as the independent variable. The next three chapters are based on nonlinear regression models where the dichotomous variable is the dependent variable of the equation. Given the links between financial risk and the macroeconomic context, this part is linked to the theme of optimal allocation via the study of crises and recessions. This class of model (probit / logit) is used in the second chapter to study empirically the role of financial development in the probability of occurrence of banking crises. Then the last two chapters focus on the methodological framework developed by Kauppi and Saikkonen (2008) and Candelon, Dumitrescu and Hurlin (2012 . 2014) about forecasting business cycles from probit / logit models. Thus, the third chapter studies the empirical relationship linking the evolution of the credit spread and the future probability of expansion/recession in an extended data panel while testing the homogeneity of this relationship. Finally, the fourth chapter proposes a theoretical contribution by deriving the response functions of probit / logit models from the approach of Kauppi and Saikkonen (2008). These response functions are then used in an empirical framework to estimate the impact of an exogenous shock on the expansion/recession cycle.

We propose a new measure of systemic risk based on interconnectedness, defined as the level of direct and indirect links between financial institutions in a correlation-based network. Deriving interconnectedness in terms of risk, we empirically show that within a financial network, indirect links are strengthened during systemic events. The relevance of our measure is illustrated at both local and global levels. Our framework offers policymakers a useful toolbox for exploring the real-time topology of the complex structure of dependencies in financial systems and for measuring the consequences of regulatory decisions.

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This thesis, articulated in three chapters, aims to study the structural interdependencies between different financial markets. In Chapter 1, we study the architecture of the interdependencies between the main European markets. By modeling these interdependencies through dynamic networks, we propose a new methodology to measure, as a function of time, the direct and indirect links connecting each pair of elements within a given system. This topological measure allows us to evaluate the hierarchy of a system and its level of organization, thus constituting a complexity proxy. Our index is based on Simon's definition of complexity which links the complexity of a system to its hierarchical level of organization. We validate the relevance of our index by empirically studying the link between complexity and uncertainty. In Chapter 2, we study the impact of globalization on the economy, as an increase in interdependencies in a dynamic panel of 94 countries, from 1970 to 2011. Our contribution is to estimate the endogenized threshold of globalization on a dynamic panel. Finally, we study the role of economic uncertainty on the sovereign debt market: Chapter 3 exposes the impact of economic uncertainty on the level of sovereign rates in the Eurozone. Thus, this thesis studies the role of complexity and uncertainty on the structure of financial interdependencies.