Economics and Finance

The global financial system is a complex network with many stakeholders and nonlinear feedback interactions among them. A core component of the global financial system is the major stock markets, the crashes of which are rare events that are driven by large-scale collective behavior, and are accompanied by high magnitude of both social and economic consequences (Bluedorn et al. (2013), Farmer (2012)).

This work aims to explain how firms behave and select their partners in the Tokyo Stock Exchange (TSE) production network (network of suppliers and customers). Based on the microscopic interactions between firms, we look at the mechanism behind the emergence of the topology of the TSE production network. The supplier-customer network of listed firms in Japan (3,189 firms with 20,369 links) is estimated by the so-called exponential random graph model (ERGM).

The fundamental issue of an investor is to find the best possible portfolio given her/his constraints. In order for an investor to build such portfolio, she/he has to predict the risk and the return of the its assets. The work on risk measurement is generally associated to the portfolio optimization techniques over the covariance matrix.

We introduce a new factor model for log volatilities that performs dimensionality reduction and considers contributions globally through the market, and locally through cluster structure and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical Tree (DBHT) algorithm, which originates from complex systems, to fix the number of factors. We use the factor model and a new integrated non parametric proxy to study how volatilities contribute to volatility clustering.

Following the work of F. Pozzi et al. [1] who provided evidence that stocks lying at the periphery of a stock network tend to outperform those lying at the center, in this work we attempt to create an algorithmic stock selection and portfolio construction strategy based on stock network analysis.

Nowadays, quantification of complex time series, in terms of multifractality, finds a multitude of applications in diverse areas. However, thus far the majority of the related studies presented in the scientific literature often focus on analysis of singularity spectrum width as a signature of the hierarchical organization and measure of the complexity degree. The time series generated by intricate processes may include many convoluted components with different hierarchy. Even more, contribution of such components in aggregated signal vary in time and results in its sophisticated dynamics.

The aim of this work is to shed light on the relationship between firms’ performance and their technological portfolios using tools borrowed from the complexity science. In particular, we ask whether the accumulation of knowledge and capabilities related to a coherent set of technologies leads firms to experience advantages in terms of productive efficiency. To this end, we analyzed both the balance sheets and the patenting activities of about 70 thousand firms that have filed at least one patent over the period 2004-2013.

In this work [arXiv:1707.05146] we show that the space in which scientific, technological and economic developments interplay with each other can be mathematically shaped using pioneering multilayer network and complexity techniques. We build the tri-layered network (see Fig.1) of human activities (research, patenting, and industrial production) and study the interactions among them, also taking into account the possible time delays.

Like many emerging economies, the productive structure of the Paraguayan economy is not complex. It relies extensively on low value-added activities in the primary sector such as agriculture and cattle ranching. In 2014, these activities represented more than 51% of the exports registered in Paraguay. These activities have a lower return in terms of economic and social benefits than other potential productive activities and do not contribute to increasing capability accumulation.