Modeling interdependence between climatic factors, commodities, and financial markets.

This paper introduces a comprehensive approach to studying the impact of climate-related factors on commodity and financial markets using network analysis. We utilize a Bayesian network Vector Autoregressive model to investigate whether climate risk significantly influ-ences commodity prices and financial market returns. Our findings provide evidence of a climate effect on major commodities and global financial markets. Specifically, we identify Crude oil, Cotton, and Sugar as the commodities most affected by climate risk, with Gold demonstrating the least susceptibility. Additionally, we observe that climate-related risk on commodities is likely propagated by patterns such as PNA, NN1, and AO. In terms of financial markets, we find that stock markets in Hong Kong, India, and Spain are the most susceptible to climate risk, while Switzerland's market appears to be the least affected. Furthermore, we document evidence that climate-related risk capable of altering financial markets is likely propagated by factors like ENP, NN1, and WH. Overall, our study underscores the intricate relationship between climate factors and market dynamics, highlighting the importance of considering climate risk in assessing market behavior and performance.

Network based evidence of the financial impact of Covid-19 pandemic.

How much the largest worldwide companies, belonging to different sectors of the economy, are suffering from the pandemic? Are economic relations among them changing? In this paper, we address such issues by analyzing the top 50 S&P companies by means of market and textual data. Our work proposes a network analysis model that combines such two types of information to highlight the connections among companies with the purpose of investigating the relationships before and during the pandemic crisis. In doing so, we leverage a large amount of textual data through the employment of a sentiment score which is coupled with standard market data. Our results show that the COVID-19 pandemic has largely affected the US productive system, however differently sector by sector and with more impact during the second wave compared to the first.

Risk management via contemporaneous and temporal dependence structures with applications.

This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling with and without exogenous variables (BG-SEM(X)), which are developed to examine risk network structures embedded in multivariate time series. This methodical approach allows for the analysis of various dynamics and persistence in the multivariate time series in terms of risk propagation. For instance, both the BG-SEMX and BG-VARX can reveal the within-day and across-day major risk transmitters as well as risk recipients from other univariate time series, which better explain risk contagion using complex network models. In addition, the procedures for models with and without exogenous variables have been explored, which shows that the former produce more network structures compared to the latter and therefore depict their influential role. This approach, therefore, provides a platform for future research in terms of extension of the method to encompass different types of multivariate data with additional innovations that might aid feasible analysis and the design of policy instruments and the implementation of relevant policy implications.•Development and application of innovative network models that enhances the efficient analysis of multivariate time series data.•Estimation of intra-day and inter-day interconnection from a daily multivariate time series data and their dynamics and persistence from contagion analysis viewpoint.

Factorial Network Models to Improve P2P Credit Risk Management.

This paper investigates how to improve statistical-based credit scoring of SMEs involved in P2P lending. The methodology discussed in the paper is a factor network-based segmentation for credit score modeling. The approach first constructs a network of SMEs where links emerge from comovement of latent factors, which allows us to segment the heterogeneous population into clusters. We then build a credit score model for each cluster via lasso-type regularization logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 1,5000 SMEs engaged in P2P lending services across Europe. The result reveals that credit risk modeling using our network-based segmentation achieves higher predictive performance than the conventional model.