The Econometrics of Bayesian Graphical Models: A Review With Financial Application

Recent advances in empirical finance has shown that the adoption of network theory is critical to understand contagion and systemic vulnerabilities. While interdependencies among financial markets have been widely examined, only few studies review networks, however, they do not focus on the econometrics aspects. This paper presents a state-of-the-art review on the interface between statistics and econometrics in the inference and application of Bayesian graphical models. We specifically highlight the connections and possible applications of network models in financial econometrics, in the context of systemic risk

The Econometrics of Bayesian Graphical Models: A Review With Financial Application

Recent advances in empirical finance has shown that the adoption of network theory is critical to understand contagion and systemic vulnerabilities. While interdependencies among financial markets have been widely examined, only few studies review networks, however, they do not focus on the econometrics aspects. This paper presents a state-of-the-art review on the interface between statistics and econometrics in the inference and application of Bayesian graphical models. We specifically highlight the connections and possible applications of network models in financial econometrics, in the context of systemic risk

Detecting spatial and temporal house price diffusion in the Netherlands

Following the 2007-08 Global Financial Crisis, there have been a growing research interest on the spatial interrelationships between house prices in many countries. This paper examines the spatio-temporal relationship between house prices in the twelve provinces of the Netherlands using a recently proposed econometric modelling technique called Bayesian graphical vector autoregression (BG-VAR). This network approach enables a data driven identification of the most dominant provinces where house price shocks may largely diffuse through the housing market and it is suitable for analysing the complex spatial interactions between house prices. Using temporal house price volatilities for owner-occupied dwellings, the results show evidence of house price diffusion pattern in distinct sub-periods from different provincial housing sub-markets in the Netherlands. We observed particularly prior to the crisis, diffusion of temporal house price volatilities from Noord-Holland

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

Tree Networks to Assess Financial Contagion

We proposes a two-layered tree network model that decomposes financial contagion into a global component, composed of inter-country contagion effects, and a local component, made up of inter-institutional contagion channels. The model is effectively applied to a database containing time series of daily CDS spreads of major European financial institutions (banks and insurance companies), and reveals the importance monitoring both channels to assess financial contagion. The empirical application revealed evidence of a high inter-country and inter-institutional vulnerability at the onset of the global financial crisis in 2008 and during the sovereign crisis in 2011. The result further identifies Belgium and France as central to the inter-country contagion in the Euro area during the financial crisis, while Italy dominated during the sovereign crisis. The French corporates Groupama, Credit Industriel and Caisse d'Epargne were central in the inter-institutional contagion in both crises

Tree Networks to Assess Financial Contagion

We proposes a two-layered tree network model that decomposes financial contagion into a global component, composed of inter-country contagion effects, and a local component, made up of inter-institutional contagion channels. The model is effectively applied to a database containing time series of daily CDS spreads of major European financial institutions (banks and insurance companies), and reveals the importance monitoring both channels to assess financial contagion. The empirical application revealed evidence of a high inter-country and inter-institutional vulnerability at the onset of the global financial crisis in 2008 and during the sovereign crisis in 2011. The result further identifies Belgium and France as central to the inter-country contagion in the Euro area during the financial crisis, while Italy dominated during the sovereign crisis. The French corporates Groupama, Credit Industriel and Caisse d'Epargne were central in the inter-institutional contagion in both crises

Tree Networks to assess Financial Contagion

We propose a two-layered tree network model that decomposes financial contagion into a global component, composed of inter-country contagion effects, and a local component, made up of inter-institutional contagion channels. The model is effectively applied to a database containing time series of daily CDS spreads of major European financial institutions (banks and insurance companies), and reveals the importance of monitoring both channels to assess financial contagion. Our empirical application reveals evidence of a high inter-country and inter-institutional vulnerability at the onset of the global financial crisis in 2008 and during the sovereign crisis in 2011. The results identify France as central to the inter-country contagion in the Euro area during the financial crisis, while Italy dominates during the sovereign crisis. The application of the model to detect contagion between sectors of the European economy reveals similar findings, and identifies the manufacturing sector as the most central, while, at the company level, financial institutions dominate during the 2008 crisis

Sparse BGVAR models for Systemic Risk Analysis

Measuring systemic risk requires the joint analysis of large sets of time series which calls for the use of high-dimensionalmodels. In this context, inference and forecasting may suffer from lack of efficiency. In this paper we provide a solution to these problems based on a Bayesian graphical approach and on recently proposed prior distributions which induces sparsity in the graph structure. The application to the European stock market shows the effectiveness of the proposed methods in extracting the most central sectors during periods of high systemic risk level

Latent Factor Models for Credit Scoring in P2P Systems

Peer-to-Peer (P2P) fintech platforms allow cost reduction and service improvement in credit lending. However, these improvements may come at the price of a worse credit risk measurement, and this can hamper lenders and endanger the stability of a financial system. We approach the problem of credit risk for Peer-to-Peer (P2P) systems by presenting a latent factor-based classification technique to divide the population into major network communities in order to estimate a more efficient logistic model. Given a number of attributes that capture firm performances in a financial system, we adopt a latent position model which allow us to distinguish between communities of connected and not-connected firms based on the spatial position of the latent factors. We show through empirical illustration that incorporating the latent factor-based classification of firms is particularly suitable as it improves the predictive performance of P2P scoring models

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 logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 15000 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