Systemic risk and the macroeconomy

Herculano, Miguel (2020) Systemic risk and the macroeconomy. PhD thesis, University of Glasgow.

Abstract

My subject in this thesis is Systemic Risk and the Macroeconomy. The primary focus is on the macroeconomic dimension of Systemic Risk which is the common thread of all four independent essays which compose this dissertation.

The first essay studies the macroeconomic relevance of credit supply. How has the transmission of credit supply shocks changed in the past forty years? To study this question, I estimate a time varying structural vector autoregression which allows parameters governing the relationship between credit supply and the macroeconomy to move smoothly over time. The link between credit supply, inflation and leverage is theoretically unclear and ultimately an empirical question. The analysis delivers new results on such relation which may help guide the nascent theoretical literature on the subject. The key empirical findings are: i) Credit supply shocks have had a more muted effect on real activity and inflation in recent decades; ii) Whereas their importance as determinants of leverage in the economy has increased, despite remarkable oscillations.

The second paper questions the reliability of the many systemic risk indicators proposed in the literature in signalling downturns of real activity. The link between systemic risk and output growth is hard to study for two reasons. First, the relationship is believed to be nonlinear. Second, systemic risk is unobservable and the myriad of measures proposed in the literature impose the additional challenge of having to deal with model uncertainty. This paper examines the relationship between the quantiles of output growth and systemic risk through the lens of a Bayesian quantile regression. We study, in particular, the relevance of 33 systemic risk indicators to explain lower quantiles of output growth that measure growth fragility. Model uncertainty is tackled by using sparse-modelling techniques that perform both model selection and shrinkage. Only a few systemic risk indicators studied have relevant predictive content for output growth, and instability of predictive relations based on such indicators is the norm.

The third chapter aims to advance the measurement of financial conditions. This paper proposes a mixed-frequency factor-augmented vector autoregressive model with time-varying coefficients and stochastic volatility to construct a financial conditions index (FCI). This framework is extended to allow for different unbalanced panel techniques based on probabilistic principal components. In an incomplete data setting, with up to 62\% of data missing, the approach yields a less noisy FCI that tracks the movement of the underlying financial variables more accurately, and delivers better macroeconomic forecasts, on average. A macroeconomic forecasting exercise with the newly constructed FCI may help reconcile opposing views in the literature with regards to the predictive power of financial indicators.

The fourth chapter proposes a novel approach to understanding contagion of financial distress in the banking system, which takes into account the spatial nature of the phenomenon. We use a Bayesian spatial autoregressive model that treats the likelihood of default of each bank as endogenous, and dependent on the network formed by all the other banks. Identification is achieved by controlling for bank fundamentals, latent macrofinancial and bank specific shocks that have similar consequences to contagion and act as confounding factors. We find that peer effects account on average for approximately 50 per cent of total distress. Through the lens of a simulations exercise, we study the importance of the structure of financial networks for financial stability shedding light on the empirical adherence of important theoretical prepositions that remain untested.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: H Social Sciences > HB Economic Theory
• Colleges/Schools: College of Social Sciences > Adam Smith Business School > Economics
• Supervisor's Name: Tsoukalas, Professor John
• Date of Award: September 2020
• Embargo Date: 3 September 2023
• Depositing User: Dr. Miguel Herculano
• Unique ID: glathesis:2020-81628
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 20 Oct 2020 14:04
• Last Modified: 20 Oct 2020 14:04
• URI: https://theses.gla.ac.uk/id/eprint/81628