Multilayer financial networks and systemic importance: Evidence from China

In this paper, we develop a multilayer network structure and reveal the relationship between network structure and systemic risk. Unlike many previous studies, our model considers both liability and cross-holding of shares between financial institutions simultaneously. We propose a new systemic risk measurement by exploring the dynamic mechanism of financial contagion in the multilayer network. We display the network structure of Chinese financial institutions, including connectivity and diversity, and identify the systemic importance of them. We demonstrate that the multilayer network plays a non-linear role in financial risk spreading. Using the panel regression model and several experiment evidences, we show that the systemic risk can be explained more effectively by the linkage diversity more than the connectivity at both the institutional level and the system level. Our results highlight the importance of considering contagion mechanisms that go beyond a simple single-layer network structure.     

A Bayesian approach for more reliable tail risk forecasts

This paper demonstrates that existing quantile regression models used for jointly forecasting Value-at-Risk (VaR) and expected shortfall (ES) are sensitive to initial conditions. Given the importance of these measures in financial systems, this sensitivity is a critical issue. A new Bayesian quantile regression approach is proposed for estimating joint VaR and ES models. By treating the initial values as unknown parameters, sensitivity issues can be dealt with. Furthermore, new additive-type models are developed for the ES component that are more robust to initial conditions. A novel approach using the open-faced sandwich (OFS) method is proposed which improves uncertainty quantification in risk forecasts. Simulation and empirical results highlight the improvements in risk forecasts ensuing from the proposed methods.     

Social networks in the global banking sector

We show that banks with shared social connections partner more often in the global syndicated loan market and that central banks in the network play dominant roles in various interbank transactions, indicating that social connections facilitate business connections. However, more centralized banks in the network also contribute significantly to the global systemic risk. Moreover, we find the soft information generated by social networks is particularly valuable when potential partners operate under different accounting and regulatory standards. Finally, we show that the recent banking crisis significantly limited the positive soft information effects of social networks in the global banking system.     

Systemic event prediction by an aggregate early warning system: An application to the Czech Republic

This work develops an early warning framework for assessing systemic risks and predicting systemic events over a short horizon of six quarters and a long horizon of 12 quarters on a panel of 14 countries, both advanced and developing. First, we build a financial stress index to identify the starting dates of systemic financial crises for each country in the panel. Second, early warning indicators for the assessment and prediction of systemic risk are selected in a two-step approach; we find relevant prediction horizons for each indicator by a univariate logit model followed by the application of Bayesian model averaging to identify the most useful indicators. Finally, we observe the performance of the constructed EWS over both horizons on the Czech data and find that the model over the long horizon outperforms the EWS over the short horizon. For both horizons, out-of-sample probability estimates do not deviate substantially from their in-sample estimates, indicating a good out-of-sample performance for the Czech Republic.     

EXTREME VALUE THEORY IN FINANCE: A SURVEY

Extreme value theory is concerned with the study of the asymptotic distribution of extreme events, that is to say events which are rare in frequency and huge in magnitude with respect to the majority of observations. Statistical methods derived from it have been employed increasingly in finance, especially for risk measurement. This paper surveys some of those main applications, namely for testing different distributional assumptions for the data, for Value‐at‐Risk and Expected Shortfall calculations, for asset allocation under safety‐first type constraints, and for the study of contagion and dependence across markets under conditions of stress.     

Measuring systemic risk-adjusted liquidity (SRL)—A model approach

Little progress has been made so far in addressing—in a comprehensive way—the negative externalities caused by excessive maturity transformation and the implications for effective liquidity regulation of banks. The SRL model combines option pricing theory with market information and balance sheet data to generate probabilistic measure of systemic liquidity risk. It enhances price-based liquidity regulation by linking a bank’s maturity mismatch impacting the stability of its funding with those characteristics of other banks, subject to individual changes in risk profiles and common changes in market conditions impacting funding and market liquidity risk. This approach can then be used (i) to quantify an individual institution’s time-varying contribution to expected losses from system-wide liquidity shortfalls and (ii) to price insurance premia that provide incentives for banks to internalize the social cost of their individual funding decisions.     

Systemic risk and dynamics of contagion: a duplex inter-bank network

This paper constructs a duplex banking network formed by credit relationships and information interaction via the banks’ balance sheet to model the structure of systemic risk and investigate the dynamic mechanism of contagion in terms of default and liquidity infection along with the factors that affect the extent of the contagion. We systematically explain the role that duplex banking networks play in different aspects of risk contagion. Through theoretical analysis and simulations, we conclude that asymmetric information interaction would increase the inflexibility of the system, which leads to liquidity shortage and possibly the collapse of the whole market. The weakness of systemic risk in the interior of the complex banking system can be characterized by the partial discount factor using illiquid assets in the information network. By improving the connectedness of the information network of the duplex networks, the spread of contagion can be partially slowed.     

La gestión mecanicista de las Instituciones de Educación Superior: un estudio desde la modelación sistémica

The bureaucratic tendencies to organize work in Higher Education Institutions (HEIs) of which facilitates personnel control and the mechanical ways to measure educational content are the ones that shatter education in pieces, under the hierarchy criterion, also shattering the professor's labor. This organizational method results in an incompetent education, leaving very negative consequences for teaching development. We address this problem by using systemic modeling and Structural Equations Systems (SEM) to confirm hypotheses from scientific observation. It was proven that there was a negative impact of mechanistic management which affected teacher's development. This was proven in context in two HEIs located in Villahermosa (Tabasco, Mexico).     

Real estate prices and systemic banking crises

The collapse of real estate prices has historically jeopardized banking stability and triggered systemic banking crises. This paper studies risk contagion in a banking system in real estate price shock by adopting complex network theory. Modelling the real estate-related asset as a common exposure of banks to the real estate market, we propose a model that incorporates two main risk contagion channels, i.e., the financial network and asset fire sales, and reveal how the real estate price shock is transmitted and propagated across banks. We demonstrate that banking stability is highly sensitive to the real estate price shock. Moreover, due to the particularly low liquidity of the real estate market, the asset fire-sales of real estate assets overwhelms the financial network, playing the dominant role in risk contagion. Our model can be adopted by regulators to conduct stress testing and to forge effective risk management strategies.