Search results

This paper sheds light on the impact of market risk measures on systemic risk. Market risk, which is captured by the volatility of stock market returns, is also decomposed into systematic and idiosyncratic risks.

The author uses the five-factor asset pricing model and systemic risk methodologies to derive market and systemic risk measures, respectively. Using a sample of 2,667 US banks for over 30 years and employing panel data estimation techniques, the author tests the said relationship.

It is shown that idiosyncratic risk can surge systemic risk, while systematic risk plays a less important role. Results survive a battery of tests, including different systemic risk measures, controlling causality and interacting with bank size, market fear and crisis periods.

These findings call for regulatory intervention, especially for large banks with high idiosyncratic risk.

This is the first paper that provides a more granular picture of the relationship between market and systemic risk from the US banking industry for more than 30 years.

The study measures the total systemic risks and connectedness across commodities, stocks, exchange rates and bond markets in Africa during the Covid-19 pandemic.

The study uses the Diebold-Yilmaz spillover and connectedness measures in a generalized VAR framework. The author calculates the net transmitters or receivers of shocks between two assets and visualizes their strength using a network analysis tool.

The study found low systemic risks across all assets and countries. However, we found higher systemic risks in the forex market than in the stock and bond markets, and in South Africa than in other countries. The dynamic analysis found time-varying connectedness return shocks, which increased during the peak periods of the first and second waves of the pandemic. We found both gold and oil as net receivers of shocks. Overall, over half of all assets were net receivers, and others were net transmitters of return shocks. The network connectedness plot shows high net pairwise connectedness from Morocco to South Africa stock market.

The study has implications for policymakers to develop the capacities of local investors and markets to limit portfolio outflows during a crisis.

Previous studies have analyzed spillovers across asset classes in a single country or a single asset across countries. This paper contributes to the literature on network connectedness across assets and countries.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

The purpose of this paper is to assess the effects of prompt corrective action on bank risk and returns in an empirical framework.

The paper uses a difference-in-difference specification to analyse whether and how PCA affects bank risk and returns. As part of robustness, the analysis also uses a fixed effects specification with Driscoll–Kraay standard errors to account for serial correlation and cross-sectional dependence.

The findings reveal that banks under PCA framework contribute less to systemic risk and exhibit higher market valuation. These findings differ across recapitalised versus non-recapitalised banks and for banks with differing asset quality, capital and profitability. The overall price impact is a decline in lending rates and deposit costs.

To the best of the author’s understanding, this is one of the early studies in the Indian context to carefully examine the linkage between PCA and bank behaviour.

The purpose of this paper is to construct an index for systemic risk in China.

This paper develops a systemic risk index for China (SRIC) using textual information from 26 leading newspapers in China. Our index measures the systematic risk from 21 topics relating to China’s economy and provides narratives of the sources of systemic risk.

SRIC effectively predicts changes in GDP, aggregate financing to the real economy and the purchasing managers’ index. Moreover, SRIC explains several other commonly used macroeconomic indicators. Our risk measure provides a helpful monitoring tool for policymakers to manage systemic risk.

The paper construct an index of systemic risk based on the information extracted from newspaper articles. This approach is new to the literature.

This study investigates how this pandemic impacted the systemic risk in Indonesia’s Islamic commercial banks (ICBs) and conventional commercial banks (CCBs). The authors use quantitative methods, and systemic risk is measured using value at risk (VaR) and Conditional Value at Risk (CoVaR). This study provides empirical evidence regarding the estimation and determination of systemic risk. By using spillover measures, the authors find a significant increase in systemic risk among the sample banks. The novelty in this research is the measurement of the level of banking risk in the dual banking system in Indonesia. This study makes profound contributions to the literature and suggests various policy recommendations, including identifying essential institutions and testing the benefits of policy responses in containing systemic risk. These findings need to be considered by the government and financial authorities in making accurate regulations and policies.

Based on the theory of performance feedback, this study aims to explore the theoretical relationship between performance shortfalls and the financialization of non-financial enterprises. It further analyzes the moderating effect of economic policy uncertainty (EPU) and organizational redundant resources.

Multiple regression analysis is used on 16,555 initial samples of 2,658 Chinese A-share issuing enterprises from 2007 to 2019 to empirically test the relationship between performance shortfalls and the financialization of non-financial enterprises, and an instrumental variables-generalized moments estimation model is also used to verify the robustness of the results.

The results reveal that the greater the performance gap below the aspiration level, the higher the degree of enterprise financialization. Moreover, EPU strengthens the relationship between performance shortfalls and financialization, whereas organizational redundant resources weaken the relationship between performance shortfalls and financialization.

Decision-makers should determine the aspirated performance level of enterprises to make investment decisions that are most conducive to the long-term development of enterprises. Each enterprise should establish scientific management evaluation and supervision systems to avoid financial investment behaviors that place too much emphasis on short-term performance.

This study finds that financialization is one of the reactions when performance of enterprises is lower than the aspiration level, thus expanding the functional dimensions of performance feedback and supplementing the research on the influencing factors of enterprise financialization. The results also reveal information about situational factors, helping identify the boundary conditions through which performance below aspirations affects enterprise financialization.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.