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A banking system is essential for financial stability, especially economic growth and development. The authors investigate the dynamic linkage of key banking system stability measures, namely, liquidity, capital, profitability, and credit risk. To this end, the authors employ Panel Vector Autoregressive (VAR) to a panel data set of country-level banking system indicators from seven developing countries; from March 2010 to December 2020 (308 country quarter observations). A nation is selected on the basis of similar characteristics large and bank-based economy with the considerably same stage of economic development. The authors find a remarkable resilient feature of the banking system in which both liquidity risk and credit risk appears significant only in the short run (within three quarters). Shocks from both risk sources dissipate quickly, suggesting an internal mechanism is at work. This study provides evidence of how a good performance of financial safety net should be.

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.

Although several microeconomic and macroeconomic factors driving banks' credit quality have been well-studied in the literature, one aspect which appears to have received limited attention is bankruptcy reforms. To address this issue, the author exploits data on Middle East and North Africa (MENA) country banks during the period 2010–2020 and examines the impact of bankruptcy laws on their credit quality.

In view of the staggered nature of the implementation of legal reforms across countries, the author utilize a difference-in-differences specification to tease out the causal impact.

The findings reveal that bankruptcy reforms lead to a significant improvement in banks' credit quality. The impact is manifest mainly for conventional banks and driven by an increase in recovery intensity. The author also presents evidence which shows that such reforms exert positive real effects, although this impact differs across country characteristics.

The study is among the early ones for the MENA region to assess the interlinkage between bankruptcy reforms and banks' credit quality.

This paper aims to assess the impact of digital financial innovation on financial system development in Common Market for eastern and Southern Africa (COMESA). This paper evaluates the dynamic relationship between digital financial innovation measures and financial system development using time series data from COMESA countries for the period 1997–2019.

A dynamic autoregressive distributed lag model (ARDL) was adopted and the mean group (MG), pooled mean group (PMG) and dynamic fixed effect (DFE) of the model were estimated to evaluate the short- and long-run impact. In addition, the dynamic generalized method of moments (DGMM) was adopted for a robustness check. The Hausman test results show PMG to be the most consistent and efficient estimator, while the coefficient of lagged dependent variable of different GMM is less than the fixed effect coefficient, and, as such, suggests system GMM is the most suitable estimator. Data for the study were sourced from World Bank Development Indicator (WDI, 2020), World Governance Indicator (WGI, 2020) and World Bank Global Financial Development Database (GFD, 2020).

The result shows that digital financial innovation significantly impacts financial system development in the long run. As such, the evidence revealed that automated teller machines (ATMs), point of sale (POS), mobile payments (MP) and mobile banking are significant and contribute positively to financial system development in the long run, while mobile money (MM) and Internet banking (INB) are insignificant but exhibit positive and inverse relationship with financial development respectively. Further investigation revealed that institutional quality and a stable macroeconomic environment including their interactive term are significantly imperative in predicting financial system development in the COMESA region.

Researchers recommend a cohesive and conscious policy that would checkmate the divergence in the short run and suggest a common regional innovative financial strategy that could be pursued to incentivize technology transfer needed to promote financial system development in the long run. More so, plausible product and process innovations may be adapted to complement innovative institutions in the different components of the COMESA financial system.

Digital financial innovation services if well managed increase the inherent benefits in financial system development.

To the best of the authors’ knowledge, this paper presents new background information on digital financial innovation that may stimulate the development of the financial system, particularly in the COMESA region. It also exposes the relevance of digital financial innovation, institutional quality and stable macroeconomic environment as well as their interactive effect on COMESA financial system development.

The purpose of this paper is to investigate the impact of changes in credit stock on real and financial indicators in Turkey with a focus on conditional and time-varying dynamics.

In addition to lag-augmented vector autoregression (LA-VAR) based time-varying Granger causality tests, threshold models and a research setting that identifies high/low states of credit growth based on 24-month moving averages are used to explore regime-dependent behavior. For investigating the asymmetric dynamics, the authors use a methodology that identifies good/bad news in credit growth based on 24-month moving averages and standard deviations.

Results strongly suggest that the impact of changes in credit stock induces conditional responses. Moreover, we find evidence for asymmetric responses. In the case of Turkey, efforts to spur growth through credit produce a strong negative byproduct, a depreciation in the exchange rate. The authors also find that changes in credit stock have become more relevant for uncertainties in inflation and exchange rate expectations, particularly in the era after mid-2018 in which credit growth volatility has increased noticeably.

This study provides a comprehensive analysis of time-varying and conditional responses to a change in credit stock in a major emerging economy. Using a moving threshold based only on the available information in the analysis of state-dependency represents a new approach.

The idea of this study is to provide a solid Financial Condition Index (FCI) that allows the monetary transmission policy to be monitored in a country which in recent decades has suffered from major financial and monetary crises.

The authors construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime. Using monthly data from 1995 to 2017, the authors estimate FCIs with two different methodologies and build the index by taking into account the mechanism of transmission of monetary policy and incorporating the most relevant financial variables.

This study’s results show that, likewise for developing countries as Mexico, an FCI could be a useful tool for managing monetary policy in reducing macroeconomic fluctuations.

Apart from building a predictor of possible financial stress, the authors construct an FCI for a central bank that pursues inflation targeting and to analyse the role of financial asset prices in formulating monetary policy.

1. We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

2. The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

3. FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

4. FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.

We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.

This paper aims to determine causal relationships between the level of productive credit, real deposits and money demand – all of them in real terms – and Gross National Product between 2006 and 2020.

The vector autoregressive technique (VAR) was used, where data from real macroeconomic aggregates published by the Central Bank of Ecuador (BCE) are correlated, such as productive credit, gross domestic product (GDP) per capita, deposits and money demand.

The results indicate that there is no causal relationship, in the Granger sense, between GDP and financial activity, but there is between the growth rate of real money demand per capita and the growth rate of total real deposits per capita.

The study shows that bank credit mainly finances the operations of current assets and/or liabilities. In addition, economic agents use the banking system mainly to carry out transactional and precautionary activities.

In this paper, the authors investigate whether coronavirus disease 2019 (COVID-19) impacts household finances, like household debt repayments in the UK.

This paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. The authors use data information related to COVID-19, financial markets and household finances.

The authors' results show that household debt repayments' response to the first principal component of COVID-19 shocks is negative, albeit of low magnitude. However, when the authors employ specific COVID-19-related data like vaccines and tests the responses are positive, insinuating the underlying dynamic complexities. Overall, confirmed deaths and hospitalisations negatively affect household debt repayments. The authors also report low persistence in household debt repayments. Generalised impulse response functions (IRFs) confirm the main results. As draconian measures, the lockdowns are eased and the COVID-19 shocks are diminishing, and household financial data converge to the levels prior to the pandemic albeit with some lags.

To the best of the authors' knowledge, this is the first study that examines the impact of the pandemic on household debt repayments. The authors' findings show that policy response in the future should prioritise innovation of new vaccines and testing.

In this study, the authors propose a VaR method for evaluating the market risk of investing in the stock portfolio of Pension Institutions. The data used for this research is hypothetical data, including the exposure or the amount of value invested by Pension Institutions in their stock portfolio. With the VaR – Monte Carlo simulation, the authors know the loss level will occur when the Indonesian economy or market conditions deteriorate. The lost value amount is determined in the Rupiah value, according to the confidence level or the desired percentile level. The results revealed that at the 5% (99.95%) percentile level of confidence, a pension fund with an investment value of IDR 4,070,000,000 would suffer a loss of IDR 1,110,000,000. While at the 1% (99.995%), the loss rate will be of IDR 1,480,000,000. The conclusion is that the results of this study are useful for Pension Institutions in managing their asset portfolios with the VaR model.

The authors construct an index of economic policy uncertainty (EPU) for Greece using textual analysis and analyze its role in the 10-year Greek economic crisis.

To identify the causal relationship between various measures of economic activity and EPU in Greece, the authors use a sophisticated “shock-based” structural vector autoregressive identification scheme. Additionally, the authors use two additional models to ensure the robustness of the results.

EPU is negatively associated with domestic economic activity and economic sentiment, and positively with bond credit spreads. EPU is also estimated to have prolonged the crisis even in periods when macroeconomic imbalances were cured. The results are robust across various model specifications and different proxies of economic activity.

Brunnermeier (2017) observed that uncertainty may be central to understanding the evolution of the Greek crisis. Yet little attention has been paid to policy uncertainty in the existing long and growing literature on the Greek crisis. The authors attempt to fill this gap.