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Might the impact of the financial stress on exchange markets be asymmetric and exposed to regime changes? Departing from the existing literature, highlighting that the domestic and foreign financial stress in terms of money market have substantial effects on exchange market, this paper aims to investigate the impacts of the bond yield spreads of three emerging countries (Mexico, Russia, and South Korea) on their exchange market pressure indices using monthly observations for the period 2010:01–2019:12. Additionally, the paper analyses the impact of bond yield spread of the US on the exchange market pressure indices of the three mentioned emerging countries. The authors hypothesized whether the negative and positive changes in the bond yield spreads have varying effects on exchange market pressure indices.

To address the research question, we measure the bond yield spread of the selected countries by using the interest rate spread between 10-year and 3-month treasury bills. At the same time, the exchange market pressure index is proxied by the index introduced by Desai et al. (2017). We base the empirical analysis on nonlinear vector autoregression (VAR) models and an asymmetric quantile-based approach.

The results of the impulse response functions indicate that increases/decreases in the bond yield spreads of Mexico, Russia and South Korea raise/lower their exchange market pressure, and the effects of shocks in the bond yield spreads of the US also lead to depreciation/appreciation pressures in the local currencies of the emerging countries. The quantile connectedness analysis, which allows for the role of regimes, reveals that the weights of the domestic and foreign bond yield spread in explaining variations of exchange market pressure indices are higher when exchange market pressure indices are not in a normal regime, indicating the role of extreme development conditions in the exchange market. The quantile regression model underlines that an increase in the domestic bond yield spread leads to a rise in its exchange market pressure index during all exchange market pressure periods in Mexico, and the relevant effects are valid during periods of high exchange market pressure in Russia. Our results also show that Russia differs from Mexico and South Korea in terms of the factors influencing the demand for domestic currency, and we have demonstrated the role of domestic macroeconomic and financial conditions in surpassing the effects of US financial stress. More specifically, the impacts of the domestic and foreign financial stress vary across regimes and are asymmetric.

This study enriches the literature on factors affecting the exchange market pressure of emerging countries. The results have significant economic implications for policymakers, indicating that the exchange market pressure index may trigger a financial crisis and economic recession.

This study aims to empirically investigate the impact of political instability on the banking stability of the dual banking system in the Middle East and North African (MENA) countries.

The study measures banking stability with probability of default (PD) and Zscore by employing the generalised method of moment (GMM) between 2007 and 2021 on the dual banking system in the region. The authors further estimate short-long-run situations coupled with a robustness test using a generalised least square (GLS) model.

The authors' findings indicate that institutional factors of political stability, crisis period, high-crisis countries, law and order and macroeconomic indicators influence the two types of banking stability in the region. The authors found the consistency of the factors explaining stability in the region in both short-and long-run situations. Consequently, the study also reveals the adverse effects of crisis periods and high-crisis countries on banking stability.

The results of this study explicitly identify the critical need for sustaining political stability and abiding by laws and order to achieve dual banking stability in the region. Therefore, policymakers may consider allowing the region's banks to operate beyond retail banking since diversification enhances banking stability.

The authors' study balances by employing dual stability measurement in predicting the impact of political instability, law and order and other indicators on the MENA region's two banking models. This study uncovers the effect of the global crisis period on banking stability and high-crisis countries in the region and verifies the models' robustness.

Nowadays, the competition is not only emerging from within the banking sector, but nonbanking companies like nonbanking financial companies (NBFCs) and FinTech are also growing in size and numbers, offering innovative financial products and services, giving a stiff competition to Indian banks. Thus, this study aims to investigate whether competition from within and outside the banking sector enhances or reduces the financial stability of the banking industry.

The study uses Herfindahl–Hirschman index to measure market share and Z score to measure financial stability. The study further examines the role of NBFCs and FinTech companies in impacting the financial stability by introducing variables like innovation, cybercrimes, systemically important institutions, etc. Thereafter, panel regression has been applied.

Empirical results show a positive relation of market share with financial stability, implying that increased competition in the Indian banking industry erodes the market power, adversely affecting the profit margins which encourages banks to take more risk and which may impact financial stability. The study shows a positive impact of innovation on financial stability which implies that the competition is acting as an enabler for banks. The authors find a negative relation of systemic important NBFCs with financial stability. The authors observe a negative association of cybercrimes with financial stability, reflecting that competition emerging from FinTech sector has exposed banks to new risks.

The policymakers should make sure that the competition of banks with other financial institutions, such as FinTech sector, remains healthy; otherwise, it can jeopardize the entire financial system. It is for the policymakers to define a boundary for FinTech sector, as the development of this sector has exposed the banking industry to new kinds of risks potential to create financial instability. The banks should do a comprehensive check on the company to which it is granting loans, and the government should amend laws. Though big banks have huge potential, consolidations can pose challenges at a macroeconomic level.

FinTech firms are a new entrant in the financial world which are providing immense competition to the banking sector, and thus radically changing the entire financial system. Therefore, it is extremely vital to study and explore the role of NBFCs and the FinTech industry as the main variable to analyze bank competition, which to the best of the authors’ knowledge is completely missing in the previous studies.

Supply chain finance (SCF) has become a vital ingredient that fosters growth and provides flexibility to the global supply chain. Thus, it becomes essential to understand the factors that contribute to the success of the supply chain finance ecosystem (SCFE). This study aims to identify the critical success factors (CSFs) for the development of an efficient and effective SCFE. Based on their characteristics, the study intends to classify the factors into constructs and further establish a hierarchical relationship among the CSFs.

The study is based on empirical data collected from 221 respondents based on administered questionnaires. Exploratory factor analysis (EFA) is carried out on 16 selected factors (out of 21 proposed factors) based on the feedback of the experts and the factors were classified into four constructs. The total interpretive structural modeling (TISM) model was developed by identifying and finalizing CSFs of the SCFE. The model developed a hierarchical relationship between the various factors.

The study identified significant CSFs for the efficient and effective SCF ecosystem. Four constructs were developed by analyzing CSFs using the EFA. The finalized 16 CSFs modeled through the TISM and further hierarchical relationship established between the CSFs concludes that governmental policies and sectoral growth are the strongest driving forces and financial attractiveness is the weakest driving force. Based on the CSFs and the constructs identified, it was found that for the success of the SCF ecosystem, the existence of an economic ecosystem provides a facilitating framework for the overall development of the SCFE. Also, the trustworthiness among the partners fosters better relationships and results in financial feasibility and offers business opportunities for all the stakeholders.

This study will help the SCF partners across the globe understand the CSFs that ensure development of mutually beneficial SCF ecosystems and provide flexibility to the supply chain partners. The CSFs would provide insights to the policymakers and the financial intermediaries for providing a conducive environment for the development of a better SCF ecosystem. Also, the buyers and sellers would understand the CSFs that would develop better relationships among them and ultimately help in development of business across the globe.

The study identifies the CSFs for the SCF ecosystem. The study ascertains the significant factors and classifies them into clusters using EFA. Unlike the literature available, the paper develops the hierarchical relationship between the CSFs and develops a model for an efficient and effective SCF ecosystem.

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

The paper empirically investigates the link between banking market structure and funding liquidity risk.

With a panel of Vietnamese commercial banks from 2007 to 2021, the system generalized method of moments (GMM) estimator is applied as the primary regression method, while the random-effect model and the corrected least square dummy variable (LSDVC) technique are also considered in robustness checks.

Competition may increase banks' funding liquidity risk. This finding holds for competition measures derived from the Boone index and concentration ratios but not in the case of the Lerner index as a proxy for market power. Further results indicate that the funding liquidity risk of banks that are larger and have better performance (less credit risk and higher return) tends to be less affected by competition. Besides, the overall impact of bank competition on funding liquidity risk is amplified by the financial crisis and the COVID-19 pandemic.

The study extends the empirical literature by exploring the relationship between bank competition and funding liquidity risk. Additionally, the paper also studies how the impact of bank competition on funding liquidity risk depends on the characteristics of the banking sector and the macroeconomic conditions of the economy, including the moderating effect of the COVID-19 pandemic.

This paper makes a novel attempt to estimate the potential impact of credit risk on foreign direct investment (FDI hereafter), thereby focusing on a completely unexplored area in the existing empirical literature.

To provide a comprehensive understanding of the relationship between credit risk and FDI inflows, the study incorporates all the eight-member economies of the South Asian Association of Regional Cooperation (SAARC hereafter) and analyzes a panel data set, over the period 2011 to 2019, extracted from the World Development Indicators, using the suitable econometric techniques for the efficient estimations of the specified models.

The results indicate a negative and statistically significant relationship between the credit risk of the banking sectors and FDI inflows. Similarly, market size and inflation rate appear to be the two other main factors behind the increasing FDI inflows in the SAARC member economies. Interestingly, the size of the market became irrelevant in attracting FDI inflows when the Indian economy is excluded from the sample due to its higher economic weight. On the other hand, FDI inflows are not dependent on the level of trade openness, with most of the specifications showing either an insignificant or negative coefficient of the variable.

The obtained results are unique and robust to alternative methodologies, and hence, the SAARC economies could consider them as the critical inputs in formulating the appropriate policies on FDI inflows.

The findings are unique and original. The authors have established a relationship between credit risk and FDI for the first time in the SAARC context.

The aim of this study is to analyze the influence mechanism of real estate enterprises' status on debt default risk and explore the heterogeneity effect of the characteristics of enterprises.

Against the background of the “three red lines” regulation of the financing of real estate enterprises and the COVID-19 pandemic, the authors select 123 real estate enterprises listed on China's Shanghai and Shenzhen A-shares markets from the first quarter of 2021 to the second quarter of 2022 as a research sample. The social network analysis method and Z-score financial risk early warning model are used to measure real estate enterprises' status and debt default risk. The authors construct a panel regression model to analyze how the status of real estate enterprises influences their debt default risk.

The results show that the status of real estate enterprises negatively and significantly affects their debt default risk. Economic policy uncertainty and financing constraints play negative moderating and mediating roles, respectively. Further research has found that the effect of real estate enterprises' status on debt default risk is characterized by heterogeneity in equity characteristics, i.e. it is significant in the sample of nonstate-owned enterprises but not in the sample of state-owned enterprises.

It is helpful for real estate enterprises to attach importance to the value of social networks, and the authors provide policy suggestions for real estate enterprises to constantly improve their risk management systems.

Using economic policy uncertainty as the moderating variable and financing constraints as the mediating variable, the authors analyze how the status of real estate enterprises influences debt default risk, which contributes to a better understanding of the formation of the debt default risk of real estate enterprises.

In this paper, the authors examine the short-term and long-term impact of general economic policy uncertainty (EPU) and crypto-specific policy uncertainty on Bitcoin’s (BTC) exchange inflows – a form of crypto investor behaviors that the authors expect to drive the cryptocurrency volatility.

The authors use an autoregressive distributed lag (ARDL), coupled with the bounds testing approach by Pesaran et al. (2001), to analyze a weekly dataset of BTC’s exchange inflows and relevant policy uncertainty indices.

The authors observe both short-term and long-term impacts of the crypto-specific policy uncertainty on BTC’s exchange inflows, whereas the general EPU only explains these inflows in a short-term manner. In addition, the authors find exchange inflows of BTC “Granger” cause its price volatility. Furthermore, the authors document a significant and relatively persistent response of BTC volatility to shocks to its exchange inflows.

This study’s findings offer significant contributions to research in policy uncertainty and investor behaviors.