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The study evaluates the role of institutional framework and macroeconomic instability on financial market development among emerging economies.

The study uses panel data compiled from 32 countries from the sub-region of Sub-Sahara Africa (SSA), covering the period starting from 1996 to 2019. Empirical analyses were carried out using the two-step system generalized method of moments (TS-GMM) statistical framework.

Reviewed results suggest that institutional quality, effective governance and corruption control have a significant positive impact on financial market development among economies in the sub-region. Further empirical estimates show that macroeconomic risk and macroeconomic uncertainty have significant adverse effects on financial market development. Additionally, reported empirical estimates suggest that an improved institutional framework has the potential to lessen the adverse effect of macroeconomic instability on financial market development among economies in the sub-region.

The uniqueness of this empirical inquiry compared to related studies in the present literature stems from the fact that studies employing similar empirical approaches on the subject matter for economies in the sub-region are rare. Additionally, the analysis pursued in this study employs critical variables whose impact on financial market performance in the sub-region has not been examined per our review. These variables include indexes such as macroeconomic risk and institutional quality, which are unique to this study based on their construction; these indexes are generated using a principal component analysis procedure with different underlying variables compared to what may be found in the literature.

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.

This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index.

This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited.

The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers.

The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations.

The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.

This study investigates the impact of US dollar volatility and oil rents on the performance of small and medium-sized enterprises (SMEs) in the Gulf Cooperation Council (GCC) region, with an emphasis on understanding how these factors influence SME financing constraints in economies with fixed currency regimes.

Employing a random effects panel regression analysis, this research considers US dollar volatility and oil rents as independent variables, with SME performance, measured through the financing gap, as the dependent variable. Controls such as trade balance, inflation deltas and gross domestic product (GDP) growth are included to isolate their effects on SME financing constraints.

The study reveals a significant positive relationship between dollar volatility and the financing gap, suggesting that increased volatility can exacerbate SME financing constraints. Conversely, oil rents did not show a significant direct influence on SME performance. The trade balance and inflation deltas were found to have significant effects, highlighting the multifaceted nature of economic variables affecting SMEs.

The study acknowledges potential biases due to omitted variables and the limitations inherent in the use of secondary data.

Findings offer pertinent guidance for SMEs and policymakers in the GCC region seeking to develop strategies that mitigate the impact of currency volatility and support SME financing.

The research provides new insights into the dynamics of SME performance within fixed currency regimes, which significantly contributes to the limited literature in this area. The paper further underscores the complex connections between global economic factors and SME financial health.

The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.

We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.

Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.

Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.

The purpose of this study is to examine the effect of Islamic banks (IBs) and macroeconomic variables on economic growth in Saudi Arabia, the United Arab Emirates, Kuwait, Malaysia, Qatar, Bahrain and Bangladesh.

Based on these criteria, 672 observations from 24 IBs in Saudi Arabia, the United Arab Emirates, Kuwait, Malaysia, Qatar, Bahrain and Bangladesh were chosen for further investigation. Time series analysis is a well-known method for determining if model variables are stationary and how long-term relationships function through cointegration analysis. This study uses impulse response function (IRF) and variance decomposition (VD) methodologies to demonstrate how each macroeconomic variable shock influences the short-term dynamic path of all system variables.

Islamic banking promotes economic growth, especially in Saudi Arabia, the UAE, Kuwait, Malaysia, Qatar, Bahrain and Bangladesh. The findings of the Islamic banking VDC test have a direct and long-term effect on economic growth.

The literature on this topic can be improved in a number of ways, including by adopting a more robust method to analyze over a longer time frame. By researching specific financing in various areas of the economy, one can gain a deeper understanding of Islamic financing. This will enable the identification of sectors that contribute to economic expansion. Future research should examine combining nations with pure Islam and dual-banking systems to acquire sufficient data.

This paper has practice and research implications. It recommends adopting the nation’s successful experiment with the Islamic banking system as a model for attaining economic growth through Islamic financing. To replicate this successful experiment, government-based decision-makers and monetary policy experts must collaborate to make Islamic money flows simple and rapid through financial channels that enhance economic growth.

The study of the contribution of Islamic banking to economic growth in developing nations, particularly those with the highest total assets (TAs) and total deposits (TDs) in the world, remains of modest value. To the best of the authors’ knowledge, this is the first study to empirically assess the impact of IBs in developing nations, particularly those with the highest TAs and TDs in the world, on economic growth as measured by gross domestic product (GDP).

Our purpose is the examination of the effects of fiscal policy on private lending for the Eurozone countries. The emphasis is on the identification of the time path of government spending and bank lending.

Fiscal policy is a main factor of macroeconomic stability for the euro area economy. This paper, investigates the impact of government spending on bank lending. For this reason, we present a dynamic theoretical model with a perfectly competitive banking sector, estimated using panel cointegration for the Eurozone countries from 2000Q1 to 2022Q2.

Our findings highlight that, in the long run, consistent management of government spending can have a beneficial multiplicative impact on bank lending for housing and business reasons. This finding is stronger in magnitude for business versus housing lending. The high level of homogeneity of our results across Eurozone countries has positive implications for a common fiscal policy in the future. Finally, authorities should know that policy adjustments are quicker in housing lending when compared to business lending.

In this paper, we contribute to the existing literature, concentrating on the investigation of any existence of long-run and short-run relationships between government spending and bank lending. Additionally, our analysis allows one to investigate the contribution of each Eurozone member state in the short-run and long-run model’s dynamics, providing significant outcomes for the implementation of economic policy and the need for fiscal discipline in the Eurozone.

This study evaluated the effect of macroeconomic factors on residential real estate (RE) investment returns in the cities of Abuja and Lagos, Nigeria, with a view to guiding RE investors and researchers.

A survey research design was employed using a questionnaire to collect RE transaction data from 2008 to 2022 from estate surveying and valuation firms in the study areas. Rental and capital value data collected were used to construct rental and capital value indices and total returns on investment. The macroeconomic data used were retrieved from the archives of the Central Bank of Nigeria (CBN). Granger causality (GC) and multiple regression models were adopted to evaluate the effect of selected macroeconomic variables on residential RE investment returns in the study areas.

The study found a progressive upward movement in rental and capital values of residential RE investment in the study areas within the study period. Total and risk-adjusted returns on investment were equally positive within the study period. Only the inflation rate, unemployment rate and real gross domestic product (GDP) per capita were found to be the major determinants of residential RE investment returns in the study areas within the study period.

The secrecy associated with property transaction information/data by RE practitioners in the study areas posed a challenge. Property transaction data were not adequately kept in a way for easier access and retrieval in many of the estate firms and agent offices. Consequently, there was a lack of data that spanned the study period in some of the sampled estate firms or agent offices. This data collection challenge was, however, overcome by the excess time spent retrieving the required data for this study to ensure that the findings appropriately answer the research questions.

Inflation and GDP per capita have been found to be significant factors that influence residential RE investment performance in the study areas. Therefore, investors should pay attention to these identified macroeconomic factors for residential RE investment in the study areas whilst making investment decisions in order to mitigate a possible loss of income or return. The government should formulate and implement economic policies that would address the current high unemployment and inflation rates in Nigeria at large.

This study has extended and further enriched the existing body of knowledge in the field of RE investment analysis in Nigeria. To the best of the authors' knowledge, this study is the first to adopt the Cornish Fisher value-at-risk and modified Sharpe ratio models to analyse risk and risk-adjusted returns on residential RE investment, respectively, in Nigeria. It has therefore redirected the focus of RE researchers and practitioners to a more objective approach to RE investment performance analysis in Nigeria.

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

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.