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The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

This paper aims to empirically investigate the sources of real exchange rate fluctuations in Egypt using structural vector autoregression (SVAR). The data covers the period between 1980 and 2016, where exchange regime has been changed more than once.

This paper investigates the source of real exchange rate fluctuations for the period between 1980 and 2016 using the SVAR method. The SVAR method will incorporate real gross domestic product (GDP), real effective exchange rate (REER) and price level in a multidimensional equations system. However, impulse response function (IRF) and error variance decompositions (EVDC) will be generated by the system to have a behavioral insight of real exchange rate in response to economic shocks.

The IRF and EVDC results indicate a significant impact of demand shocks over the real exchange rate relative to supply shocks and monetary shocks in the period between 1980 and 2016. On the other hand, monetary shocks will have a negligible effect on the real exchange rate in the short run and converging to its previous level in the covering period of the study.

In the best of the authors' knowledge, the topic of the source of the real exchange rate fluctuations in Egypt has not been discussed in a wide range due to the lack of time series data. However, this study provides constructed data for REER for Egypt with the published method in the International Monetary Fund (IMF). Furthermore, the study involves theoretical and econometric modeling to ensure the reliability of the economic results.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The article aims to establish whether the degree of aversion to inflation and the responsiveness to deviations from potential output have changed over time.

This paper assesses time variation in monetary policy rules by applying a time-varying parameter generalised methods of moments (TVP-GMM) framework.

Using monthly data until December 2022 for five inflation targeting countries (the UK, Canada, Australia, New Zealand, Sweden) and five countries with alternative monetary regimes (the US, Japan, Denmark, the Euro Area, Switzerland), we find that monetary policy has become more averse to inflation and more responsive to the output gap in both sets of countries over time. In particular, there has been a clear shift in inflation targeting countries towards a more hawkish stance on inflation since the adoption of this regime and a greater response to both inflation and the output gap in most countries after the global financial crisis, which indicates a stronger reliance on monetary rules to stabilise the economy in recent years. It also appears that inflation targeting countries pay greater attention to the exchange rate pass-through channel when setting interest rates. Finally, monetary surprises do not seem to be an important determinant of the evolution over time of the Taylor rule parameters, which suggests a high degree of monetary policy transparency in the countries under examination.

It provides new evidence on changes over time in monetary policy rules.

The coronavirus disease 2019 (COVID-19) pandemic has become a global humanitarian challenge. This scourge has impacted people from all walks of life as well as every economic sector and activity, from travel to automotives, hotels to banking, and supply chain to retail. The pandemic has affected not only physical and mental health but also financial health. Studies have examined the pandemic's economic impact, but very few have examined its impact on personal finances. Efforts to contain the pandemic's spread, such as lockdowns, have resulted in suspended business operations throughout the world that have intensified joblessness. To prepare and protect people from such unforeseen situations, financial education and planning are necessary. We attempt to expand the evidence on this issue by applying a structural equation modelling approach to identify the mediating role of financial literacy programs in preparing and protecting household wealth against sudden worldwide setbacks. The research design is descriptive and exploratory using snowball sampling technique. The data was collected through an internet survey. In total, 400 survey responses were obtained. After testing the measurement model for key validity dimensions, the hypothesised causal relationships are examined in several path models. The results indicated that coronavirus awareness exerts a direct or indirect influence on the financial health of individuals through financial literacy. We conclude that financial literacy has a full mediating effect on the personal finance of individuals during the COVID-19 pandemic. The findings not only contributed to the need and understanding of financial literacy but also have managerial implications. Financial literacy programs provide investment advice and suggestions which are actionable and also work to help individuals to come out stronger in terms of knowledge and skill set when the COVID-19 crisis passes.

This study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.

Using principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.

A significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.

The study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.

Enhanced risk governance could reduce RAs, influencing banking policy.

The study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.

This study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.

As a typical nature-based solution to climate change, forestry carbon sinks are vital to achieving carbon neutrality in China. However, regulations in China are insufficient to promote the development of carbon offset projects in forestry. This study aims to identify the regulatory obstacles impeding the development of forestry offsets under China’s certified emission reduction (CCER) and explore ways to improve the regulatory system.

This study conducts a qualitative analysis using a normative legal research method. This study conducted a synthetic review of national and local regulatory documents to gain insights into the regulatory landscape of forestry offsets in China. The main contents and characteristics of these documents are illustrated. Furthermore, related secondary literature was reviewed to gain further insight into forestry offset regulations and to identify significant gaps in China’s CCER regulation.

Forestry offset regulations under the CCER are characterized by fragmentation and a relatively lower legally binding force. There is no systematic institutional arrangement for forestry offset development, impeding market expectations and increasing transaction costs. The main challenges in China’s regulation of forestry carbon sinks include entitlement ambiguity, complicated rules for registration and verification, a lack of mechanisms for incentives, risk prevention and biodiversity protection.

Forestry carbon sinks’ multiple environmental and social values necessitate their effective development and utilization. This study assessed forestry offset regulations in China and proposed corresponding institutional arrangements to improve forestry carbon sink regulations under the CCER.

Economic literature highlights both positive and negative impact of FDI on economic growth. The purpose of this study is to confirm the relationship between various economic factors and FDI equity inflows and find out deviations, if any. This is investigated using standard time-series econometric models. The long and short run relationship is inquired with respect to market size, inflation rate, level of infrastructure, domestic investment and openness to trade. The choice of variables for Indian economy is purely based on empirical observations obtained from scientific literature review.

The study involves application of autoregressive distributive lag (ARDL) model to investigate the relationship. The long run co-integration between FDI and economic growth is tested by Pesaran ARDL model. The stationarity of data is tested by augmented Dickey Fuller test and Phillip–Perron unit root test. Error correction model is applied to study the short run relationship using Johansen’s vector error correction model method besides other tests.

The results show that the domestic investment, inflation rate, level of infrastructure and trade openness influence inward FDI flows. These factors have both long and short-term relationship with FDI inflows. However, market size is insignificant in influencing the foreign investments inflows. There lies an inverse relation between FDI and inflation rate.

To the best of the authors’ knowledge, the study is original. The methodology and interpretation of results are distinct and different from other similar studies.

This paper empirically investigates whether trade openness (TO) in Brazil, Russia, India, China and South Africa (BRICS) countries affects how banks might employ loan loss provisions (LLPs) to smooth out their earnings and how adopting the International Financial Reporting Standards (IFRS) can mitigate it.

The analysis includes 78 commercial banks from five BRICS nations and spans 2014 through 2020. To test these hypotheses, the authors utilized a fixed-effect and two-step system panel generalized methods of moments (GMM) estimator.

TO positively affects income smoothing (earnings management) across BRICS commercial banks. The effect is clearer in banks that make financial reports under the IFRS. Path analysis reveals that the effect of TO is driven by nonperforming loans (NPLs). Additionally, the IFRS restricts earnings management in the BRICS banking sector when a better institutional environment is present. The authors found that accounting rules (IFRS) and enforcement (better institutional settings) interact to enhance earnings’ quality.

The relationship between TO and bank earnings management practices is important for understanding the complex interplay between trade and finance and ensuring financial stability, investor confidence and regulatory compliance. This study recommends better regulations and governance mechanisms for financial reports in emerging nations like BRICS. Additionally, macro-prudential regulators and banking supervisors should work closely to ensure transparent TO decisions with improved discipline, institutional quality and regulatory support to enhance bank stability.

The study finds evidence of bank income smoothing in the BRICS and introduces TO as a determinant. It also identifies the evolving role of IFRS in the presence of higher institutional quality and TO, thereby expanding the financial reporting literature.