Eurasian Economic Review最新文献

Fluctuations in crude oil prices exert substantial economic influence, necessitating adept responses and strategic policy formulations to mitigate potential adverse consequences. Kilian has emphasized the heterogeneous nature of oil price shocks, wherein price rises can yield varied effects contingent upon underlying determinants. Consequently, it is imperative for investors, economists, and policymakers to disentangle real price shocks and assess their impact on macroeconomic aggregates. This study employs a two-stage approach grounded in a structural vector autoregressive (SVAR) model, inspired by Kilian’s framework, to examine and contrast the repercussions of various crude oil price shocks on the actual Gross Domestic Product (GDP) and Consumer Price Index (CPI) in countries that are either net importers or exporters of oil. Our empirical results reveal that variations in real oil prices are more substantially influenced by shocks due to aggregate demand and precautionary demand, as opposed to shocks originating from the oil supply side. Additionally, aggregate demand shocks lead to significant GDP surges for most oil-importing countries and all oil-exporting countries, while only leading to continuous CPI increases in oil-importing countries. Precautionary demand shocks initially boost GDP in oil-exporting countries but lead to GDP reductions in oil-importing countries. Precautionary demand shocks sustain CPI increases in the oil-importing countries, though with variations in significant durations, but have mixed effects in oil-exporting countries, with significant CPI increases observed in Canada and Norway. Concerning the implications for policymakers and investors, the findings underscore the importance of considering variations in response patterns to crude oil price shocks based on their drivers and the country’s status as an oil importer or exporter.

Access to financial services, which is a component of financial inclusion, enables individuals and businesses to access the funds they need on favorable terms and at low cost, without time and place constraints. In this respect, access to finance is seen as an important tool for economic growth, employment and poverty reduction. The study aims to examine the role of the variables of shadow economy (SE), human development level (HDI), automatic teller machines usage (ATM) and inflation (INF) on access to financial services (AFS) in the member countries of the Organization of Turkic States (OTS). In the study, which covers the period 2004–2021, analyzes were conducted within the framework of the fixed-effect Method of Moment Quantile Regression (MM-QR). According to the results of the study, while the level of HDI and the use of ATMs have a positive effect on AFS, while the SE has a negative effect. Although the INF rate has a negative effect on AFS, this effect could not be confirmed at any quantile level.

This paper investigates the key factors driving the capital gains overhang (hereafter CGO) in the US stock market. We used a sample of 3865 non-financial US companies with 331,023 observations from January 2001 through December 2020. The data is analyzed using a panel regression model. It contributes to the literature by using a new set of firm characteristics, namely, liquidity proxied by turnover, company beta, leverage, EPS, cash flow to price, market to book ratio, and size. This research is interesting as it provides an alternative to the behavioral finance point of view that serves only limited stylized facts. We find that CGO is increasing in some firm attributes, namely earnings per share, leverage, growth, and size, and decreasing in others, namely turnover, beta, and cash flow to price. Our results are robust to cross-sectional regression that checks the stability of estimates over time and to subsample analyses. Finally, our results remain the same even after accounting for endogeneity.

The purpose of this study is to explore the influence of macroprudential measures on financial stability in both advanced and emerging economies, considering key endogenous variables such as banking crises and economic growth. Our analysis covers 66 countries over the period 2000–2017 and focuses on two effects: the direct stabilizing effect, which reduces the likelihood of banking crises, and the indirect destabilizing effect, which may slow down the GDP growth. Employing Generalized Impulse Response Function modeling, we assess the efficacy of macroprudential policy tools. Our findings suggest that macroprudential tightening is more effective in reducing the likelihood of banking crises than in stimulating economic growth, particularly when the impact of macroprudential tools is positive. Ultimately, our study highlights the interplay between the probability of banking crises and financial stability, demonstrating the importance of macroprudential regulation in promoting a stable and resilient financial system.

This paper aims to uncover the cross-sectional relationship between country-level sustainability performance and the domestic lending activity of commercial banks. Considering a worldwide sample of countries, it uses publicly available sustainability scores from SolAlability Sustainable Intelligence, macroeconomic data, and banking sector data from the World Bank. The results show that the country’s sustainability performance is positively related to the amount of domestic credit granted by banks to the private sector and negatively related to the importance of nonperforming loans. Moreover, looking at the pillars that constitute the sustainability scores, this work finds evidence that social cohesion, intellectual capital, and governance are the pillars of sustainability that affect domestic lending activity. Results survive several robustness tests concerning samples, variables’ definitions, and estimation procedures. Our results suggest that policies aiming at improving a country’s sustainability contribute to domestic banking sector stability and financial development. Specifically, measures that contribute to social cohesion and solidarity, innovation and value-added industries, and the country’s governance performance, contribute to the smooth functioning of credit markets.

We decompose the yield curve of U.S. Treasury bonds into three components—the level, slope, and curvature. We then explore the interaction between these factors and uncertainty in the U.S. bond market. We assess this uncertainty using a VIX-style estimate originating in options on the CBOE’s Treasury Note futures. Using monthly data for 2003–2020, we find that interest rate uncertainty drives the evolution in the shape of the yield curve, but not vice versa. Specifically, the bond market’s VIX-style metric not only correlates with but also influences the yield curve’s level and slope. Moreover, increased uncertainty about interest rates is negatively associated with, and can significantly influence, the yield curve’s curvature. The results of this study are crucial for both policymakers and money managers.

This paper investigates the interconnectedness patterns between agricultural commodities, crude oil, and ethanol, along with their determinants before and during the COVID-19 pandemic and the Russia–Ukraine conflict. We employ a time-varying parameter vector autoregression model to analyze interconnected behaviors among energy and agricultural commodities. Additionally, quantile regression is used to assess the impact of financial and economic fundamentals on transmission mechanisms in commodity markets. The empirical findings reveal time-varying and crisis-responsive linkages between energy and agricultural commodities, particularly during the COVID-19 pandemic and Russia–Ukraine conflict. Furthermore, economic and financial market uncertainties emerge as significant determinants of the interconnectedness between these commodity groups.

This paper aims to identify herding biases and assess the inefficiency of Ethereum using an inefficiency index (MLM). Additionally, it investigates the nonlinear dynamical properties of Ethereum by estimating the MFDFA, aiming to deduce the impact of COVID-19 on Ethereum’s performance. The paper also captures abnormal changes resulting from COVID-19-related events and assesses their influence on the Ethereum market response.

The empirical results show that Ethereum was multifractal before the pandemic and became less fractal in the period following the outbreak using Generalized Hurst Exponent (GHE) estimation. Based on the MLM measure of efficiency, we found Ethereum to be more efficient in the first phase of the pandemic than before it, and as in the Hausdorff topology, the pandemic reduced herd bias. The event study analysis took into account specific events related to the pandemic and showed that each led to significant abnormal returns in the Ethereum market. The results reported are used to empirically establish differences in the value of Ethereum before and during the COVID-19 pandemic. The results are useful in a general sense for traders, investors, and policy makers because they provide new information about market trading opportunities and social responses.

Peer-to-peer (P2P) lending has attracted scholarly attention because of its economic significance and potential to democratize access to finance. However, P2P lending platforms face many challenges and failures that we need to understand more clearly. We build a computational model to study how borrower default affects P2P platform lending. We show that borrower default disrupts the P2P network formation process and undermines platform stability. Moreover, we find that defaults increase the inequality in accessing funding and provide a rationale for using curation rules, widely used in P2P platforms, in contrast to P2P insurance, which fosters cascading defaults. We also address a new trend in P2P lending platforms in which large companies (institutional investors) play an increasingly important role. We find that the presence of large companies creates a denser network (more loans) but generates a trade-off between making the platform more resilient to cascading defaults and more dependent on specific players. Overall, we explain how borrower defaults affect platform stability and what makes a platform vulnerable, threatening its survival. We discuss research and managerial insights into platform stability and the economic effect of P2P lending platforms.

The paper focuses on the relationship between cryptocurrencies and economic policy uncertainty (EPU) shocks by adopting robust econometric techniques. Results on monthly data from 2016 to 2022 confirm that the volumes of cryptos are stationary, and the short- and long-run impacts of uncertainty shocks are significantly positive, and, in most cases, begin to show already in the first six months after the shock. When uncertainty significantly prevails, investors increase their demand for cryptos. The ARDL(12,11) specifications for Bitcoin show a significant increase in volumes of around 1% occurring over a year after a unit increase in economic policy indices. Conclusions from the findings are related to supervision, and monitoring of markets and financial stability.