Central Bank Review最新文献

Using a Monte Carlo method and quarterly data from the 2019 Household Expenditure and Income Survey, we examine the resilience of urban and rural households to various shocks, including exchange rate change, changes in asset prices, job losses, and decline in income. Based on the exposure at default (EAD) estimates, the largest impact was observed in the case of an income shock. The EAD values ranged from 0.38 to 0.43 compared to a baseline case of 0.35. The latter indicates that about one-third of the debt held by households may be problematic, especially in rural areas. The second largest impact is seen for a major currency devaluation, followed by the rise in unemployment. In addition, the breakdown of these results by income shows that households in the lower income quartiles are more vulnerable. Potential implications are discussed.

The design of pension schemes is crucial in determining savings behavior. The impact of pension schemes on saving rates across countries remains to be an intriguing empirical question considering the complicated nature of the relationship between saving patterns and pension wealth. This paper investigates the effect of the private pension contributions on savings rates in 25 selected OECD countries between the period 2001–2019 by employing quantile regression analysis which takes the heterogeneity of the data into account and provides information about not only the midpoint but also the extreme points of the distribution. According to the results, the savings rate is negatively associated with private pensions at all quantile levels. The empirical findings indicate that pension contributions tend to be strong substitutes for voluntary savings in countries with low tendencies to save. This result is especially important for its policy design implications as the policy makers tend to provide incentives either in the form of tax reliefs or direct substitutions for private pension contributions with the motivation to raise domestic savings.

Do extreme events have a significant effect about textual sentiment? The purpose of this article is to highlight the need to correct the estimation of indicators of economic uncertainty. The indicators were constructed from textual data about the perspective of extreme events. For this purpose, based on data extracted from the minutes of mee-tings of the Monetary Policy of eighteen Central Banks, we estimated two variables of perception of economic uncertainty: the first using only a traditional sentiment dictio-nary and the second incorporating terms associated with the extreme event (COVID- 19 Pandemic) in its word list. Initial results show that there is a significant effect of COVID-19 on the estimation of the perception of economic uncertainty; this effect acts as an accelerator that potentiates its impact. It was evident that incorporating conjunctural issues - be it local or global - is indispensable when performing sentiment analysis in texts during extreme events. Moreover, failing to take conjunctural issues into account throughout the estimation process can result in variables with biased in-formation.

This study investigates the role of sectoral market structure in the inflationary dynamics of the domestic producer prices (D-PPI) in Türkiye. We start by reporting the fact that industries with intense competition had lower producer price inflation compared to industries with low-competition in the past few years. Further investigation shows that this differentiation across industries corresponds to the years with higher exchange rate volatility. The industries characterized with low-competition have higher annual producer price inflation on average than those that are characterized with high-competition especially during the periods of high exchange rate volatility. Results are robust to a wide set of additional specifications and cast light on the role of market structure on firms’ pricing behavior in Türkiye.

Global financial crisis has shown the importance of understanding the structure of interbank relations. In this study, we investigate the network relations based on interbank exposures in Türkiye. We estimate several network statistics and document how the network relations have changed over the time period of 2002–2021. We find that the network structures vary substantially by financial instruments such as repo, deposit, loan, security issuances, derivatives and other off-balance sheet items showing the significance of covering all type of exposures in network analysis. Using network statistics, we show that Turkish interbank network structure shows a core-periphery structure which is found to be more resilient during stress times in the literature. Finally, we find that larger banks are characterized as having higher network centrality measures as degree, clustering coefficient and closeness centrality showing the importance of these banks in terms of intermediation and substitutability.

This paper examines the lead/lag relations between size-sorted portfolio returns through the lens of financial cycles governing these returns using a novel econometric methodology. Specifically, we develop a Markov-switching vector autoregressive model that allows for imperfect synchronization of cyclical regimes such as bull and bear market regimes in US large-, mid- and small-cap portfolio returns. This is achieved by characterizing the cycles of the mid- and small-cap portfolio returns in concordance with the cycle of large-cap portfolio returns together with potential phase shifts. We find that a three-regime model with distinct phase shifts across regimes characterizes the joint distribution of returns most adequately. These regimes are closely linked to the business cycle and small-cap portfolio returns are more sensitive to the cyclical phases than the large-cap portfolios. While all portfolios switch contemporaneously into boom and crash regimes, the large-cap portfolio leads the small-cap portfolio for switches to a moderate regime from a boom regime by a month. This suggests that small-cap portfolio adjusts with a delay to the relatively negative news compared to portfolios with larger market capitalization. We document that information diffusion accelerates in response to surprises related to the monetary policy. This reflects a link between financial returns and real economic activity from the viewpoint of ‘financial accelerator theory’ where portfolios with distinct size serve as a proxy for firm characteristics.

The aim of this study is to analyze the impact of exporting on labor demand in Turkish manufacturing industry. By using Generalized Methods of Moments (GMM) with the firm-level production and trade data of Turkish manufacturing industry, this paper is exploring the employment impact of international trade. The analysis is based on firm level data obtained from Turkish Statistical Institute (TURKSTAT) and covers the period from 2003 to 2013. The estimations were carried out for different technology-oriented industries and 2-digit NACE sub-industries to see how the labor demand dynamics change. The results showed that both manufacturing exports and imports have significant and positive impact on the labor demand of the firm. The impact, on the other hand, was found to differ not only in the firms operating in different technology-oriented industries but also in different sub-industries of Turkish manufacturing.

The paper aims to analyze the effect of bank risk appetite on banks' default probabilities during the year of COVID-19 in 12 countries while controlling for bank-specific and country-specific effects over time. A System Generalized Methods of Moments (GMM) model of default probabilities is estimated over the periods 2010–2021. This study confirms the ‘risk-mitigation view’, in which banks with higher ESG scores are more prudent in lending and have better relationship management, reducing the probability of bank default. Underperforming banks tend to have a higher portion of risky loans in their credit portfolio and therefore demonstrating a higher default propensity. Bank risk appetite, ESG, asset quality, economic growth, and currency depreciation appear to be material drivers for bank risk. We find that a lower risk appetite ratio (corresponding to higher risk appetite) is associated with higher estimated default probability during the COVID-19 outbreak, identified through interaction with a single time dummy for 2020 (the break-out year of the pandemic).

This paper investigates the impact of Covid-19 pandemic and monetary policy measures adopted by the European Central Bank (ECB) on the sovereign risk for the European Monetary Union (EMU) countries for the period between March-2020 and November-2020 using daily data. The impact of Covid-19 and monetary policy shocks on the credit default swap rates and bond yields are investigated relying on a fixed effects panel regression model for five core (Germany, France, Austria, Netherlands, Belgium) and three periphery (Italy, Portugal and Spain) countries. To investigate the cross-country differences in responses, the interactions of the independent variables with periphery dummy and other country-specific variables are included in the regressions. The results of the empirical analysis suggest that Covid-19 shock increased the sovereign risk in the periphery EMU countries significantly and monetary policy measures have been effective in easing financial conditions in these countries. The results are insignificant for the core countries. The results also show that financial stability alleviates the negative impact of Covid-19 on the sovereign risk.

This paper examines prediction of U.S. bank failure with a probit model that uses bias-corrected technical efficiency estimated using bootstrap data envelopment analysis as the measure of management quality. The model is tested on a sample of failed and non-failed banks during the sub-prime mortgage meltdown, 2008–2009. Results demonstrate this measure of management efficiency, together with other CAMEL factors (i.e., capital adequacy, asset quality, earnings quality, and liquidity), is significant for predicting bank failure. This measure of managerial quality allows more accurate prediction of failure than other measures. The model successfully predicts bank failure one and two years prior to failure.