Central Bank Review最新文献

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.

An important question for the major central banks of the industrialized world is how to design desirable strategies to exit quantitative easing (QE). At the exit, if a central bank needs to reduce rapidly the liquidity created by its balance sheet expansion, issuing new interest-bearing liabilities would be preferable to rapidly shrinking the balance sheet by selling existing assets, both for the stability of those assets' markets and to be able to keep capital losses from being reflected in the balance sheet under amortized-cost accounting. Given that existing assets accumulated during the quantitative easing period have low interest returns, and new liabilities to be issued in the quantitative tightening period would have high interest payouts, the central bank may run a loss that may threaten its solvency, which may force the bank to expand the monetary base above the level that is consistent with the central bank's ideal price stability path. This study considers a central bank that exits QE by issuing liabilities and examines an optimal exit strategy while maintaining the solvency by constructing a simple dynamic optimization model. The model is then applied to the Bank of Japan and the Federal Reserve to examine their possible exits.

Turkey has experienced a rapid increase in exports during the last two decades. In addition, there has been a significant increase in the exchange rate and its volatility in recent years. Hence, the empirical examination of the volatility-export nexus in a comprehensive framework seems to be important to provide insights for policymakers. In this study, we investigate how the exchange rate volatility affects Turkey's exports to its major partners namely, Belgium, France, Germany, Italy, Netherlands, Russia, Spain, the UK, and the USA for the period of 2002:01–2019:12. Considering the existence of an asymmetric effect of volatility on trade, we separate positive changes of volatility from negative changes via the partial sum concept and introduce nonlinearity into the estimation and testing procedure. Our results indicate that (i) exchange rate volatility plays quite important role for Turkey's export, (ii) asymmetry matters for better understanding the volatility-export nexus, (iii) the impact of volatility is country and commodity-specific, (iv) exchange rate volatility shows higher impacts on capital and consumption goods export. Lastly, exchange rate volatility affects exports in opposite directions in the short and long-run. Both low and high volatility generally increase (decrease) Turkey's exports in the short-run (long-run). These results provide important implications for policymakers.

This article analyses Turkey's safeguard (SG) actions using a renovated approach to the measurement of the impacts of the SG duties with empirical application introduced by Bown and McCulloch (2004). We examine the trade impacts of 16 safeguard duties, covering 52 different 4 and 6-digit Harmonized System (HS) product categories, implemented by Turkey between 2003 and 2013, and we aim to reveal whether these measures had discriminatory impacts on those trading partners, whose imports represented a threat to the domestic importing industry. Since Turkish applications of SGs vary widely in terms of their duration, target markets and forms, this makes Turkey an interesting case study for 2003–2013 period, as Turkey mostly used SG applications based on additional financial obligations, not in the form of quotas after 2014. The empirical methodology is based on the approach introduced by Bown and McCulloch (2004), which serves as an attempt to approximate dynamic specifications in the context of cross-sectional data. Our findings show that the SGs applied by Turkey during the period of 2003–2013 effectively had a discriminatory impact on imports from major trading partners, and quotas and tariff rate quotas were more effective than tariffs for restricting imports.

The aim of this study is to analyze the location decisions of the manufacturing industry companies in the ICI Top 500 and Second Top 500 Industrial Enterprises (ICI-1000) for the year 2018. The location choice model developed accordingly is based on the assumption that companies choose the location of their production facilities with “the goal of profit maximization, and that this decision is influenced by both internal (company-specific) and external (sectoral and regional) factors”. The empirical analysis is conducted utilizing the nested logit estimation method and a large data set containing information on 909 manufacturing industry companies among ICI-1000, sub-sectors and location alternatives. The results support the views of Neoclassical and Institutional location approaches. The location decisions of the companies are affected by the characteristics specific to the company, sector and location. The ICI-1000 companies in the study tend to locate in areas with high market power and market growth, qualified and abundant labor, high sectoral growth and diversity and good geographical and physical conditions. The impact of these factors on company location preferences varies depending on the technological intensity of the industry in which they operate (high/low). Companies operating in high-tech (high, medium-high, medium-low) sectors choose places with a diversified and deepened labor pool, sectoral diversity and knowledge diffusion. On the other hand, the effect of specialization that emerged with localization economies is crucial in the location decisions of companies in low-tech sectors.

This paper investigates the degree of dispersion in the loan pricing of commercial banks and its association with competitive conditions in the banking industry of a large emerging economy. To quantify the lending rate variability in consumer loans, we utilize a new indexation mechanism exploiting a detailed bank-level dataset for the period January 2007–April 2020. With panel convergence methods, we show the existence of heterogeneity in long-term co-movements among banks' loan pricing, while periods following the tightening in financial conditions display short-term deviations from general tendencies as demonstrated by dispersion indices. Our empirical design also entails the construction of competition indicators for aggregated and consumer segment-based credit market developments. Quantile regression results validate that the improvements in industry competition are related to the lower level of lending rate dispersion in housing and vehicle segments in a statistically significant manner, whereas an opposite relationship is evident for general-purpose loans.

This paper estimates potential growth in Turkey using a production function estimation approach. Our approach aims to measure the inputs of production in the most detailed fashion that is possible and empirically addresses concepts of sustainable potential growth for Turkey. While developing measures of the sources of potential growth, we provide a thorough discussion of the estimated trends in labor force participation, capital growth by asset type, and total factor productivity since the mid-2000s. Our results suggest that the key driver of potential growth has increasingly been capital accumulation. The declining trend in the positive TFP growth stands out as the key area of improvement for potential growth.

Central Bank speeches usually function as aggregators of internal quantitative and qualitative analysis of the institutions regarding the macro economy, the monetary policy and the health of the financial systems. Speeches usually function as a summary of the current status of a countries economic health, the undergoing trends and some future perspectives of the global economy. In this study departing from classical econometrics we employ natural language processing technologies in combination with machine learning techniques in order to filter out the most important signals in the corpus of speeches and translate into a sentiment index for forecasting the future financial markets behaviour. In our analysis, it is evident that central banker's expectations on economy tend to exhibit a predictive ability for financial markets turmoil. Using a combination of dictionaries which are either predefined or build based on historical speeches of the corpus we train an Extreme Gradient Boosting model that generates a sentiment index which signals turmoil with acceptable accuracy when passing a specific threshold.