Emerging Market Finance: New Challenges and Opportunities: Volume 21

This study examines the effects of foreign branch activity on commercial banks in the Central, Eastern, and Southeastern European countries for the period 1995–2015. The author shows that more foreign bank branches are present in countries that have higher taxes and regulatory restrictions on bank activity. The increased activity of foreign bank branches adversely affects lending by foreign banks, and to a lesser extent, that of state-owned banks. The author attributes this finding to the fact that foreign bank branches and foreign banks compete for the same type of clients, namely, multinational corporations.

The recent rise of nonperforming loans (NPLs) in some Asian economies calls for close analysis of the determinants, the potential macrofinancial feedback effects, and the implications for financial stability in the region. Using a dynamic panel model, we assess the determinants of the evolution of bank-specific NPLs in Asia and find that macroeconomic conditions and bank-specific factors – such as rapid credit growth and excessive bank lending – contribute to the buildup of NPLs. Further, a panel vector autoregression (VAR) analysis of macrofinancial implications of NPLs in emerging Asia offers significant evidence for feedback effects of NPLs on the real economy and financial variables. Impulse response functions demonstrate that a rising NPL ratio decreases the GDP growth, credit supply and increases the unemployment rate. Our findings underline the importance of considering policy options to swiftly and effectively manage and respond to a buildup of NPLs. The national and regional mechanisms underlying NPL resolution are important for safeguarding financial stability in an increasingly interconnected global financial system.

This chapter studies banks’ loan pricing behavior in mainland China during 2003–2013 by applying panel regressions to firm-level loan data and the estimated default likelihood for listed companies. The authors find that with the progress of market-oriented financial reforms, banks generally require compensation for their exposure to borrowers’ default risks. It is even more so if the borrower is a non-state-owned enterprise (non-SOE), mainly due to the pricing behavior of the Big Four banks. Bank lending rates are shown to be less sensitive to the default risks of state-owned enterprises (SOEs). Our results also reveal that banks priced in firm default risks before 2008 financial crisis, but not necessarily so after the crisis. As for industries, we find that after the 2008 Global Financial Crisis, the real estate sector and other government-supported industries tended to enjoy better terms on loan pricing in terms of default risks. We believe the main reason is that the government stimulus policies tilted toward those industries that have played crucial roles in China’s economic growth.

The Chinese banks have increased their market entry to Russia since their initial entry in 1993 and have expanded their banking business operations in Russia significantly. The banking sector interaction between China and Russia has received great attention and interests from businesses as well as policy-makers. This chapter describes the main activities of Chinese banks in Russia, assesses their achieved results, and discusses their opportunities for further development of banking interactions of the Chinese banks and the Russian banking sector in the future.

This chapter investigates a shock transmission path between a home country (a country where globalized banks’ headquarters are located) and a host country (Indonesia as the emerging market) through the lending channel of global banks’ local branches (i.e., the internal transfer channel). Using novel data of monthly individual foreign bank’s balance sheet in Indonesia, the author finds the evidence that shocks to a parent bank and a home economy are transmitted to a host economy through the foreign banks’ internal capital market. With the Indonesia banks’ capital injections and their difficulty in financing dollar funds without risk premiums since the 1998s crisis, the foreign banks’ dollar lending in Indonesia is a good showcase of internal capital markets. A change in a home stock market index and industrial production appears to have a negative effect on growth rates in foreign currency loans of foreign banks in the host market. On the other hand, high growth rates in the parent bank’s stock price in the home market lead to an increase in foreign banks’ US dollar lending in the host country. This effect does not appear in local currency lending because limited hedging instruments against foreign exchange risk results in immobility of bank capital in the local currency.

This chapter examines spillover effects of global monetary shocks on lending by foreign banks in an emerging country, South Korea. Foreign banks play a significant role by providing additional domestic credit and foreign currency liquidity and directing international capital flows via the banking sector. Using macroeconomic and banking data for the period of 2000Q1–2016Q2, the authors present evidence that foreign bank branches in Korea have responded in providing their foreign currency loans with one-quarter (three months) time lag to changes in monetary policies in their home countries (mainly, the United States and the Euro area). This short-run spillover effect of monetary policy shocks from the home countries to foreign banks in Korea seems consistent with the main findings from our bank-level data analysis. This chapter also discusses useful policy implications.

This chapter examines changes in US monetary policy uncertainty (ΔMPU) and fiscal policy uncertainty (ΔFPU) on stock returns while controlling for downside risk, lagged dividend yield, and time series patterns. Testing G7 markets consistently shows that both ΔMPU and ΔFPU have significant negative impacts on stock returns. Evidence shows that any downside risk, ΔMPU or ΔFPU in US market will soon be transmitted to G6 industrial markets and the impacts are extended to two months. These risk and uncertainty premiums should be priced in the stocks of the major industrial markets.

The aim of this study is to compare volatility persistence with daily volatility and to analyze the asymmetry effect of volatilities in stock markets of emerging economies. Using daily observations of stock market indices of selected major emerging countries during the period of January 1, 2002 to December 31, 2018, the authors estimate the persistence, the half-life measure of volatility and the daily volatility of the return series using the GARCH model application. The authors also examine the leverage effect on stock market returns using the EGARCH model estimation. In addition, the authors investigate the impact of the 2008 global financial crisis on various volatility measures and the leverage effect of emerging stock market returns. The authors then examine and compare the different speeds of mean reversion, volatility persistence and leverage effects in the national stock market indices during the pre-crisis, crisis, and post-crisis periods. The authors hereby present evidence that the effects of negative shocks are significantly larger than those of positive shocks in emerging stock markets throughout their different sample periods.

This chapter examines a multifactor model for stock returns in nine Asian markets (Japan, China, South Korea, Hong Kong, Taiwan, Singapore, Indonesia, Malaysia, and Thailand). The authors develop a model using the market risk premium, size, book-to-market, profitability, investment, momentum, price-to-earnings ratio, and dividend yield factors for each market. The empirical results suggest that this eight-factor model can better explain the variations of stock returns than the original Fama–French three-factor model. Factor-based models using local data outperform those using data from US markets. In addition, the evidence suggests that the eight-factor model can better explain stock returns when the market is under stress.

This chapter examines the existence of dynamic herding behavior by Tunisian investors in the Tunisia stock market during the revolution period of 2011–2013. The sample covers all Tunindex daily returns as a proxy for the Tunisia stock exchange index over the period 2007–2018. The author modifies the cross-sectional absolute deviation model to include all market conditions (bull and bear markets) and the geopolitical crisis effect corresponding to the Tunisian Jasmine revolution during 2011–2013, and show that herding is indeed not present in the Tunisia stock market including during its turmoil periods. These findings imply that the Tunisian emerging financial market became more vulnerable to adverse herding behavior after the revolution. There is also a clear implication for capitalist firms and angel investors in Tunisia that adverse herding behavior tends to exist on days of higher uncertainty and information asymmetry.

This chapter provides a theory model of trade finance to explain the “great trade collapse.” The model shows that, first, the riskiness of international transactions rises relative to domestic transactions during economic downturns; and second, the exclusive use of a letter of credit in international transactions exacerbates a collapse in trade during a financial crisis. The basic model considers banks’ optimal screening decisions in the presence of counterparty default risks. In equilibrium, banks will maintain a higher precision screening test for domestic firms and a lower precision screening test for foreign firms, which constitutes the main mechanism of the model.

This chapter explores the major drivers of capital structure, which is measured by using two alternative measures (total debt/equity and total debt/total assets), for Romanian firms. By employing panel-data models for a sample of non-financial companies publicly traded on the Bucharest Stock Exchange, this research examines how capital structure of the Romanian firms are affected by CEO age and several firm-specific characteristics including free cash flow, return on assets (ROA), return on invested capital (ROIC), effective tax rate, dividend payout ratio, cash ratio, current ratio, and quick ratio, where firm-level controls (total assets and firm age) are adopted. Using fixed effects estimation on panel data, we find: (1) ROIC, dividend payout ratio and liquidity ratios all negatively affect capital structure; (2) whereas ROA provides evidence of its mixed role on capital structure. Robustness checks using the generalized method of moments reinforce the negative impact of dividend payout ratio and the mixed influence of ROA, and document the varied effects of liquidity measures on capital structure.

This chapter examines various conditions for optimality in financial inclusion. The optimal level of financial inclusion is achieved when basic financial services are provided to members of the population at a price that is affordable and that price is also economically sufficient to encourage providers of financial services to provide such financial services on a continual basis. Any level of financial inclusion that does not meet these conditions is sub-optimal and incentive-inefficient both for users and providers of financial services.

The chapter examines the relationship between social inclusion patterns and economic development in selected EU countries. This research has two objectives: (i) to reveal whether there are similarities among the EU countries in nation’s social inclusiveness of three demographic groups, namely the entire population, immigrants, and Roma communities; and (ii) to analyze the influence of social inclusion indicators on sustainable economic development and prosperity for the EU countries by accounting for the ethnicity impact. The chapter presents the results of a cluster analysis approach, which indicates best-positioned countries and emphasizes vulnerabilities in terms of social inclusiveness both in a broad demographic sense (the entire population) and a narrow sense (immigrants and respective Roma population). The chapter then employs a panel data regression approach to investigate which social inclusion indicators might have the potentially influencing role on economic development. Seven alternative proxies for sustainable economic development and prosperity measures were used. The social inclusion indicators, as explanatory variables, are represented by the education-related indicators and labor market-related indicators. The robustness and stability of the estimates are validated by including several interaction terms in the baseline regression model to account for the occurrence of the financial crisis. Overall, improving both population as well as immigrants’ inclusion is shown to have an important impact on sustainable economic development in the EU countries.