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This study reviews the literature on the origins of currency and banking crises. It presents empirical tests on the performance of alternative early-warning indicators for emerging-market economies. The book also identifies crisis-threshold values for early-warning indicators.
Household financial fragility has received considerable attention following the global financial crisis, but substantial gaps remain in the analytical underpinnings of household financial vulnerability assessment, as well as in data availability. This paper aims at integrating the contributions in the literature in a coherent fashion. The study proposes also analytical and estimation extensions aimed at improving the quality of estimates and allowing the assessment of household financial vulnerability in presence of data limitations. The result of this effort is a comprehensive framework, that has wide applicability to both advanced and developing economies. For illustrative purposes the paper includes a detailed application to one developing country (Namibia).
The European currency crises of 1992-93, the Mexican crisis of 1994-95, and especially the Asian/global crisis of 1997-98, have all contributed to a heightened interest in the early warning signals of financial crises. This pathbreaking study presents a comprehensive battery of empirical tests on the performance of alternative early warning indicators for emerging-market economies that should prove useful in the construction of a more effective global warning system. Not only are the authors able to draw conclusions about which specific indicators have sent the most reliable early warning signals of currency and banking crises in emerging economies, they also test the out-of-sample performance of the model during the Asian crisis and find that it does a good job of identifying the most vulnerable economies. In addition, they show how the early warning system can be used to construct a "composite" crisis indicator to weigh the importance of alternative channels of cross-country "contagion" of crises, and to generate information about the recovery path from crises. This timely study comes on the eve of impending changes at the International Monetary Fund as that institution reexamines how it reacts to financial crises. Moreover, the study provides "... a wealth of valuable elements for anyone investigating and forecasting adverse developments in emerging markets as well as industrial countries," according to Ewoud Schuitemaker, vice president of the economics department at ABN AMRO Bank.
Local Governments’ Financial Vulnerability presents a conceptual framework developed to examine how vulnerable local finances were before and in the immediate aftermath of the COVID-19 pandemic crisis by mapping and systematising its dimensions and sources. The model is then applied to eight countries with different administrative models and traditions: Australia, Austria, Bosnia and Herzegovina, Germany, Italy, Portugal, Spain, and the United States. Comparative results reveal not only that COVID-19 impacts and policy tools had a lot of similarities across countries, but also that financial vulnerability has an inherently contingent nature in time and space and can lead to paradoxical outcomes. The book shows that the impact of the crisis on local governments’ finances has been postponed and that financial vulnerability is expected to increase dramatically for a few years following the pandemic, especially in larger and richer municipalities which are traditionally more autonomous and less financially vulnerable. The authors provide timely insights and analytical tools that can be useful for both academic and public policy purposes, to further appreciate local governments’ financial vulnerability, especially during crises. This book is a valuable resource for practitioners and academics, as well as students of public policy, public management, financial management, and public accounting. Local governments can use the framework to better appreciate and manage their financial vulnerability, while oversight authorities can use it to help local governments become less financially vulnerable or, at least, more aware of their financial vulnerability. Financial institutions, advisors, and rating agencies may use this publication to refine or revise their models of credit risk assessment.
The global economy has experienced four waves of rapid debt accumulation over the past 50 years. The first three debt waves ended with financial crises in many emerging market and developing economies. During the current wave, which started in 2010, the increase in debt in these economies has already been larger, faster, and broader-based than in the previous three waves. Current low interest rates mitigate some of the risks associated with high debt. However, emerging market and developing economies are also confronted by weak growth prospects, mounting vulnerabilities, and elevated global risks. A menu of policy options is available to reduce the likelihood that the current debt wave will end in crisis and, if crises do take place, will alleviate their impact.
The October 2019 Global Financial Stability Report (GFSR) identifies the current key vulnerabilities in the global financial system as the rise in corporate debt burdens, increasing holdings of riskier and more illiquid assets by institutional investors, and growing reliance on external borrowing by emerging and frontier market economies. The report proposes that policymakers mitigate these risks through stricter supervisory and macroprudential oversight of firms, strengthened oversight and disclosure for institutional investors, and the implementation of prudent sovereign debt management practices and frameworks for emerging and frontier market economies.
We explore empirically how the time-varying allocation of credit across firms with heterogeneous credit quality matters for financial stability outcomes. Using firm-level data for 55 countries over 1991-2016, we show that the riskiness of credit allocation, captured by Greenwood and Hanson (2013)’s ISS indicator, helps predict downside risks to GDP growth and systemic banking crises, two to three years ahead. Our analysis indicates that the riskiness of credit allocation is both a measure of corporate vulnerability and of investor sentiment. Economic forecasters wrongly predict a positive association between the riskiness of credit allocation and future growth, suggesting a flawed expectations process.
This book features technical portrayals of today’s constantly developing banking issues; including stock market contagion, the impact of internet technology (IT) and financial innovation on stock markets, and a perspective on the loan puzzle in emerging markets.
In most of the currency crises of the 1990s, the largest output falls have occurred in those emerging economies with large currency mismatches, a phenomenon that occurs when assets and liabilities are denominated in different currencies such that net worth is sensitive to changes in the exchange rate. Currency mismatching makes crisis management much more difficult since it constrains the willingness of the monetary authority to reduce interest rates in a recession (for fear of initiating a large fall in the currency that would bring with it large-scale insolvencies). The mismatching also produces a "fear of floating" on the part of emerging economies, sometimes inducing them to make currency-regime choices that are not in their own long-term interest. Authors Morris Goldstein and Philip Turner summarize what is known about the origins of currency mismatching in emerging economies, discuss how best to define and measure currency mismatching, and review policy options for reducing the size of the problem.
Pattern Recognition has a long history of applications to data analysis in business, military and social economic activities. While the aim of pattern recognition is to discover the pattern of a data set, the size of the data set is closely related to the methodology one adopts for analysis. Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery tackles those data sets and covers a variety of issues in relation to intelligent data analysis so that patterns from frequent or rare events in spatial or temporal spaces can be revealed. This book brings together current research, results, problems, and applications from both theoretical and practical approaches.