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This book deals with the challenges of macro financial linkages in the emerging markets.
This paper assesses and disseminates experiences and lessons from low-income countries (LICs) in Sub-Saharan Africa that were selected by the Africa Department in 2015-16 as pilots for enhanced analysis of macro-financial linkages in Article IV staff reports. The paper focuses on the common characteristics across the pilot countries and highlights the tools used in the analysis, the challenges encountered, and the solutions deployed in overcoming them.
This paper investigates macro-financial linkages in Egypt using two complementary methods, assessing the interaction between different macroeconomic aggregates and loan portfolio quality in a multivariate framework as well as through a panel vector autoregressive method that controls for bank-level characteristics. Using a panel of banks over 1993-2010, the authors find that a positive shock to capital inflows and growth in gross domestic product improves banks’ loan portfolio quality, and that the effect is fairly similar in magnitude using the multivariate and panel vector autoregressive frameworks. In contrast, higher lending rates may lead to adverse selection problems and hence to a drop in portfolio quality. The paper also reports that a larger market share of foreign banks in the industry improves loan quality.
According to a dynamic panel estimated over 1995 - 2008 on around 80 banks in the GCC region, the NPL ratio worsens as economic growth becomes lower and interest rates and risk aversion increase. Our model implies that the cumulative effect of macroeconomic shocks over a three year horizon is indeed large. Firm-specific factors related to risk-taking and efficiency are also related to future NPLs. The paper finally investigates the feedback effect of increasing NPLs on growth using a VAR model. According to the panel VAR, there could be a strong, albeit short-lived feedback effect from losses in banks’ balance sheets on economic activity, with a semi-elasticity of around 0.4.
Macro-prudential policies aimed at mitigating systemic financial risks have become part of the policy toolkit in many emerging markets and some advanced countries. Their effectiveness and efficacy are not well-known, however. Using panel data regressions, we analyze how changes in balance sheets of some 2,800 banks in 48 countries over 2000–2010 respond to specific macro-prudential policies. Controlling for endogeneity, we find that measures aimed at borrowers––caps on debt-to-income and loan-to-value ratios––and at financial institutions––limits on credit growth and foreign currency lending––are effective in reducing asset growth. Countercyclical buffers are little effective through the cycle, and some measures are even counterproductive during downswings, serving to aggravate declines, consistent with the ex-ante nature of macro-prudential tools.
We provide a comprehensive empirical characterization of the linkages between key macroeconomic and financial variables around business and financial cycles for 21 OECD countries over the period 1960–2007. In particular, we analyze the implications of 122 recessions, 112 (28) credit contraction (crunch) episodes, 114 (28) episodes of house price declines (busts), 234 (58) episodes of equity price declines (busts) and their various overlaps in these countries over the sample period. Our results indicate that interactions between macroeconomic and financial variables can play major roles in determining the severity and duration of recessions. Specifically, we find evidence that recessions associated with credit crunches and house price busts tend to be deeper and longer than other recessions. JEL Classification Numbers: E32; E44; E51; F42
This paper reviews the literature on financial crises focusing on three specific aspects. First, what are the main factors explaining financial crises? Since many theories on the sources of financial crises highlight the importance of sharp fluctuations in asset and credit markets, the paper briefly reviews theoretical and empirical studies on developments in these markets around financial crises. Second, what are the major types of financial crises? The paper focuses on the main theoretical and empirical explanations of four types of financial crises—currency crises, sudden stops, debt crises, and banking crises—and presents a survey of the literature that attempts to identify these episodes. Third, what are the real and financial sector implications of crises? The paper briefly reviews the short- and medium-run implications of crises for the real economy and financial sector. It concludes with a summary of the main lessons from the literature and future research directions.
This is the third of a series of papers that are being written as part of a larger project to estimate a small quarterly Global Projection Model (GPM). The GPM project is designed to improve the toolkit for studying both own-country and cross-country linkages. In this paper, we estimate a small quarterly projection model of the US, Euro Area, and Japanese economies that incorporates oil prices and allows us to trace out the effects of shocks to oil prices. The model is estimated with Bayesian techniques. We show how the model can be used to construct efficient baseline forecasts that incorporate judgment imposed on the near-term outlook.
No sooner had the Asian crisis broken out in 1997 than the witch-hunt started. With great indignation every Asian economy pointed fingers. They were innocent bystanders. The fundamental reason for the crisis was this or that - most prominently contagion - but also the decline in exports of the new commodities (high-tech goods), the steep rise of the dollar, speculators, etc. The prominent question, of course, is whether contagion could really have been the key factor and, if so, what are the channels and mechanisms through which it operated in such a powerful manner. The question is obvious because until 1997, Asia's economies were generally believed to be immensely successful, stable and well managed. This question is of great importance not only in understanding just what happened, but also in shaping policies. In a world of pure contagion, i.e. when innocent bystanders are caught up and trampled by events not of their making and when consequences go far beyond ordinary international shocks, countries will need to look for better protective policies in the future. In such a world, the international financial system will need to change in order to offer better preventive and reactive policy measures to help avoid, or at least contain, financial crises.
This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance. It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies. The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks