This book provides insight into current research topics in finance and banking in the aftermath of the financial crisis. In this volume, authors present empirical research on liquidity risk discussed in the context of Basel III and its implications. Chapters also investigate topics such as bank efficiency and new bank business models from a business diversification perspective, the effects on financial exclusion and how liquidity mismatches are related with the bank business model. This book will be of value to those with an interest in how Basel III has had a tangible impact upon banking processes, particularly with regard to maintaining liquidity, and the latest research in financial business models.
The Global Financial Crisis unleashed changes in the operating and regulatory environments for large international banks. This paper proposes a novel taxonomy to identify and track business model evolution for the 30 Global Systemically Important Banks (G-SIBs). Drawing from banks’ reporting, it identifies strategies along four dimensions –consolidated lines of business and geographic orientation, and the funding models and legal entity structures of international operations. G-SIBs have adjusted their business models, especially by reducing market intensity. While G-SIBs have maintained international orientation, pressures on funding models and entity structures could affect the efficiency of capital flows through the bank channel.
The next few years will be critical for Europe's banking industry. It faces a number of financial sector reforms that will have a decisive impact on the dominant practices and business models followed across the European Union. This timely volume presents the results of the first screening exercise conducted on the performance, stability, risk, efficiency, and corporate governance of twenty-six major European banks--before, during, and after the financial crisis. The authors use those findings to help identify the key strengths and weaknesses inherent in the dominant business models, in light of the upcoming regulatory changes.
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Until about twenty years ago, the consensus view on the cause of financial-system distress was fairly simple: a run on one bank could easily turn to a panic involving runs on all banks, destroying some and disrupting the financial system. Since then, however, a series of events—such as emerging-market debt crises, bond-market meltdowns, and the Long-Term Capital Management episode—has forced a rethinking of the risks facing financial institutions and the tools available to measure and manage these risks. The Risks of Financial Institutions examines the various risks affecting financial institutions and explores a variety of methods to help institutions and regulators more accurately measure and forecast risk. The contributors--from academic institutions, regulatory organizations, and banking--bring a wide range of perspectives and experience to the issue. The result is a volume that points a way forward to greater financial stability and better risk management of financial institutions.
Major events such as the Asian crisis in 1997, the Russian default on short-term debt in 1998, the downfall of the hedge fund long-term capital management in 1998 and the disruption in payment systems following the World Trade Center attack in 2001, all resulted in increased management’s attention to liquidity risk. Banks have realized that adequate systems and processes for identifying, measuring, monitoring and controlling liquidity risks help them to maintain a strong liquidity position, which in turn will increase the confidence of investors and rating agencies as well as improve funding costs and availability. Liquidity Risk Measurement and Management: A Practitioner’s Guide to Global Best Practices provides the best practices in tools and techniques for bank liquidity risk measurement and management. Experienced bankers and highly regarded liquidity risk experts share their insights and practical experiences in this book.
Using a multi-country panel of banks, we study whether better capitalized banks experienced higher stock returns during the financial crisis. We differentiate among various types of capital ratios: the Basel risk-adjusted ratio; the leverage ratio; the Tier I and Tier II ratios; and the tangible equity ratio. We find several results: (i) before the crisis, differences in capital did not have much impact on stock returns; (ii) during the crisis, a stronger capital position was associated with better stock market performance, most markedly for larger banks; (iii) the relationship between stock returns and capital is stronger when capital is measured by the leverage ratio rather than the risk-adjusted capital ratio; (iv) higher quality forms of capital, such as Tier 1 capital and tangible common equity, were more relevant.
In these highly competitive times and with so many technological advancements, it is impossible for any industry to remain isolated and untouched by innovations. In this era of digital economy, the banking sector cannot exist and operate without the various digital tools offered by the ever new innovations happening in the field of Artificial Intelligence (AI) and its sub-set technologies. New technologies have enabled incredible progression in the finance industry. Artificial Intelligence (AI) and Machine Learning (ML) have provided the investors and customers with more innovative tools, new types of financial products and a new potential for growth.According to Cathy Bessant (the Chief Operations and Technology Officer, Bank of America), AI is not just a technology discussion. It is also a discussion about data and how it is used and protected. She says, "In a world focused on using AI in new ways, we're focused on using it wisely and responsibly."
Bank Liquidity Creation and Financial Crises delivers a consistent, logical presentation of bank liquidity creation and addresses questions of research and policy interest that can be easily understood by readers with no advanced or specialized industry knowledge. Authors Allen Berger and Christa Bouwman examine ways to measure bank liquidity creation, how much liquidity banks create in different countries, the effects of monetary policy (including interest rate policy, lender of last resort, and quantitative easing), the effects of capital, the effects of regulatory interventions, the effects of bailouts, and much more. They also analyze bank liquidity creation in the US over the past three decades during both normal times and financial crises. Narrowing the gap between the "academic world" (focused on theories) and the "practitioner world" (dedicated to solving real-world problems), this book is a helpful new tool for evaluating a bank's performance over time and comparing it to its peer group. - Explains that bank liquidity creation is a more comprehensive measure of a bank's output than traditional measures and can also be used to measure bank liquidity - Describes how high levels of bank liquidity creation may cause or predict future financial crises - Addresses questions of research and policy interest related to bank liquidity creation around the world and provides links to websites with data and other materials to address these questions - Includes such hot-button topics as the effects of monetary policy (including interest rate policy, lender of last resort, and quantitative easing), the effects of capital, the effects of regulatory interventions, and the effects of bailouts
