Lecture 13 - Overview of Banks

Banks are among our enduring of financial institutions. Their survival in so many different historical periods is testimony to their importance. Professor Shiller traces the origins of interest rates from Sumeria in 2000 BC, to ancient Greece and Rome, up to the Song Dynasty in China between the 10th and the 12th century.

Subsequently, he looks at banking in Italy during the Renaissance and at the goldsmith bankers in 16th and 17th century England. Banks have survived so long because they solve adverse selection and moral hazard problems. Additionally, he covers Douglas Diamond's and Philip Dybvig's model, which does not only analyze the banks' role for liquidity provision, but also reveals the possibility of bank runs. This leads Professor Shiller to deposit insurance as a means to prevent bank runs. He discusses the Federal Deposit Insurance Corporation as well as the Federal Savings and Loans Insurance Corporation, together with the role that the latter played during the savings and loan crisis of the 1980s.

The necessity to regulate banks in the presence of deposit insurance results in a discussion of the role of the Basel commission and an explicit calculation to illustrate the core principles of Basel III. At the end, Professor Shiller provides an overview of financial crises since the beginning of the 1990s, with the Mexican crisis of 1994-1995, and the Asian crisis of 1997.

1. Introduction
2. Basic Principles of Banking
3. The Beginnings of Banking: Types of Banks
4. Theory of Banks: Liquidity, Adverse Selection, Moral Hazard
5. Bank Runs, Deposit Insurance and Maintaining Confidence
6. Bank Regulation: Risk-Weighted Assets and Basel Agreements
7. Common Equity Requirements and Its Critics
8. Recent International Bank Crises

https://www.youtube.com/watch?v=1mDL1fKEVZM

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.