The Liquidity Problem

The Traditional View of Liquidity

Within the U.S. economy, the central bank, officially known as the Federal Reserve (or the “Fed” for short), has been seen as the source of banking system liquidity.

The Federal Reserve can alter the supply of money by: changing bank capital requirements, changing interest rates, or by old fashioned money printing. Banks then create liquidity by making loans.

Banks benefit from involvement in the Federal Reserve System in the following ways:

  • The U.S. government insures the deposits held in participating banks; insurance instills confidence and reduces the risk of a “run” on the banks.
  • Banks can borrow money through the Federal Reserve’s discount window.

Participation in the Federal Reserve System is not without its constraints, as capital requirements imposed by the Fed restrict financial leverage and can require banks to raise additional capital.

An Alternative View of Liquidity

The financial crisis, which began in 2008 illustrated that financial vehicles outside the traditional banking system also created liquidity through alternate methods.

These “non-bank” vehicles included hedge funds, collateralized debt obligations, asset backed securities, real estate investment trusts, and other investment instruments.

Non-bank financial institutions differed from banks in the Federal Reserve System, as they rely on financial markets for liquidity, they relied on credit rating agencies to influence investor decisions, and they not insured.

Generically traditional banks are distinguished from these “non-banks” by the fact that traditional banks take deposits from individuals and businesses.

The Minsky Hypothesis

Economist Hyman Minsky created the “Financial Instability Hypothesis”.

The general idea of the hypothesis is that long periods of financial stability will lead to instability at some point.  The reason is that stability leads to excessive investor confidence, which then causes poor lending practices.

Minsky described three types of debt, each with increasing risk:

  1. The hedge unit: This is conservative debt, where the borrower’s income stream supports both interest and principal repayment. A standard amortizing mortgage from a borrower of sound credit quality would fall under the hedge unit.

  2. The speculative unit: This is more risky than the hedge unit; the income stream supports only the interest, but not principal repayment. During the U.S. housing market meltdown, the speculative unit was reflected by interest only mortgages that contained balloon payments at the end of the borrowing term.

  3. The Ponzi unit: This entails even more risk than the speculative unit; the income stream does not even fully support the interest payment. An example from the U.S. housing market crisis would be a negative amortizing mortgage.

Subprime Mortgages – A Free “at the money” Call Option

Generally, by classifying a mortgage as “subprime”, the designation is referring to the credit quality of the borrower.  A borrower below a certain credit rating is considered a subprime borrower.

In the years leading up to the financial crisis, subprime borrowers commonly had little or no equity capital to put down when purchasing housing property.

  • Within the above context, a subprime mortgage situation could be compared to a free at the money call option.

  • If the house went up in value, then the borrower obtained positive equity in the property.  This positive equity position would incent the borrower to make mortgage payments.

  • If the house went down in value below the mortgage, then the “call option” is worthless and there is no incentive for the borrower to continue making payments.

Finance Train Premium
Accelerate your finance career with cutting-edge data skills.
Join Finance Train Premium for unlimited access to a growing library of ebooks, projects and code examples covering financial modeling, data analysis, data science, machine learning, algorithmic trading strategies, and more applied to real-world finance scenarios.
I WANT TO JOIN
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Saylient AI Logo

Accelerate your finance career with cutting-edge data skills.

Join Finance Train Premium for unlimited access to a growing library of ebooks, projects and code examples covering financial modeling, data analysis, data science, machine learning, algorithmic trading strategies, and more applied to real-world finance scenarios.