- Technology and Invention in Finance
- Financial Markets: Course Introduction
- Risk and Financial Crises
- Portfolio Diversification and Supporting Financial Institutions
- Insurance, the Archetypal Risk Management Institution
- Barron's Criticism, Determinants of Investment Return
- Lecture 7 - Efficient Markets
- Lecture 8 - Theory of Debt, Its Proper Role, Leverage Cycles
- Lecture 9 - Corporate Stocks
- Lecture 10 - Real Estate Finance
- Lecture 11 - Behavioral Finance
- Lecture 12 - Misbehavior, Crises, Regulation and Self Regulation
- Lecture 13 - Overview of Banks
- Lecture 14 - A Brief History of AIG with Maurice "Hank" Greenberg
- Lecture 15 - Forward and Futures Markets
- Lecture 16 - Banking and Regulations in China with Laura Cha
- Lecture 17 - Options Markets
- Lecture 18 - Monetary Policy
- Lecture 19 - Overview of Investment Banking
- Lecture 20 - Professional Money Managers and Their Influence
- Lecture 21 - Exchanges, Brokers, Dealers, Clearinghouses

# Risk and Financial Crises

Professor Shiller introduces basic concepts from probability theory and embeds these concepts into the concrete context of financial crises, with examples from the financial crisis from 2007-2008. Subsequent to a historical narrative of the financial crisis from 2007-2008, he turns to the definition of the expected value and the variance of a random variable, as well as the covariance and the correlation of two random variables.

The concept of independence leads to the law of large numbers, but financial crises show that the assumption of independence can be deceiving, in particular through its impact on the computation of Value at Risk measures. Moreover, he covers regression analysis for financial returns, which leads to the decomposition of a financial asset's risk into idiosyncratic and systematic risk.

Professor Shiller concludes by talking about the prominent assumption that random shocks to the financial economy are normally distributed. Historical stock market patterns, specifically during crises times, establish that outliers occur too frequently to be compatible with the normal distribution.

# This content is for paid members only.

Join our membership for lifelong unlimited access to all our data science learning content and resources.