- Why Finance?
- Utilities, Endowments, and Equilibrium
- Computing Equilibrium
- Efficiency, Assets, and Time
- Present Value Prices and the Real Rate of Interest
- Irving Fisher's Impatience Theory of Interest
- Shakespeare's Merchant of Venice and Collateral, Present Value and the Vocabulary of Finance
- How a Long-Lived Institution Figures an Annual Budget Yield
- Yield Curve Arbitrage
- Dynamic Present Value
- Financial Implications of US Social Security System
- Overlapping Generations Models of the Economy
- Will the Stock Market Decline when the Baby Boomers Retire?
- Quantifying Uncertainty and Risk
- Uncertainty and the Rational Expectations Hypothesis
- Backward Induction and Optimal Stopping Times
- Callable Bonds and the Mortgage Prepayment Option
- Modeling Mortgage Prepayments and Valuing Mortgages
- Dynamic Hedging
- Dynamic Hedging and Average Life
- Risk Aversion and CAPM
- The Mutual Fund Theorem and Covariance Pricing Theorems
- Risk, Return, and Social Security
- Leverage Cycle and the Subprime Mortgage Crisis
- Shadow Banking: Parallel and Growing?
Backward Induction and Optimal Stopping Times
In the first part of the lecture we wrap up the previous discussion of implied default probabilities, showing how to calculate them quickly by using the same duality trick we used to compute forward interest rates, and showing how to interpret them as spreads in the forward rates. The main part of the lecture focuses on the powerful tool of backward induction, once used in the early 1900s by the mathematician Zermelo to prove the existence of an optimal strategy in chess. We explore its application in a series of optimal stopping problems, starting with examples quite distant from economics such as how to decide when it is time to stop dating and get married. In each case we find that the option to continue is surprisingly valuable.
Source: Open Yale Courses
Data Science in Finance: 9-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 includes PDFs, explanations, instructions, data files, and R code for all examples.
Get the Bundle for $39 (Regular $57)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.
