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4434Orbis: Company Financials
http://www.merlot.org/merlot/viewMaterial.htm?id=1006158
A quick intro to Orbis, a company financials database.Orbis: Building A Query
http://www.merlot.org/merlot/viewMaterial.htm?id=1006160
How to find companies based on your criteria.Lecture 7 - Shakespeare's Merchant of Venice and Collateral, Present Value and the Vocabulary of Finance
http://www.merlot.org/merlot/viewMaterial.htm?id=985667
This video was recorded at YALE - ECON 251 - Financial Theory. While economists didn't have a good theory of interest until Irving Fisher came along, and didn't understand the role of collateral until even later, Shakespeare understood many of these things hundreds of years earlier. The first half of this lecture examines Shakespeare's economic insights in depth, and sees how they sometimes prefigured or even surpassed Irving Fisher's intuitions. The second half of this lecture uses the concept of present value to define and explain some of the basic financial instruments: coupon bonds, annuities, perpetuities, and mortgages.Lecture 13 - Demography and Asset Pricing: Will the Stock Market Decline when the Baby Boomers Retire?
http://www.merlot.org/merlot/viewMaterial.htm?id=985679
This video was recorded at YALE - ECON 251 - Financial Theory. In this lecture, we use the overlapping generations model from the previous class to see, mathematically, how demographic changes can influence interest rates and asset prices. We evaluate Tobin's statement that a perpetually growing population could solve the Social Security problem, and resolve, in a surprising way, a classical argument about the link between birth rates and the level of the stock market. Lastly, we finish by laying some of the philosophical and statistical groundwork for dealing with uncertainty.Lecture 14 - Quantifying Uncertainty and Risk
http://www.merlot.org/merlot/viewMaterial.htm?id=985681
This video was recorded at YALE - ECON 251 - Financial Theory. Until now, the models we've used in this course have focused on the case where everyone can perfectly forecast future economic conditions. Clearly, to understand financial markets, we have to incorporate uncertainty into these models. The first half of this lecture continues reviewing the key statistical concepts that we'll need to be able to think seriously about uncertainty, including expectation, variance, and covariance. We apply these concepts to show how diversification can reduce risk exposure. Next we show how expectations can be iterated through time to rapidly compute conditional expectations: if you think the Yankees have a 60% chance of winning any game against the Dodgers, what are the odds the Yankees will win a seven game series once they are up 2 games to 1? Finally we allow the interest rate, the most important variable in the economy according to Irving Fisher, to be uncertain. We ask whether interest rate uncertainty tends to make a dollar in the distant future more valuable or less valuable.Lecture 16 - Backward Induction and Optimal Stopping Times
http://www.merlot.org/merlot/viewMaterial.htm?id=985685
This video was recorded at YALE - ECON 251 - Financial Theory. 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.Lecture 17 - Callable Bonds and the Mortgage Prepayment Option
http://www.merlot.org/merlot/viewMaterial.htm?id=985687
This video was recorded at YALE - ECON 251 - Financial Theory. This lecture is about optimal exercise strategies for callable bonds, which are bonds bundled with an option that allows the borrower to pay back the loan early, if she chooses. Using backward induction, we calculate the borrower's optimal strategy and the value of the option. As with the simple examples in the previous lecture, the option value turns out to be very large. The most important callable bond is the fixed rate amortizing mortgage; calling a mortgage means prepaying your remaining balance. We examine how high bankers must set the mortgage rate in order to compensate for the prepayment option they give homeowners. Looking at data on mortgage rates we see that mortgage borrowers often fail to prepay optimally.Lecture 18 - Modeling Mortgage Prepayments and Valuing Mortgages
http://www.merlot.org/merlot/viewMaterial.htm?id=985689
This video was recorded at YALE - ECON 251 - Financial Theory. A mortgage involves making a promise, backing it with collateral, and defining a way to dissolve the promise at prearranged terms in case you want to end it by prepaying. The option to prepay, the refinancing option, makes the mortgage much more complicated than a coupon bond, and therefore something that a hedge fund could make money trading. In this lecture we discuss how to build and calibrate a model to forecast prepayments in order to value mortgages. Old fashioned economists still make non-contingent forecasts, like the recent predictions that unemployment would peak at 8%. A model makes contingent forecasts. The old prepayment models fit a curve to historical data estimating how sensitive aggregate prepayments have been to changes in the interest rate. The modern agent based approach to modeling rationalizes behavior at the individual level and allows heterogeneity among individual types. From either kind of model we see that mortgages are very risky securities, even in the absence of default. This raises the question of how investors and banks should hedge them.Lecture 19 - History of the Mortgage Market: A Personal Narrative
http://www.merlot.org/merlot/viewMaterial.htm?id=985691
This video was recorded at YALE - ECON 251 - Financial Theory. Professor Geanakoplos explains how, as a mathematical economist, he became interested in the practical world of mortgage securities, and how he became the Head of Fixed Income Securities at Kidder Peabody, and then one of six founding partners of Ellington Capital Management. During that time Kidder Peabody became the biggest issuer of collateralized mortgage obligations, and Ellington became the biggest mortgage hedge fund. He describes securitization and tranching of mortgage pools, the role of investment banks and hedge funds, and the evolution of the prime and subprime mortgage markets. He also discusses agent based models of prepayments in the mortgage market.Lecture 20 - Dynamic Hedging
http://www.merlot.org/merlot/viewMaterial.htm?id=985693
This video was recorded at YALE - ECON 251 - Financial Theory. Suppose you have a perfect model of contingent mortgage prepayments, like the one built in the previous lecture. You are willing to bet on your prepayment forecasts, but not on which way interest rates will move. Hedging lets you mitigate the extra risk, so that you only have to rely on being right about what you know. The trouble with hedging is that there are so many things that can happen over the 30-year life of a mortgage. Even if interest rates can do only two things each year, in 30 years there are over a billion interest rate scenarios. It would seem impossible to hedge against so many contingencies. The principle of dynamic hedging shows that it is enough to hedge yourself against the two things that can happen next year (which is far less onerous), provided that each following year you adjust the hedge to protect against what might occur one year after that. To illustrate the issue we reconsider the World Series problem from a previous lecture. Suppose you know the Yankees have a 60% chance of beating the Dodgers in each game and that you can bet any amount at 60:40 odds on individual games with other bookies. A naive fan is willing to bet on the Dodgers winning the whole Series at even odds. You have a 71% chance of winning a bet against the fan, but bad luck can cause you to lose anyway. What bets on individual games should you make with the bookies to lock in your expected profit from betting against the fan on the whole Series?