The questions financial advisers ask clients to get at the answer actually measure something completely different—often leading to misguided investment strategies.
This WSJ article provides a fairly comprehensive look at the financial implications for #Harvey for small business. What’s particularly disconcerting is that NFIP is already for all intents and purposes technically insolvent (current debt to the US Treasury stands at around $25 billion) and Congress is supposed to reauthorize funding for the program’s next five years by September 30. On the lighter side of things, it’s fun to see a couple of academic colleagues’ names in print in this article; specifically, Erwann Michel-Kerjan of the Organization for Economic Cooperation and Development Board on Financial Management of Catastrophes and Ben Collier, who is a faculty member at Temple University’s Fox School of Business.
Hurricane will strain a National Flood Insurance Program out of step with needs of small businesses in era of extreme weather.
This WSJ article from last spring addresses how to measure uncertainty and also explains the subtle, yet important differences between risk and uncertainty. Risk reflects the “known unknowns,” or the uncertainties about which one can make probabilistic inferences. Ambiguity (AKA “Knightian” uncertainty; see https://en.wikipedia.org/wiki/Frank_Knight) reflects the “unknown unknowns,” where the probabilities themselves are a mystery.
Good article from Bloomberg on how catastrophe (AKA “cat”) bonds are a unique asset class for investors and how such bonds disrupt traditional reinsurance markets. For a broader perspective of these topics, also see the August 2016 WSJ article entitled “The Insurance Industry Has Been Turned Upside Down by Catastrophe Bonds” and my blog posting entitled “Cat Bonds“.
Cat bonds represent a form of securitization in which risk is transferred to investors rather than insurers or reinsurers. Typically, an insurer or reinsurer will issue a cat bond to investors such as life insurers, hedge funds and pension funds. The bonds are structured similarly to traditional bonds, with an important exception: if a pre-specified event such as a hurricane occurs prior to the maturity of the bonds, then investors risk losing accrued interest and/or the principal value of the bonds. This is why these bonds are falling in price – investors expect that the payment triggers tied to storms like #Harvey will reduce the payments received by holders of these bonds.
This is an excellent article on how asset prices impound political risks, and the role of so-called “prediction markets” in assessing political event probabilities (in this case, the likelihood of the U.S. defaulting on its debt).
During the past year, Financial Times writer Tim Harford has presented an economic history documentary radio and podcast series called 50 Things That Made the Modern Economy. While I recommend listening to the entire series of podcasts, I would like to call your attention to Mr. Harford’s episode on the topic of insurance, which I link below. This 9-minute long podcast lays out the history of the development of the various institutions which exist today for the sharing and trading of risk, including markets for financial derivatives as well as for insurance. Here’s the description of this podcast:
“Legally and culturally, there’s a clear distinction between gambling and insurance. Economically, the difference is not so easy to see. Both the gambler and the insurer agree that money will change hands depending on what transpires in some unknowable future. Today the biggest insurance market of all – financial derivatives – blurs the line between insuring and gambling more than ever. Tim Harford tells the story of insurance; an idea as old as gambling but one which is fundamental to the way the modern economy works.”
Besides going over the syllabus during the first day of class on Tuesday, August 22, we will also discuss a “real world” example of financial risk. Specifically, we will look at the relationship between short-term stock market volatility (as indicated by the CBOE Volatility Index (VIX)) and returns (as indicated by the SP500 stock market index).
As indicated by this graph from page 25 of next Tuesday’s lecture note, daily percentage changes on closing prices for VIX and the SP500 are strongly negatively correlated. In the graph above, the y-axis variable is the daily return on the SP500, whereas the x-axis variable is the daily return on the VIX. The blue points represent 6,959 daily observations on these two variables, spanning the time period from January 2, 1990 through August 11, 2017. When we fit a regression line through this scatter diagram, we obtain the following equation:
where corresponds to the daily return on the SP500 index and corresponds to the daily return on the VIX index. The slope of this line (-0.1198) indicates that on average, daily VIX returns during this time period were inversely related to the daily return on the SP500; i.e., when volatility as measured by VIX went down (up), then the stock market return as indicated by SP500 typically went up (down). Nearly half of the variation in the stock market return during this time period (specifically, 49.5%) can be statistically “explained” by changes in volatility, and the correlation between and comes out to -0.703. While a correlation of -0.703 does not imply that and will always move in opposite directions, it does indicate that this will be the case more often than not. Indeed, closing daily returns on and during this period moved inversely 78% of the time.
You can see how the relationship between the SP500 and VIX evolves prospectively by entering http://finance.yahoo.com/quotes/^GSPC,^VIX into your web browser’s address field.
Equations (2), (3), and (7) play particularly important roles in Finance 4335!
I highly recommend this Freakonomics podcast (and transcript) about passive versus actively managed investment strategies. It provides historical context for the development of some of the most important ideas in finance (e.g., the efficient market hypothesis) and the implications of these ideas for investing in the long run. Along the way, you get to “virtually” meet with many of the best, brightest and most influential academic and professional finance thinkers who played important roles in shaping this history.
Prior to listening to this podcast, I was not aware of how a quip in a 1974 Journal of Portfolio Management article authored by the MIT economist Paul Samuelson inspired Vanguard founder Jack Bogle to launch the world’s first index fund in late 1975. Samuelson suggested that, “at the least, some large foundation should set up an in-house portfolio that tracks the S&P 500 Index — if only for the purpose of setting up a naive model against which their in-house gunslingers can measure their prowess.” (source: “Challenge to Judgment”, available from http://www.iijournals.com/doi/abs/10.3905/jpm.1974.408496).
It’s hard enough to save for a house, tuition, or retirement. So why are we willing to pay big fees for subpar investment returns? Enter the low-cost index fund.
From page 1 of today’s Wall Street Journal – how automation is increasingly (and in many cases, adversely) affecting the livelihoods of financial advisors.
Services that use algorithms to generate investment advice, deliver it online and charge low fees are pressuring the traditional advisory business. The shift has big implications for financial firms that count on advice as a source of stable profits, as well as for rivals trying to build new businesses at lower prices. It also could mean millions in annual savings for consumers and could expand the overall market for advice.