One of the side effects of building dynamic models is that you learn new things about the system you were modeling. I’m not an economist by training, so while what I’m learning is new to me, I’m not claiming it is new to everyone. Please do keep in mind, though, that an important goal of this modeling work is to help society, most of whom are not experts in economics (or energy or healthcare or …) develop an understanding of how society works and how to make it work better.
This positing at a blog I follow, Franz Dills’s The Eponymous Pickle, pointed to an article Re-thinking Risk Management: Why the Mindset Matters More Than the Model. This got me thinking about how financial firms think about risk.
The Path to the Insight
Since we will be modeling “structured finance” products like Mortgage-Backed Securities (MBS) and Credit Default Swaps (CDS), I need to understand what these are, how they work, and why they are desirable to the parties involved. This led me to the concept of price signals in markets.
An example of a price signal is the price of gas. When it is high, it sends a signal to drivers to drive less in the short term, and consider more efficient vehicles and public transportation in the long term. It is possible for government to send the same signal by a gas tax, even if the price of imported oil is low. The key point is that in a market, signals influence behavior.
Investments have two key signals, price and risk. These are traditionally unified—if an asset is perceived to be riskier, buyers of the asset will demand a higher return to offset the risk (think of “junk bonds”). The risk signal is set by a credit rating agency such as Moody’s, which assigns a rating such as AAA for the safest investments (such as a money market fund) to BBB for a junk bond.
In the article Regulatory Malfeasance and the Financial System Collapse, economist Joseph R Mason, Senior Fellow at the Wharton School , writes that it is important to understand “the terms and triggers of securitizations to recognize perverse incentives apparent in selling the AAA securities but keeping the risk.”
What structured finance products do, whether intentionally or not, is to obscure the risk signal associated with an investment. This occurs because risk is separated out from the underlying investment. When a MBS is insured by a CDS, the risk is apparently moved from the firm holding the MBS to the firm holding the CDS. But the decoupling or risk and return is an illusion, because the MBS and CDS are both linked to the risk of the underlying real asset—the mortgage. If the mortgage fails, the MBS loses value, and the CDS is supposed to pay. But why did the mortgage fail? Because the economy has problems, and these problems also affect the ability of the holder of the CDS to pay when the mortgage fails. So, while the risk signal of the MBS was changed via financial engineering, the underlying risk remained with the MBS.
The Insight
The insight is that financial risk obeys a kind of “conservation of matter” law. That is, “risk can neither be created nor destroyed”. So, while financial engineering can move risk around, it behooves anyone considering purchasing a structured finance product to understand exactly where the risk is being moved to, and to ensure that the destination of the risk is truly unconnected with the structured finance product being purchased. My hunch is that you can't ever guarantee decoupling, and that structured finance is essentially just an exercise in obfuscation. Once we have the model running, I look forward to tracking the flow of risk.
A note on Financial Engineering
The practice of creating structured finance products is called “financial engineering”. I am trained as an engineer, and grew up viewing engineering as synergistically combining elements from a palette of technologies to create a new capability. These new capabilities add value to the economy, because they either solve a problem that has value or enable some new activity or product, which also has value.
Financial engineering, in contrast, seems to only move things (e.g. risk) around without creating new value. One may argue that securitizing debt (e.g. MBS) allowed many more institutions to sell debt to individual people in the form of mortgages and credit cards, which expanded homeownership and consumer spending. But it seems to me that primary motivation behind securitizing debt was to have new financial products to trade, with a commission being made on every trade. Hosting the trading of assets is a great business model, because “the house always wins” in that every trade generates a commission, whether the buyers and sellers win or lose. In other words, securitization of debt doesn't add any real value to the world; it just allows the institutions that facilitate the trades to siphon value out as commissions.
That doesn't sound like engineering to me.
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Price Signals vs. Individual Psychology?
ReplyDeleteIn pondering Ken's discussion of how price signals affect market behavior, another aspect of their influence came to my mind.
Price signals don't exist in a vacuum, after all. The degree to which a price signal affects a given consumer's actions is dependent also on that consumer's evaluation of their own potential as an earner in the context of the current and future economy. For example, reducing the price of gasoline won't lead to as much of an increase in driving among those people who have been recently unemployed for a significant period, even if they are working again now. They are "gun shy" about spending since they are concerned that the past may repeat itself. In aggregate, these effects are measured as the "Consumer Confidence Index".
But in the real world, each person's individual consumer confidence index is different, and developing a model which includes that effect would allow us to investigate the distribution of confidence and its changes over time. While this would be difficult, if not impossible, to accomplish using algebraic models, an agent-based model such those Ken specializes in would be the perfect basis!
I'm personally curious if current conditions of the economy are leading to a bimodal distribution of confidence, wherein the pessimistic people are in effect becoming "trapped" in that state because of the perceived distance (in terms of level of confidence) between their positions and that of the typical optimist is so large.
Hi Ken, been a long time since we chatted.
ReplyDeleteThis is very interesting but I think the key issue is the risk equation. That equation is a moving target, sometimes affected by psychology and sometimes by the eurphoria of economic bubbles. Surely that equation was different during the hay day of sub-prime mortgages but that equation has now changed dramatically. This is a key issue that requires lots of research and the development of a theory of economic bubbles. The mortgage meltdown was one example as well as the dot-com implosion of several years ago. If you can figure that out and embed it into ABM, you have something that is very interesting.
M Wolfe