Category Archives: The Real World

Transaction risk insurance use rises as part of M&A deals

The article referenced below showcases some particularly  interesting and innovative developments in commercial insurance; quoting from this article,

“M&A insurance has changed the way deal professionals allocate risk, using insurance as a tool to bridge the gap on one of the most fundamental issues in any M&A transaction: the potential post-closing erosion of value, either of the consideration received by the seller or the business acquired by the buyer.”

Transaction insurance to cover mergers and acquisitions deal risk continues to rise, according to a new report from Aon, which found “extraordinary growth” in the use of the coverage between 2016 and 2018.

Adverse Selection – definition, examples, and some solutions

During yesterday’s Finance 4335 class meeting, I introduced the topic of adverse selection, and we’ll devote next Tuesday’s class to further discussion of this topic.

Adverse selection is often referred to as the “hidden information” problem. This concept is particularly easy to understand in an insurance market setting; if you are an insurer, you have to be concerned that the worst possible risks are the ones that want to purchase insurance. However, it is important to note that adverse selection occurs in many market settings other than insurance markets. Adverse selection occurs whenever one party to a contract has superior information compared with his or her counter-party. When this occurs, often the party with the information advantage is tempted to take advantage of the uninformed party.

In an insurance setting, adverse selection is an issue whenever insurers know less about the actual risk characteristics of their policyholders than the policyholders themselves. In lending markets, banks have limited information about their clients’ willingness and ability to pay back on their loan commitments. In the used car market, the seller of a used car has more information about the car that is for sale than potential buyers. In the labor market, employers typically know less than the worker does about his or her abilities. In product markets, the product’s manufacturer often knows more about product failure rates than the consumer, and so forth…

The problem with adverse selection is that if left unchecked, it can undermine the ability of firms and consumers to enter into contractual relationships, and in extreme cases, may even give rise to so-called market failures. For example, in the used car market, since the seller has more information than the buyer about the condition of the vehicle, the buyer cannot help but be naturally suspicious concerning product quality. Consequently, he or she may not be willing to pay as much for the car as it is worth (assuming that it is not a “lemon”). Similarly, insurers may be reticent about selling policies to bad risks, banks may be worried about loaning money to poor credit risks, employers may be concerned about hiring poor quality workers, consumers may be worried about buying poor quality products, and so on…

A number of different strategies exist for mitigating adverse selection. In financial services markets, risk classification represents an important strategy. The reason insurers and banks want to know your credit score is because consumers with bad credit not only often lack the willingness and ability to pay their debts, but they also tend to have more accidents than consumers with good credit. Signaling is used in various settings; for example, one solution to the “lemons” problem in the market for used cars is for the seller to “signal” by providing credible third party certification; e.g., by paying for Carfax reports or vehicle inspections by an independent third party. Students “signal” their quality by selecting a high-quality university (e.g., like Baylor! :-)). Here the university provides potential employers with credible third-party certification concerning the quality of human capital. In product markets, if a manufacturer provides a long-term warranty, this may indicate that quality is better than average.

Case studies of how (poorly designed) insurance creates moral hazard

During yesterday’s class meeting, we discussed (among other things) how contract designs and pricing strategies can “fix” the moral hazard that insurance might otherwise create. Insurance is “good” to the extent that it enables firms and individuals to manage the risks that they face. However, we also saw insurance has a potential “dark side.” The dark side is that too much insurance and/or incorrectly priced insurance can create moral hazard by insulating firms and individuals from the financial consequences of their decision-making. Thus, in real world insurance markets, we commonly observe partial rather than full insurance coverage. Partial insurance ensures that policyholders have incentives to mitigate risk. Furthermore, real world insurance markets are characterized by pricing strategies such as loss-sensitive premiums (commonly referred to as “experience rated” premiums), as well as premiums that are contingent upon the extent to which policyholders invest in safety.

In competitively structured private insurance markets, we expect that the market price for insurance will (on average) be greater than or equal to its actuarially fair value. Under normal circumstances, one does not expect to observe negative premium loadings in the real world. Negative premium loadings are incompatible with the survival of a private insurance market, since this would imply that insurers are not able to cover capital costs and would, therefore, have incentives not to supply such a market.

Which brings us to the National Flood Insurance Program (NFIP). The NFIP is a federal government insurance program managed by the Federal Emergency Management Agency (also known as “FEMA”). According to Cato senior fellow Doug Bandow’s blog posting entitled “Congress against Budget Reform: Voting to Hike Subsidies for People Who Build in Flood Plains”,

“…the federal government keeps insurance premiums low for people who choose to build where they otherwise wouldn’t. The Congressional Research Service figured that the government charges about one-third of the market rate for flood insurance. The second cost is environmental: Washington essentially pays participants to build on environmentally-fragile lands that tend to flood.”

Thus, the NFIP provides us with a fascinating case study concerning how subsidized flood insurance exacerbates moral hazard (i.e., makes moral hazard even worse) rather than mitigates moral hazard. It does this by encouraging property owners to take risks (in this case, building on environmentally fragile lands that tend to flood) which they otherwise would not be inclined to take if they had to pay the full expected cost of such risks.

There are many other examples of moral hazard created by insurance subsidies. Consider the case of crop insurance provided to farmers by the U.S. Department of Agriculture.  The effective premium loading on federally provided crop insurance is typically quite negative (often in excess of -60%), thus putting crop insurance on a similar footing to flood insurance in terms of cost compared with actuarially fair value. Just as mis-priced flood insurance effectively encourages property owners to build in flood plains, mis-priced crop insurance incentivizes farmers to cultivate acreage which may not even be particularly fertile.

I could go on (probably for several hundred more pages – there are innumerable other egregious examples which I could cite), but I think I will stop for now…

Empirical evidence of risk aversion in the real world: Small stocks, Large stocks, Government bonds, and Treasury bills, 1926-2017

Although there may be various social contexts in which people stray from risk averse behavior (e.g., the risk loving behavior which is on display whenever people place bets on gambles with unfair odds), in other (more economically consequential settings), it does appear that risk averse behavior is more the rule rather than the exception. Indeed, risk aversion is what motivates people to buy insurance and diversify risk in their asset holdings.

The financial markets provide us with a superb example of risk averse behavior writ large. Historically, here are the long run (1926-2017) compound annual returns on stocks, bonds, and bills that are traded in U.S. financial markets (source: page 9 of

Risk for these various asset classes is lowest for Treasury bills, a bit higher for Government bonds, a bit higher yet for Large stocks, and highest for Small stocks. If you are risk averse, then if one asset has higher risk than another, you are not willing to invest in the riskier asset unless you can reasonably expect that on average, you’ll be compensated for bearing the extra risk in the form of a higher expected return, and it turns out that this is exactly what happens in the real world. If investors were to act in a risk neutral fashion, then the average returns wouldn’t be all that different from each other. Finally if investors were to act in a risk loving fashion, they’d pay more for risky assets than for safe; this would cause risky assets to be bid up in value relative to safe assets, which in turn would imply lower average returns for risky than for safe assets.

How Do Energy Companies Measure the Temperature? Not in Fahrenheit or Celsius

Instead of Fahrenheit or Celsius, a metric called “degree days” is used to capture variability in temperature. The risk management lesson here is that this metric makes it possible to create risk indices which companies can rely upon for pricing and hedging weather-related risks with weather derivatives.

How Hurricane Florence Could Move Insurance Markets

Hurricane Florence provides a particularly timely and compelling case study of the economic consequences of natural catastrophes; specifically, the nexus of direct and indirect effects upon property insurance markets, reinsurance markets, alternative risk markets (e.g., catastrophe bonds), and public policy.

Some hurricanes are worse than others — both for people in the way and the insurance industry that tries to understand storms and put a price on their risks.