News of LIBOR rigging at Barclays bank and others is one more shock to confidence in banks and the banking system. Coming after the "London Whale" loss at JP Morgan, which cast doubt on the effectiveness of risk controls, LIBOR rigging prompted The Economist magazine to title its article, "The Rotten Heart of Finance."
In this post, we talk about how LIBOR was manipulated, which depends on understanding how it is calculated. Then we'll discuss about some of the implications and possible improvements to LIBOR rates.
How are LIBOR rates calculated?
U.S. LIBOR rates are calculated by asking 18 major London banks, "At what rate could you borrow funds, were you to do so by asking for and then accepting inter-bank offers in a reasonable market size just prior to 11 a.m.?" After that, the highest and lowest four quotes (eight in total) are disregarded and the remaining numbers averaged together to form that day's LIBOR quote.
This method is designed to make LIBOR less sensitive to any one bank's condition by removing outliers. Overall, it is a logical and sensible approach from a purely statistical point of view.
There are at least three problems with this method in practice, however. They are, in increasing order of seriousness:
- Rates are self-reported by banks, with no firm connection to independently observable market transactions, contracts, or rates.
- Reported rates are for hypothetical transactions; those reporting them have no obligation actually to accept or transact at the rates they report, or even be objective or unbiased in stating them.
- The banks contributing to the survey will often have a material interest in influencing whether rates move upwards or downwards on a particular day, or on knowing which direction they are likely to move.
How were LIBOR rates manipulated?
Over time, some banks realized that by deliberately quoting excessively high or low borrowing cost estimates (i.e., rates), they could ensure that their quote would fall in the "excluded" group of rates. If the number were extreme enough, this would force a previously excluded number into the average. That number would generally pull the average in the same direction as the biased quote, even if the quote itself did not end up being used in the average. Quoting an unrealistically low number would push a higher-but-still-low number into the average, dragging the average down; and vice versa for high numbers.
Part of the rigging scandal is revealed in emails from traders at Barclays bank to individuals responsible for responding to the LIBOR survey. The emails ask the desks to reduce the reported borrowing rate estimates in order to push the LIBOR average lower and increase trading portfolio profits. Often, the difference may have been on the order of just one or two basis points (1 bp = 0.01%), but on large enough portfolios or trading positions, differences of even one basis point could amount to hundreds of thousands of dollars, or more.
Some thoughts on the rigging mechanism
A few interesting points stand out on this process. First, it is interesting that most emails sought to lower LIBOR rates, rather than raise them. For the average citizen, lower LIBOR figures translate into lower credit card costs, lower mortgage reset rates, and lower borrowing costs in general. A few basis points is scarcely a noticeable interest rate change to the average borrower, but the direction of the move actually benefited much the borrowing public and those whose businesses revolve around the availability of credit.
The losers in this particular event were lenders, both Barclays bank (as a lender) itself, and any other bank lending with rates tied to LIBOR. Basically, bank traders were making money by forcing lenders to price their loans off of an artificially low baseline. Lenders and providers of capital were effectively being asked to shoulder risks with less compensation. Traders essentially booked profits by grabbing an extra few pennies from millions of savers and creditors.
But it also shows that short-term traders had effectively been given priority over all other bank functions. Normally, the idea is that banks must regulate their own interest rate and report it accurately because if they do not charge enough interest to compensate for risks, their own balance sheet and even their very existence is in jeopardy. All of that went out the window: Jacking up trading profits was all that mattered. Trading requests to push down LIBOR rates had an effect, but where were the loan officers complaining that Barclays (and other banks) were not receiving enough compensation for the risks of lending?
And there is no reason that just because the current rigging scheme pushed down interest rates future rigging would not push them artificially high. We know that one cannot push rates down forever: There is a boundary at zero, and eventually a reporting officer would need to rejoin the averages if they are to be taken seriously.
Finally, it could be that the incentives to push numbers higher may ultimately be balanced out by the incentive to push numbers lower. That is what markets are supposed to do. But there is still a problem that, in the short term, if traders know that they can affect tomorrow's number, even if only probabilistically, they can take positions to profit from it today. The next day, they try to immunize themselves from any reversion to reality, at a cost to their counterparty. Even if it turns out to be impossible to push LIBOR around significantly over the long term, the fact that the trading desks can manipulate prices in the short term is problematic for anyone who wants to trade.
What this all means
At one level, the events show that the LIBOR process works reasonably well. If the process had simply averaged the results of all 18 banks, then extreme quotations would have had an even greater effect on final LIBOR rates and the incentive to "quote stuff" would have been more appealing from the start. The technique of disregarding possible outliers reduced the impact of manipulations without being able to eliminate them.
The real weakness in the process comes from the fact that banks are in no way bound to their quotes and that the end figure directly affects their own profitability. It would be better to have either independently observable data, such as the actual interest rates paid for short-term money, or some number that a bank would have to honor if actually offered.
It might also help also to ask banks what rate would require from another bank wanting to borrow from them, rather than to ask what rate they think they can receive from another bank. If a bank were forced to lend at a rate that it quoted, this would create real consequences for the kind of misquoting that happened here.
Neither of these solutions is foolproof, and the main damage is the public sense that the people in charge of things financial will go to any extreme to manipulate numbers to their own benefit. People who work day-in and day-out in the industry might find it odd to think that someone would not manipulate or lie if they could get away with it, but they seem out of touch with the fact that the rest of the world sees this as criminal behavior.
Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
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