Prof. Jayanth R. Varma's Financial Markets Blog

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Prof. Jayanth R. Varma's Financial Markets Blog, A Blog on Financial Markets and Their Regulation

© Prof. Jayanth R. Varma
jrvarma@iima.ac.in

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Sun, 26 Aug 2012

Structured by cows or by foxes?

An Instant Message Dialog in which a rating agency employee claimed that “it could be structured by cows and we would rate it ” has been repeatedly quoted as evidence of the failures of the rating agencies in rating complex structured products in the build up to the global financial crisis. It also finds mention in a ruling earlier this month by the New York District Court allowing a case against a rating agency to go to trial (h/t FT Alphaville).

I find this puzzling because the least dangerous structured products to rate are those designed by incompetent simpletons. These would more likely correspond to the random samples to which statistical modelling is easiest to apply. The hardest instruments to rate are those put together by cunning foxes rather than by dumb cows. The cunning foxes are likely to design instruments with an intent to game the rating agency models. Model errors that may be harmless in the context of randomly designed pools could be disastrous when the pool is designed to include the worst securities that could scrape through the rating agency’s models.

That the rating agency employees did not realize this is strange. However, the fact that after years of being exposed to Abacus and Magnetar, many commentators do not seem to realize this is even more puzzling.

Posted at 19:57 on Sun, 26 Aug 2012     View/Post Comments (0)     permanent link


Sun, 19 Aug 2012

Corporate Hedging and Distorted Benchmarks

I wrote the following short piece on the subject of “Corporate Hedging and Distorted Benchmarks” for the magazine CFO Connect.

Background

The ongoing investigations into the manipulation of Libor fixing have highlighted the possibility that important benchmarks underlying corporate hedges may be manipulated by large players with the possible tacit acquiescence of the global regulators. Similarly, recent developments in key crude oil benchmarks (Brent and WTI) have demonstrated that these benchmarks can be distorted by factors that could not have been anticipated a few years ago. How does this affect corporate risk management? What can corporate risk managers do to make their hedging programmes more resilient?

There are some corporate hedges that are completely unaffected by the distortion or manipulation of benchmarks. This comfortable situation arises when the hedge is designed in such a way that the benchmark in question is completely eliminated from the all-in hedged cost. For example, consider the following:

The crucial feature of the above example is that the hedge has no “basis risk” at all because the hedging instruments exactly matches the risk exposure and the risk is neatly cancelled out (Libor - Libor = 0). Not all real life hedges are so neat and simple – “basis risk” is quite common.

Consider another example which illustrates the problem:

Libor manipulation

Libor fixing methodology

Libor stands for the London Inter Bank Offer Rate – the rate at which the large banks are able to borrow on an unsecured basis for short maturities. The official definition is that Libor* is: “The rate at which an individual contributor panel bank could borrow funds, were it to do so by asking for and then accepting interbank offers in reasonable market size, just prior to 11.00am London time.”

There are two problems with this benchmark. First, as the definition makes very clear, Libor is not the rate at which a bank has actually borrowed – it is the rate at which it could borrow funds. Second, unlike say a stock market, where all trades take place in public and transaction prices are known to all, the interbank borrowing is a bilateral market about which there is very little transparency. Therefore, if a bank says that it could borrow at 2.45%, it is not easy for anybody to verify whether this is a reasonable estimate at all.

The Libor computation tries to ameliorate this problem by polling several banks, dropping the bottom 25% and the top 25% of all quotes and averaging the central 50%. If one rogue bank submits an unreasonable quote, it is likely to fall in the top or bottom 25% which are dropped and therefore the final average may not be contaminated by this rogue quote.

The Libor computation has one final problem which people did not worry about in the good old days, but is probably very important in retrospect. The entire Libor computation methodology and process are managed by the British Bankers Association and not by an official body independent of the banks who are being polled.

Pre-crisis manipulation

Evidence that has become available in recent weeks indicates that prior to the global financial crisis, some banks were trying to manipulate Libor to suit their own exposures. Banks are large players in interest rate swaps and other derivative markets based on Libor. The British Bankers Association was of course aware of this possibility and they stipulated that “The rates must be submitted by members of staff at a bank with primary responsibility for management of a bank’s cash, rather than a bank’s derivative book.”

When regulators in the US and the UK examined all the internal emails as part of their investigation, they found that the derivative traders were routinely requesting the “submitters” to submit false quotes designed to suit the positions of those traders. The submitters were routinely complying with these requests.

In a few cases, requests were coming from traders at other banks and these were also being accommodated. Such collusion between banks would of course imply that the simple expedient of dropping the top and bottom quartiles of quotes would no longer be sufficient to prevent the average itself from being manipulated.

Manipulation during the crisis

The other evidence that has become available is about the period during the global financial crisis, when people were scared about the solvency of banks and were unwilling to lend to weak banks. During this period, it appears that most banks were systematically under reporting the rates at which they could borrow.

Comparison with the Credit Default Swap (CDS) market which measures the credit worthiness of banks suggests that Libor might have been understated by several percentage points.

It also appears that the Bank of England and the Federal Reserve Board in the US were aware of this and it is suggested that they tacitly approved of this practice. Regulators apparently feared that if the true borrowing cost of banks were widely known, that could add to the panic in the markets.

Example of Failed Libor Hedges

The case of US municipalities provides a very interesting example of how hedges based on Libor can go very badly wrong when the underlying benchmark is distorted or manipulated.

US municipalities traditionally borrowed using auction rate securities. The interest rate was a floating rate, but instead of being set as a spread over Libor, it was determined by periodic auctions. Historically, these auction rates (adjusted for the tax free status of municipal bonds) tended to be very close to Libor. It was common for them to hedge their interest rate risk by using interest rate swaps based on Libor. In normal times, this hedge worked quite well.

During the crisis however, the municipalities faced very steep borrowing rates in their auction rate securities (some auctions actually failed). This was partly driven by the general lack of liquidity in the market and partly by perceived risk of insolvency of some municipalities. Of course, the banks also faced similar perceived risk of insolvency, but because of the manipulation, the reported Libor did not reflect the same degree of stress.

As a result, the floating rate (Libor) that the municipalities received on their swaps was much lower than the floating rate (auction rates) that they paid on their borrowings. The actual borrowing cost turned out to be far in excess of the fixed rate that they thought they had locked in through the hedges.

This is a dramatic example of how a “basis risk” which was regarded as modest and manageable during normal times can become life threatening when the underlying benchmarks are distorted.

Crude oil benchmark distortion

Almost all crude oil trading in the world (both the physical market and the derivative markets) is based on a handful of benchmarks of which the two most prominent are Brent and WTI (West Texas Intermediate). As is to be expected, WTI used to be the dominant benchmark in the US while Brent was the benchmark of choice elsewhere in the world. Based on the quality of the crude (for example, the sulphur content), the crude from a specific oilfield might trade at a premium or discount to Brent or WTI. A long term supply contract between an oil producer and an importer might therefore specify the price as simply Brent + 3$ or Brent - 1$.

In recent years, the emergence of shale oil has drastically altered the supply-demand imbalances within the US. The Cushing region on which the WTI benchmark is based has become an oil surplus region and WTI prices have become artificially depressed. This position is expected to be solved as new pipelines are built and existing pipelines are modified to run in the opposite direction. In the meantime, retail gasoline prices in the US appear to have completely decoupled from WTI prices and seem to be much more closely aligned to Brent prices.

At the same time, Brent has been affected by declining production in the North Sea oilfields on which this benchmark is based. The short supply of Brent has led to a rise in prices to the extent that Brent now trades at a premium to WTI though historically, WTI was more expensive because of its superior quality.

A lot of oil price hedgers have struggled to cope with the unexpected blow out of “basis risk” due to the historically unprecedented distortion of the two principal benchmarks. To make matters worse, the methodology underlying crude oil benchmarks suffers from the same infirmities as Libor possibly on a larger scale. The markets depend on prices reported by some private agencies like Platt who are completely unregulated.

Broader lessons for corporate hedging

Some of us are fond of joking that much of what passes for hedging is actually speculating on the “basis”. Like all good jokes, this joke too has some grain of truth in it. For example, US municipalities were to some extent taking a speculative position that their borrowing cost would not materially exceed Libor on a tax adjusted basis. Their only real cause for complaint is that Libor was manipulated and did not reflect free market outcomes.

In reality, however, “basis risk” is impossible to eliminate completely. Liquid derivative markets must perforce be based on liquid benchmarks, and a specific company’s costs are unlikely to exactly mirror these benchmarks. Moreover, a hedge with significant “basis risk” is likely to be much less risky than a completely unhedged position. Thus even an imperfect hedge is risk reducing and can not fairly be described as speculative risk taking.

The exception is when hedging is used to justify high levels of leverage. Many of the problems that banks have faced during and after the crisis were due to this. A mistaken belief that “basis risk” is negligible leads to the assumption that the hedged position is practically risk free and can be supported by astronomical levels of leverage. Even modest movements in the “basis” can then wipe out the capital and expose the bank to risk of insolvency.

Outside of finance, some manufacturing companies might be making similar mistakes. By underestimating the basis risk, they may be emboldened to adopt risky financial and operating policies that they might not have chosen if they were fully aware of the “basis risk”. These are the companies that can be truly described as speculating on the “basis”.

Posted at 20:12 on Sun, 19 Aug 2012     View/Post Comments (2)     permanent link


Wed, 15 Aug 2012

Anchoring bias as a regulatory tool

The anchoring bias is a well known phenomenon in behavioural finance. As Tversky and Kahneman described it long ago(Amos Tversky and Daniel Kahneman (1974), “Judgment under Uncertainty: Heuristics and Biases”, Science, New Series, 185(4157), pp. 1124-1131):

In many situations,people make estimates by starting from an initial value that is adjusted to yield the final answer. ... adjustments are typically insufficient. That is, different starting points yield different estimates, which are biased toward the initial values. We call this phenomenon anchoring.

Milind Kulkarni from FinIQ, a leading structured products solution provider gave me some information on an interesting regulatory measure by the Central Bank of Taiwan that exploits this behavioural bias to protect retail investors. Though he could not find an official English language text of the regulation, his colleague was able to provide a translation of the Chinese text:

When a bank buys an option from the client (to create yield enhancement) which is collateralized by client’s deposit which happens in to be the call currency with matching notional amount, in the event of the option exercise by the bank the client’s deposit (in call currency) will be retained (bought) by the bank and the alternate (put) currency will be repaid to the client at the strike rate, leading to potential capital loss to the client, such loss should not be more than 30% of the capital at any cost.

This means that the bank must sell a 70% out of the money call option back to the client to create such an airbag type protection against extreme capital loss. In short client sells a regular near ATM call option to the bank and buys back a deep OTM call option from the bank.

The interesting part of this regulation is that it does not rule out short term toxic products in which the retail investor’s annualized rate of return is hugely negative. If you lose 30% every month, you can lose practically everything pretty quickly. Even products that have a maturity of several months or even a year do not need to produce potential losses of 30% to create meaningful yield enhancement. On the other end of the scale, some of the most toxic products do not produce capital losses at all. Consider for example, some of the highly toxic principal protected Power Reverse Dual Currency (PRDC) notes that suddenly became 30 year near-zero coupon bonds when the yen moved sharply during the global financial crisis. The present value loss can be huge even if the principal is fully protected.

The practical effect of the Taiwanese regulation is not therefore so much economic as behavioural. When the product is structured with a 30% airbag, the structure draws the investor’s attention to the potential loss of 30%. Of course, investors know that the 30% loss is extremely unlikely, but 30% is now the anchor from which an adjustment is made to estimate the likely loss. This probably leads to an overestimate of the true loss. In the absence of the airbag, the bank probably tries to deflect the investor’s attention away from possible losses. The smart investor would of course take the bank’s sales pitch with a pinch of salt. But now the anchor is zero loss and insufficient adjustment from this anchor leads to an underestimate of potential losses.

Posted at 21:22 on Wed, 15 Aug 2012     View/Post Comments (1)     permanent link


Fri, 10 Aug 2012

Minimum balance at risk for all safe assets

Last month, the Federal Reserve Bank of New York published a staff report with a very interesting proposal to reduce the systemic risk of runs on money market mutual funds (Patrick E. McCabe, Marco Cipriani, Michael Holscher and Antoine Martin, “The Minimum Balance at Risk: A Proposal to Mitigate the Systemic Risks Posed by Money Market Funds”, Federal Reserve Bank of New York, Staff Report No. 564, July 2012).

I found the proposal very innovative and my only quibble with the proposal is that I see no need at all to limit the idea to just money market mutual funds. I think that the same idea can be applied to bank deposits, liquid mutual funds and many other pools that offer high levels of liquidity.

The proposal is that when an investor redeems his or her investment, a small percentage (say 3-5%) of the investment is held back for a short period (say 30 days). If losses are detected at the fund during this period, the balance held back from the redeeming investor is available to absorb the losses. McCabe and his co-authors show that it is possible to design the loss allocation mechanism in such a way that runs on the fund are discouraged without eliminating market discipline. A fund that pursues risky investment strategies would see redemption from rational investors who anticipate losses in the long term (beyond 30 days). But investors who did not redeem before the losses are revealed do not gain anything by redeeming at the last minute. This eliminates panic runs and allows orderly liquidation.

I think this idea could be extended to bank deposits and many other savings vehicles. All “safe assets” or “informationally insensitive assets” to use Gorton’s phrase could be subject to this rule to prevent disorderly runs without requiring taxpayer bailouts.

The authors themselves suggest that small balances could be exempted from some of the subordination requirements and clearly insured deposits do not need to be subject to the minimum balance at risk requirement. The major impact of the proposal would be on large investors, and I do not believe that large investors have any god given right to safe and liquid assets. In fact, society can make such assets available to them only by imposing losses on the taxpayer.

Pozsar and Singh have pointed out that:

Asset managers do not just invest long-term, but also have a large demand for money (or more precisely, money-market instruments). ... The money demand aspect of the asset management complex ... involves massive volumes of reverse maturity transformation, whereby significant portions of long-term savings are transformed into short-term savings. It is due to portfolio allocation decisions, the peculiarities of modern portfolio management and the routine lending of securities for use as collateral. This reverse maturity transformation occurs in spite of the long-term investment horizon of the households whose funds are being managed. This reverse maturity transformation is the dominant source of marginal demand for money-type instruments in the financial system.

If the minimum balance at risk leads to a re-engineering of the asset management industry to reduce the demand for safe and liquid assets, I think that would be a good thing.

Posted at 17:01 on Fri, 10 Aug 2012     View/Post Comments (0)     permanent link