Last week the OTC swaps market took a big step towards the creation of standardized interest rate swaps. Pushed by the buy-side, ISDA developed a “Market Agreed Coupon” or MAC contract with common, pre-agreed terms. From the ISDA press release:

The MAC confirmation features a range of pre-set terms in such areas as start and end dates, payment dates, fixed coupons, currencies and maturities. It is anticipated that coupons in the contract will be based on the three- or six-month forward curve and rounded to the nearest 25 basis point increments. Effective dates will be IMM dates, which are the third Wednesday of March, June, September and December. The initial currencies covered include the USD, EUR, GBP, JPY, CAD and AUD. Maturities will be 1, 2, 3, 5, 7, 10, 15, 20 and 30 years.

This is good for end-users. Dealers have long used superfluous customization as a tool to blunt competition and maintain margins. Creating a subset of contracts with standardized terms will make the interest rate swap market more efficient in many ways.

Some in the industry worry this just feeds the trend to futurization of swaps:

“It’s quite speculative to try to figure how this will turn out, but on the one hand a more standardised product is presented as more homogeneous, which is good for OTC markets, while on the other, you could argue the more a product is standardised, the less differentiated it is from futures and ultimately could lose out to straight futures activity,” says one New York-based rates trader. “I think there is a fear that this standardisation process creates a much easier path towards futurisation. You could argue this is one step closer towards promoting the success of swap future contracts.” (RISK magazine, subscr. required)

But that ship had already sailed. The G20 specifically rejected the old model of faux customization, and mandated standardization in support of improved transparency and clearing. Whether standardization happens within the OTC swaps space, or via futurization is a detail.

In a recent paper, the economists Claude B. Erb and Campbell R. Harvey present strong evidence that the gold market was severely overbought. The increase in gold prices did not represent a change in the trend of inflation. As the chart indicates, even with the sell-off, the price of gold is still high and has a long ways to fall to get back to the “golden constant” that gold-standard advocates cite as proof that the dollar should be pegged to gold.

Bartlett thinks this picture proves mean reversion. But it doesn’t. More importantly, he thinks that the authors of the paper with this figure claim this picture proves mean reversion. But they never actually take a stand. The paper doesn’t really test the proposition. In fact, the paper is careful not to take a stand, and never to clearly assert anything one way or another.

The paper is a nice talky piece that can be used for a fun conversation with potential investment clients who demand to talk about gold. The important thing is to touch on all the popular angles, whether or not you put any stock in them. “You want to talk about mean reversion? Here, let me show you a graph that suggests mean reversion.”

There is lots of statistical evidence that the gold price does not mean revert. In particular, there is lots of evidence from futures prices that there is no predictable reversion.

Those people who want to claim otherwise have a minimal burden of proof to meet before we spend too much time listening. Maybe the gold price does mean revert. But show me some data with some real analysis. And be clear about what’s what in the analysis. Smooth talk is a waste of everyone’s time.

Does JP Morgan’s use derivatives to make prop trade bets on interest rates. This picture suggests ‘yes.’

Let me explain.

Public attention is focused on the disastrous trades at JP Morgan’s Chief Investment Office, aka the London Whale’s trades. JP Morgan’s management claims the trades were a hedge of its loan portfolio. Others argue the trades were a classic example of proprietary trading, a speculative bet quite the opposite of hedging. The debate matters because it speaks to whether this type of operation will be legal in the future when the Volcker Rule, passed as a part of the Dodd-Frank financial reform, goes into effect. Moreover, understanding how to  distinguish between hedging and speculation is very important for all companies.

Claims and counterclaims. Will we ever be able to settle it?

Yes, we can. Whether some derivative trades are a hedge is something that can be measured. Either the trades offset the risk in another line of business, or they don’t. That’s measurable. It may not be simple to measure, and there may be plenty of room for arguments on the details. But that caveat applies to lots of things that are measurable.

The figure above compares the risk on JP Morgan’s derivatives portfolio over the years against the risk on JP Morgan’s traditional deposit taking and lending line of business. What we see does not look like hedging. Quite often the derivatives risk adds to the risk from the traditional deposit taking and lending line of business. That’s not hedging.

The erratic nature of the dotted lines is especially interesting. It is emblematic of proprietary trading. Sometimes the company has a ‘view’ or forecast that rates are going to move one direction. So it opens up a derivatives position to profit from that movement. Other times the company has the other ‘view’ or forecast, anticipating that rates are going to move in another direction. Then it opens up the opposite derivatives position to profit from that movement. Hedging doesn’t look like that, but prop trading does.

This is exactly the type of quantitative evidence that can settle the claims and counterclaims. Both hedging and prop trading possess ‘fingerprints’ betraying their presence. And the fingerprints are different. Instead of endless bickering back and forth, we should turn to the data.

I first saw the figure earlier this week in a seminar in which Monika Piazzesi of Stanford University presented her research on “Banks’ Risk Exposures.” It’s very interesting work.

Here’s a little more background on how to read the figure. There are 3 sets of lines showing JP Morgan’s interest rate risk exposure on 3 different parts of its operations: (i) the traditional deposit taking and lending business, (ii) the securities trading portfolio, and (iii) the derivatives portfolio. Each of these parts of its operations is exposed to movements in interest rates and credit spreads that produce gains or losses for the business. These exposures can be made comparable a number of different ways. These authors choose to translate each line of business into its equivalent portfolio of a pair of benchmark securities. For example, the solid green and red lines show the exposure on its traditional deposit and lending business. The red line is the position in cash and the green line is the position in a 5-year zero coupon bond. The fact that the green line sits in positive territory, while the red line sits in negative territory expresses the usual risk exposure of the traditional banking business: borrowing short maturities and lending long maturities.

The dotted lines show JP Morgan’s interest rate risk exposure on its derivatives business. If the derivatives portfolio hedged the bank’s traditional deposit taking and lending business, then the red dotted line should lie in the positive territory, while the green dotted line should lie in the negative territory. That’s not what the figure shows. The dotted lines are very erratic. Often times the red dotted line is in the negative territory and the green dotted line is in the positive territory. In those times, the derivative portfolio is increasing JP Morgan’s interest rate exposure. That’s why I say the figure is not consistent with hedging.

I write this post because the figure is a powerful graphic that nicely illustrates the type of quantitative analysis we should look to in order to answer the question of whether the derivative trades in JP Morgan’s CIO are a hedge or are a prop trading speculation.

Having used Professor Piazzesi’s figure to illustrate that point, it is appropriate now to mention some important provisos.

First, Professor Piazzesi’s and her co-authors’ work is still in progress. They are developing a tool. The results presented are both preliminary for these authors, and even more preliminary for the profession as a whole. It’s not been fully developed yet, let alone stood the test of time. And, for pedagogical purposes, the authors used an oversimplified version of their tool in order to generate the figure above.

But while their work with this specific tool is preliminary, there are plenty of other tools that have been around for a long while which also measure exposure. What was novel in this paper was the authors’ ability to extract a meaningful measure using only publicly available data. And so we have a unique picture for JP Morgan.

Second, I only use this figure suggestively, in order to illustrate a point about the type of evidence one should look to. Unfortunately, the publicly available data used to construct this figure is too aggregated to allow us to measure what needs to be measured. I tried to phrase the questions and conclusions at the top of the post carefully. In the words of one of my former math professors, “The theorem says what it says, and nothing more.” What we really need to know is whether a specific subset of the derivative portfolio successfully hedges the bank’s other lines of business. But the figure shows the risk exposure on the whole derivative portfolio, because the paper’s authors are working from publicly available data which only provides the aggregate position. The whole derivatives portfolio combines many different operations, including the market making that the company does. Some of the fluctuating risk on the whole portfolio may be shaped by factors driving these other operations, and not speculations in the CIO office. So we cannot firmly conclude from this figure whether the trades in the CIO are designed to hedge the traditional deposit taking and lending operations. And we cannot firmly conclude from this figure whether the trades in the CIO are prop trading.

We can’t, but JP Morgan’s management can. And its supervisors ought to be able to. They have access to the disaggregated data that can answer these questions. While this figure doesn’t show us precisely what we need to see, the right figure can be constructed.

Suppose that its steady-state GDP growth is 4 per cent and that fixed interest on EU rescue loans is 3% per cent Instead of the fixed rate loan, Cyprus could issue bonds paying interest at its GDP growth minus 1% (the difference between the average growth rate and the EU bailout rate). If GDP growth next year is 0 per cent, lenders would pay the Cypriot government 1%, providing Cyprus with some relief in hard times. But if after, say, 10 years GDP growth is 7 per cent, lenders would instead receive 6 per cent. In essence, during recession EU lenders will provide insurance and interest subsidy to troubled Eurozone members, helping them pull themselves up, in exchange for higher growth returns during good times. Increased interest bills in good times might also discourage governments from sliding back into bad habits.

As we’ve written in a couple of earlier posts, this is easier said than done. But it’s certainly thinking along the right lines.

This is silly. There’s plenty of scientific evidence that the gold price is a random walk. Here’s an old reference: Eduardo Schwartz’s Presidential Address to the American Finance Association back in 1996. There are older and more recent papers finding the same.

Last week I wrote a post in which I mentioned that the time series of commodity spot prices are often mean reverting. They contain an element of predictability. Gold, however, is the exception. Gold is very, very, very cheap to store. And it is widely held purely as a store of value without any use value. Consequently, the spot price of gold quickly incorporates changing market views about future supply availability and any other fundamentals like those itemized in the Goldman report. For all intents and purposes, a physical investment in gold is a financial security, which means that the spot price is a martingale. The distinction I made in my last post between the spot price series for a commodity and the time series for a specific futures price is a meaningless distinction for gold.

Our paper on end-users and the cost of margins is now out in Morgan Stanley’s Journal of Applied Corporate Finance.

Chesapeake still thinks it can time the market.

On Tuesday management held its Conference Call to update to investors and stock analysts. Steve Dixon, the acting CEO, said “We’ve also taken advantage of the recent surge in natural gas prices to lock in additional price protection in 2013, and we have begun to hedge natural gas production in 2014 at prices well above $4, a level the market has not seen for some time.”

The company has had problems in the past from its foolish attempts to time natural gas prices. Last time prices were falling and the company took off its hedges. This time prices are rising and its putting on hedges. But the mindset is the same.

Behind this dynamic hedging strategy is a common misunderstanding about mean reversion in natural gas prices. The same misunderstanding applies to other commodities as well.

Most stock investors are familiar with the random walk model of stock prices, which challenges the naïve imagination that stock prices are mean reverting and investors can time the market to buy low and sell high. Many investors have realized that the stock market is efficient enough so that there is no easy money to be had in that sort of timing. In order to time market moves, an investor needs inside information, or the privileged access of high frequency traders, or some other edge.

It’s well understood that commodity prices are not a random walk. They mean revert. Gluts and shortages do arise, temporarily driving the spot prices down or up. Recoveries can be predicted, and the temporary fluctuations can be substantial. In the case of crude oil, I use the rough estimate that about one-half of the volatility has to do with short-term volatility where mean reversion is to be expected. For natural gas, even more of the volatility is associated with transitory factors. So the dynamics of a commodity spot price are very, very different from the dynamics of stock price.

Commodity companies rightly shift production in response to these short-term fluctuations, where possible. These fluctuations are associated with a shifting slope in the yield curve, meaning either more backwardation or a stronger contango. These shifts are signals to store less or store more, to increase or decrease short-run production even at greater expense.

But… these are NOT signals to lock-in a forward price on more or less of a given production profile.

Although commodity spot prices are mean reverting, the price of a futures contract for any given maturity date is essentially a random walk. Managers cannot time their hedges to capture the peaks or troughs of futures prices.[1]

Spot prices and futures prices are very different beasts this way.

A time series of spot prices is a concatenation of prices for delivery at different dates. Delivery at different dates is essentially delivery of a different thing, and the cost of delivering on different dates can be predictably different. There is no reason that the spot price of a commodity for delivery on date t has to be an unbiased forecast of the price for delivery on date t+1 or t+30 or any other future date.

A time series of futures prices for a fixed delivery date T is not a concatenation of prices for different things. They are sequential prices for the same thing: delivery of the specified commodity on the specified date. The futures price on date t should be an unbiased forecast of the futures price on date t+1 or t+30, if they are futures for delivery on the same delivery date T. A time series of futures prices for a fixed delivery date T should follow a random walk, and for the very same reason that a time series of stock prices should follow a random walk.

In technical terms, a spot price series is not the price of financial asset and doesn’t need to follow a martingale, while a futures price series is a financial asset and should follow a martingale.

So, just as investors in stock are unlikely to capture much return with a simple model for timing stock prices, so, too, Chesapeake management is unlikely to capture much return with a simple model for timing their forward sales of natural gas. Given a time profile of production, there is no way to produce extra value by putting on a hedge at a natural gas price peak or taking off a hedge at a natural gas price trough.

Chesapeake should drop their futile efforts to time the company’s hedges. That time and effort could be better spent on the issues that really will impact value: the management search, the disposition of assets and leverage ratio, and the shift of focus to liquids production.

The Financial Times’ Andrew Jack reports on the pharmaceutical company Valeant which has been on a buying spree financed by debt. For an ambitious businessman with a view, debt is the tool that makes scale feasible. And, as long as everything works out as planned, the returns are great.

But what if they don’t work out as planned? Who’s bearing that risk?

The Deal Professor, Steven Davidoff over at the New York Times takes a look at debt financing concocted the other way ‘round. Instead of used as a tool to enable acquisitions, it is offloaded as a part of a spinoff.

In these cases, it’s often much clearer who is bearing the risk.