Trading the Inverted Yield Curve (Part 2)

Following up from Part 1, I'd now suggest that STPP ETN could be the most hassle-free candidate to trade the inverted yield curve, but take note of its expiry date (13 August 2020). STPU ETF is another candidate, but the data points are too few to assess if it would have good tracking. We can possibly first trade the STPP and then "roll" it over to STPU nearing expiry.

The risk I'd like to address here is transmission risk - risk that the trade vehicle does not deliver on its expected results. We might get the call right, but if the vehicle gives poor tracking, it'll be a shame.

Make your own assessment and take the trade according to your risk appetite. For me, it's more for fun because these come only once a decade or so.


Is this time different?

First we have to get a sense of what are the likely scenarios in the next 1-2 years:

Recession - this should give us the best outcome on the steepening bet as the near yields would decline faster than the far yields.

No recession - the 2YR and 10YR rates would likely peg each other closely like in 1995-2001, and we need to wait for the next recession to trigger the steepener.

The major phases can be summarized as such:

< 1990 No clear trend in spreads
1990 - 1992 Yields falling, spread widens Recession
1993 - 1995 Yields rising, spread slowly narrows Expansion
1995 - 2000 Spread continues to slowly narrow Rates fluctuate, expansion
2001 - 2003 Yields falling, spread widens Recession
2004 - 2007 Yields rising, spread narrows Expansion
2008 - 2011 Yields falling, spread widens Recession
> 2011 Yields flat / slowly rising, spread narrows Expansion

There is a good amount of similarity between the 1993-2000 phase and the current phase in that both feature strong, prolonged bull markets and a slowly declining yield spread.

In addition, the instance in 1995 where the spread is near 0 and then both of the rates go lower in sync is very similar to what is happening right now. In that line, what may occur next is a prolonged wait before the steeepening occurs.


How good is STPP for tracking?

The rolling beta is too choppy for any use, so instead we will look at major inflection points on the 2YR-10YR Spread, and focusing on the dates from when the STPP is available.

The drag effect that you see is not so much due to STPP as it is due to the index that it attempts to track. The index does not do too good a job in tracking the spread. As stated on the website:

"Reasons why this might occur include: market prices for underlying U.S. Treasury bond futures contracts may not capture precisely the underlying changes in the U.S. Treasury yield curve; the index calculation methodology uses approximation; and the underlying U.S. Treasury bond weighting is rebalanced monthly."

The beta (sensitivity of the STPP price to the yield spread) can vary quite widely. It's a crude measure, but gives a ballpark figure of what we can expect. The effect seems a bit asymmetrical - down moves are of a higher beta - which is not good. Also note the small recent uptick in spread did not translate to any gains in the price - in fact, price went down slightly.

However, note that we are only looking at fractals of one leg of the cycle. These intermittent fluctuations could well be overwhelmed (in a favourable way) in the next major leg up.

Of course, the it could also be that the leg up of the cycle suffers from the low beta problem as well. Which direction, I'm uncertain, but it may not matter that much.

Below are 20-day and 60-day correlations on the values and on the daily % change in prices. Again, this is a more granular detail which I believe will be flushed away with the tide on the next leg up. The minor concern here is that the correlations can be zero or negative at some points in time, despite giving it a 60-day period (3 months).


Targets and stops

From the first chart, we can reasonably expect the spread (when it steepens) to return to previous historical levels of 2.0 to 2.5.

The mechanics of the pricing is not easy to estimate because of the way it's structured - the index multiplier and the monthly rebalancing.

A simplistic assumption of the STPP price would be for it to return to its $40+ level - where it coincided with the 2.5 spread level.

The spread could go to -0.5 as per previous inversions, or worse but unlikely to -2.5. This might roughly translate to a price of $20 and $0.


Continue reading "Trading the Inverted Yield Curve (Part 2)"

Trading the Inverted Yield Curve

Recent headlines are all about the yield curve being inverted and how low yields are now. Generally when there's such consensus and focusing on the extremes in the news, I like to see if there are any opportunities to be traded.

You can skip to the recommendated implementation in Part 2 or go to the bottom of this page for the implementation of the synthetic position

First, on the low rate environment

The problem is that it is not so simple as to go short on bonds (long yields) in hope that since it's at a historical low, it will rebound. The reason is because we are likely at the late stage of the business/economic cycle, and there's the looming risk that equity markets will undergo a correction of some degree soon. When that happens, it's almost certain that rates will be lowered even further to cushion the fall. Therefore it is not unlikely that rates go even lower from where they are right now.

Next,  on the inverted yield curve

This is a better play in terms of likelihood. The inversion of the yield curve is a relative play and we can implement a long-short strategy to benefit from the eventual steepening of the curve.  If a recession happens, this will take a little longer. If there's no recession and the economy chugs on, then the curve should become steeper sooner as confidence is regained.

Which spread to trade

While the focus is usually on the 2YR-10YR spreads, we can take a look at the other combinations to see which one is the most 'underpriced' now compared to its historical values.

This turns out to be the 1MO-10YR spread, but this is not readily implementable. Thus, we will look at implementing the benchmark 2YR-10YR spread.








There are ETFs that directly track this. Examples are the new STPU, and the older STPP (ETN). You can even try shorting the ETF FLAT. The problem with these are the illiquidity (spread costs) and the expense ratio.

A cheaper DIY way is  to use the bond ETFs for the short end and the long end: SHY and TLT. These 2 tickers respectively target the  1-3 years and the 20+ years portion of the curve. Going long on SHY and short TLT is akin to trading the yield spread, as we can see how they move in similar fashion, though not perfectly.  

You can also attempt the trade using futures /ZN and /ZT if you have access and margin.

Below are some details to consider for the SHY-TLT synthetic position. The rolling correlation uses the 60-day change in level (not percentage) and a 60-day rolling window.



Synthetic position (1x SHY - 1x TLT)

Note of caution - the SHY was not very responsive to the 2YR rates (and thus also the resulting price spread vs yield spread) pre-2009, but it seems to be ok now.

To execute this spread as a steepener, we will need to long SHY and short TLT, one unit each.

Time Decomposition of FX prices

In this method, I investigate the behaviour of prices in off-market hours - if they have a consistent drift, and whether it could be used as a trading strategy.

The currency pair I've chosen to start with is the USDCAD as both countries share the same broad timezone. This makes for easy identification of 'busy' hours (normal trading hours) and 'quiet' hours (major markets are closed).

From there, we can construct two new price series consisting only of movements within those hours - one for busy, one for quiet.

The preliminary results (for the sample period 2018) indicate that the movements in the pair were driven mainly by activity in the Busy period - seeing how the Busy moves almost alongside the Actual. This is an expected outcome - we expect  that real money (coming in during the market hours) are what drives prices and create permanent impact.

However, the Quiet period, over the course of days/weeks, can move in the opposite direction of the Busy trend. As examples, the major down move from Jul to Oct driven by Busy period was partially offset by the Quiet period upward move. The following rally by Busy was also partially dampened by Quiet in Oct to Dec.

Do note though that this mean reversion in quiet hours is not strong enough on a daily basis to warrant a fade-the-move style of trading. The daily change correlation is near 0, and the cumulative prices correlations are near 0 too.

We need to dig deeper into how this effect plays out on an aggregate level to produce the offsetting effects we see in the 2 example periods highlighted above.

Beta of VIX Futures to VIX Spot

VIX Futures are the most direct way to trade the VIX. However, the price of the front month future will hardly track the VIX one-to-one. Thus it is important to estimate the sensitivity (beta) of the futures versus the index so that we can have some gauge of how our futures position might change in relation to the index.

The chart below shows that the front month futures (VF1) can be quite different from the VIX spot index.

Note that as VIX spot index gets higher, it is more likely that the VF is in backwardation. This is intuitive as at higher levels, it's much more likely that the VIX recedes back to some normalized lower level by settlement date.

Contango vs Days to Expiry

First thing to note is that VF is most of the time in contango, and up to 30% premium. The contango ratio here is simply VF/VIX such that a reading of 1.3 means the VF is 1.3 times the value of VIX.

Second thing to note is the decay of contango - looking at how the cluster of 5-10 DTE decays the fastest as it goes to the 1-2 cluster, compared to the decays of the other clusters. This means that the decay rate is fastest when the DTE is less than 10 days - something to note if you're selling volatility.

Estimating beta

Beta can be measured in many ways. Here we use daily changes in the front month future (VF1) over the VIX index, expressed as a ratio.

We should expect that as the days to expiry decreases, the VF will track the VIX more closely. This is because VF is essentially a bet on what the VIX will settle at the VF expiry date. As expiry gets closer, market participants have narrower ranges for their predictions.

We are able to see from the chart below that the median beta gets closer to 1 as the days to expiry goes down. The betas are however very noisy and it might be useful to use other ways to measure beta, perhaps using intraday data.







VIX Selling Strategies

This series of posts will look into ways to systematically short the VIX via VIX Futures.

Strategy 1 - Sell and hold to expiry

Strategy 1a is simply to sell on the open of a new front month future, and close just before expiry.

Strategy is not good, as we can see there are years of choppy growth, and a drawdown can take several months to recover. The strategy owes it profits to the stellar years in 2016-2017.

A look at the opening price vs ending profit shows us that opening trades at higher prices are more likely to end up profitable. We can thus attempt to use a simple filter - sell only if price is >15. The results are shown below as Strategy 1b.

The equity curve is much smoother, but it has years of missing out (when open price is lower than 15 e.g. during the calm of 2017 which were the best years of selling VIX). Realistically, you wouldn't want a strategy that hardly trades.

Strategy 1c is a play on the autocorrelation structure of the returns. If losing months are usually followed by winning months, then we can enter after observing a loss on the hypothetical portfolio.

While it is overall positive, it fails to capitalize on the winning streaks of the strategy, and it does not filter out the losing months well enough, and so overall it's not good.

Conclusion of strategy series 1

The simple buy and hold (or rather, sell and hold) is not robust enough for a strategy, and we will have to investigate further.


2013-05 is the first expiry date obtained from CBOE and data before that to 2007 obtained from