Brexit No Deal – Custom Payoff Strategy

Here we look at constructing a customized payoff for this event.

Betting site odds are 7/1 for No Deal: Bet $1000 on No Deal

Buy ATM GBPUSD Call for $2000 at strike 1.2650 (spot is at 1.2640) - breakeven is at 1.2850 which is 1.7% up (if including loss from bet, 1.2950, which is 2.5% up)

Deal No Deal
GBP up -1000 from Bet

+ ??? from Call

Net > -1000

+7000 from Bet

+ ??? from Call

Net > +7000

GBP down -1000 from Bet

-2000 from Call

Net -3000

+7000 from Bet

-2000 from Call

Net +5000

Limited risk payoff with maximum downside of -$3000

Assign probabilities into the 4 scenarios to get expected value, example:

Deal No Deal
GBP up 70% 5%
GBP down 5% 20%

Unfortunately, I think that when the Deal scenario plays out, the GBP will not rise enough to cover the cost of the bet and the premium paid.

Also, I don't have access to online betting sites, so this will only remain a theoretical trade.

The January Effect – Part 2

Continuing from Part 1

Comparing between strategies

S2 strategy invests in months {1,3,4,7,10,11,12} and yields better Sharpe and higher total return than the benchmark. It also has less negative skew.

S1 invests in only 4 months of the year {3,4,11,12} and so the total profit is much lower. The Sharpe does not improve, but the skew is significantly improved – it is now positive.

S1b squeezes out a little more total return by shorting month 9, giving us less of a max DD, but does not improve on the Sharpe nor the skew, so it might not be worth the effort.

Because this is an active method, the number of trades needed is proportional to the number of months. S2: 7, S1: 4, and S1b: 5.

Active method equity curves

Using the active method, we can see that the curve is significantly smoother than the benchmark, especially for S1 strategies.

Passive method

The passive method is what we normally do in real life – a hypothetical growth of $1. We do not rebalance the amount, but simply let it grow. The percentage returns are compounded, not simply added as per previous method.

Conservative strategies (like S1) are penalized because they tend to miss out on some of the compounding gains accrued in bull runs. This makes a sizeable difference in the long run.

The passive strategy comparisons show that S2 allows us to beat the benchmark, with slightly lower SD and drawdown measures.

S1 strategies are far superior in terms of Sharpe, but don’t eke out as much of a profit. To solve that, use leverage.

Using leverage of 1.5x, we can get the S1 strategies to beat the the total return of S2 and the benchmark, with somewhat similar SD and drawdown.


However, implementation is not so simple – we will need to find a way to get the 1.5x monthly return of the index, and keep compounding it. It’s not as easy to implement as S2.



Following a simple strategy like S2 can give us better total and risk-adjusted returns than the benchmark.

Continue reading "The January Effect – Part 2"

The hunt for the ESG factor

Personally I don't fully believe there exists an ESG risk premia. I think it's very difficult to find a big enough and uniform group of companies that can achieve a dual mandate of profitability and high ESG focus.

To be able to pick a few of these companies would also prove difficult and may not be worth the effort.

However I do believe that in the near future, there will be a tide of investors (millennials or the next generation perhaps) who will fanatically invest only in such companies.

Because of that, there is upside to this theme.

So what I hope to do is ride the short term trend when it is in fashion in an almost bubble-like way, then exit (if my assessment of no long term premia still holds).

The January Effect

Many have heard of January effect in stock markets - stocks generally go up in January. Here we take a look at this phenomenon and see if it still persists.

Below are the results for the S&P 500 for 1951 to 2018 using monthly simple percentage returns

It shows that while the mean return is indeed positive, there are many other months with higher mean returns. Might as well call it the January-and-March-and-April-and-July-and-November-and-December-effect.

What's more important is that the Sharpe of the other (Mar, Apr, Jul, Nov, Dec) months are higher.

The only advantage that January has is that it is the only month with a positive skew, albeit very slight.

Below are the return distributions for each month.

We are also interested in the smoothness of the equity curve, and whether the strategy is robust through time.

Below we can note the following observations:

  • The January effect lost its effect from 2000 onwards
  • Mar, Apr, Nov, Dec are remarkably good month
  • Sep is consistently bad
  • Oct (while having the reputation for worst declines) is still on a general up trend

In the next part, we will look at implementing a market timing strategy that invests only in certain months, and see how it fares against the benchmark.

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)"