Introducing Model Portfolios

On this website we already offer access to a portfolio page where we show the current exposure that our models have on a number of markets we trade. Throughout the years many customers have expressed their desire to have access to even more markets and at the same time to have some more guidance on what instruments to prioritize at any point in time. 

To address these points we are now introducing a new product: the model portfolios. 

Access the model portfolios here

Model Portfolios that are made available to our customer are Model_A, Model_B, Model_C, Model_D, Model_E and Model_F.  These portfolios follow different rules and are designed to show low correlation with the other portofolios and the market. As such, they can sometimes open opposite positions (i.e. Model_A is long SPY while Model_B is short SPY). Our model porfolios trade different instruments within the following set: 

For this reason, people can choose to follow their preferred model portfolios based on risk and performance or decide to follow all of them. 

How to follow a model portfolio

A model portfolio will only hold one ETF at any given time and if a position change is required this will be visible on the dedicated page on this website (shortly after market closes) and an order to buy/sell will be submitted at the next market open. 

How do model portfolios differ from the other strategies we publish

These portfolio follow a more complex set of strategies than those we employ for the signals we already publish here. The underlying models will select the instruments that are good candidates for the long or short position that we want to initiate. An algorithm will then pick the single best instrument amongst all the candidates at any given point in time. The trader will simply need to check what is the pick for each model portfolio and submit an order to buy at the next market open. 

Access the model portfolios here

How active are the model portfolios

Historically positions are held unchanged for about 80% of days. This means that on average we could expect less than 4 trades per month. 

Performance and Risk of the Model Portfolios

Performance on all model portfolios have been amazing and far superior than the markets we used as benchmark despite showing at most the same level of drawdowns. 

The equity lines and the drawdowns of each of the model portfolios are shown below (in blue). For comparison, the equity line and the drawdown of the benchmark market are also shown (in red). Performance is shown since 2008 or since inception of the instruments used (if later). In all cases the starting capital is $10,000.

Model_A is long-only and it is designed to switch between equity and interest rates ETFs. Only one instrument will be held at any given time. Here the performance is shown against SPY. 

The model has an annualised return of 21.2% and the initial investment of $10,000 has grown to more than $220,000 (a performance of +2,119.7%).

Model_B is short-only and is designed to short equity or volatility ETFs. Only one instrument will be held (short) at any given time. Here the performance is shown against SVXY. 

The model has an annualised return of 40.3% and the initial investment of $10,000 has grown to more than $657,000 (a performance of +6,473.9%).

 

Model_C is long-only and it is designed to switch between various equity, interest rates and inverse ETFs. Only one instrument will be held at any given time. Here the performance is shown against SPY. 

The model has an annualised return of 25.78% and the initial investment of $10,000 has grown to more than $400,000 (a performance of +3,929.3%).

 

Model_D is long-only and it is the most aggressive portfolio since it invests in 3X ETFs. It is designed to trade equity and interest rates ETFs. Only one instrument will be held at any given time. Here the performance is shown against UPRO, since inception. 

The model has an annualised return of 95.8% and the initial investment of $10,000 has grown to more than $115,000,000 (a performance of +1,155,697.0%).

 

Model_E is long-only and it is designed to trade equity (leveraged) and interest rates ETFs. Only one instrument will be held at any given time. Here the performance is shown against QQQ. 

The model has an annualised return of 38.89% and the initial investment of $10,000 has grown to more than $1,900,000 (a performance of +19,790.3%).

 

Model_F is long-only and it is designed to switch between equity and interest rates ETFs. Only one instrument will be held at any given time. Here the performance is shown against SPY. 

The model has an annualised return of 18.73% and the initial investment of $10,000 has grown to more than $159,000 (a performance of +1,494.5%).

 

Performance and Risk of the Model Portfolios (during 2020-2024)

The following charts focus on equity line and drawdowns since Jan 2020. Again, starting capital is assumed to be $10,000.

 

If interested in checking the updated positions of our model portfolios click the link below.

Access the model portfolios here

 

Performance and Risk of the Model Portfolios (linear scale)

The following charts show performance since Jan 2008 in linear scale. Again, starting capital is assumed to be $10,000.

The strategies we use in our portfolio are purely quantitative, i.e. they follow a specific set of rules to identify risk on and risk off conditions that we then use to determine our exposure to the market on a given day, 

There is another interesting approach to analyse the markets that is somehow less objective despite having its own set of rules and guidelines. We are talking about Elliott Wave Theory. 

There is often a lot of confusion about how to use Elliott Wave Theory when trading and it mostly come from the fact that people tend to have the wrong expectations about this tool. What needs to be understood is that this approach won’t give us one prediction about future market direction but many.

The rules and guidelines are extremely helpful to identify a list of plausible scenarios but will never be able to reduce such list to just one scenario. 

So is it of any use then? Definitely yes, because being able to identify a set of plausible counts that describe the current market environment and how it can evolve in the future is extremely important when taking trading decisions.   

Let’s look at one example. This is our current Elliott Wave count on SPY. What we show here as dotted lines (red and white) are expected future paths for the price based on what we consider being the most likely count at this point. 

As we can see, in the short term we expect the market to either go up or down… not very helpful for now. But with Elliott Wave Theory we need to have a plan and we won’t decide to enter long or short at this point without first having a confirmation from the market.  

But in the medium term both the red and white path point down, so can we go short with that horizon in mind? If that is your primary count then yes, that’s a possibility. But when building the exposure we should always take into consideration other possible counts in order for us to understand at what point our initial “primary count” becomes less likely and we should shift our view and adjust our position accordingly. 

For example, the following count is also a possibility at this point in time. Whoever wants to go short with a medium term horizon must take this into consideration.  

In our “Option Strategist” series we have covered many adjustment to option strategies and we believe that type of activity would benefit greatly from a Elliott Wave type of analysis. Deciding how to adjust a position and when is what Elliott Wave Theory can help you to decipher. 

But the information presented in this document can be used by any trader who wants to learn how to read the markets, not only those using options. 

 

 

What is a 60/40 portfolio?

In order to achieve some diversification and reduce volatility, a common approach is to create a portfolio containing both equity and bonds. A 60/40 portfolio, in particular, is one where we invest 60% in equity and the remaining 40% in bonds.

In this balanced combination, the bonds have often provided a partial offset to losses generated by the equity component thus reducing the overall volatility of the portfolio.

Why 60/40 portfolios are suffering right now?

What has worked in the past seems to have changed this year and the reason is that, after a very long period of declining interest rates, we have entered into a rising rate era.

Bonds prices depreciate when interest rates go up and viceversa. So it shouldn’t come as a surprise that in the past bonds have generally performed quite well overall, given the general downtrend in rates. It should also be noted that when interest rates are generally high, bonds also provide higher income in the form of higher coupons hence adding to the total return of the instrument. This is why, until recent past, having a portion of the portfolio in bonds has provided positive total return that has helped to absorb losses generated by the equity investment during stressed periods.  

Since we are now in low rate regime with rates starting to rise, this is a double source of headwind for bonds prices which helps to explain why 2022 has seen a very bad performance in this asset class.

But, as we all know, we are currently experiencing also a bear market in equity. So both bonds and equity are falling at the same time. Very bad for a pure 60/40 type of exposure that is based on the idea that diversification between bonds and equity should help reducing the overall volatility.

How about an actively managed 60/40 portfolio?

The general idea behind 60/40 allocation is a valid one but as markets change so does the effectiveness of that idea. A buy & hold in a 60/40 would have performed relatively well in the past but quite terribly in 2022, due to rising rates. 

In the chart below we are comparing the performance of such portfolio traded actively using our strategy (in blue) vs a simple buy and hold (in red).

In both cases, the 60% invested in equity is using UPRO and TQQQ (in equal portion, i.e. 30% of total capital each) while for the bond component we are using TMF (with 40% of the capital).

While buy & hold keeps this exposure throught the entire period, in our actively managed allocation we follow our signals to decide how much exposure to have on any single day. For example, given that 30% of the capital is allocated to TQQQ, if our system suggests an exposure of 50% on a given day, 15% of our capital is invested in TQQQ for that trading session, with the remaining 15% kept as cash.

We can see that a buy and hold of this portfolio would have generated an annualised performance of -4.2% while actively trading the portfolio using our signals would have boosted the annualised performance to 56.1%. But the core of our strategies is to achieve superior returns with much less risks so let’s look at the VaR and expected shortfalls.

We can see that with a 1% probability a buy and hold would have exceeded a daily loss of 8.88% and in such cases the expected loss would have been 13.15%. By trading using our signals, we managed to reduce the VaR to just 4.14% and during those 1% of days experiencing losses higher that the VaR, the expected loss was just 5.1% (vs 13.15% for the buy and hold).

Did volatility drag penalise the performance when trading the leveraged ETFs?

In one of our articles we have described the mechanics of leveraged ETFs and how volatility drags could impact their performances. In the context of leveraged ETFs, a volatile market is directionless, with frequent swings that keep eroding the performance of the instrument.

This makes these instruments somehow problematic to trade, because it might happen that even if you get the direction right, excessive volatility in the path will have partially reduced the overall profitability of your trade.

One of our conclusions was that leveraged ETFs can still be used successfully as trading vehicle in an active trading strategy, i.e. not as buy and hold investment. In particular, a strategy like Trading Mate that tries to increase exposure to the markets only when a directional move is expected, should not suffer as much from volatility drags in leveraged ETFs.

To quickly understand how much the volatility drag has impacted on perfomance we can look at the performance obtained using non-leveraged products (here using SPY, QQQ and TLT) and compare this against the performance using leveraged ETFs (as per previous section). 

The buy and hold position on the non-leveraged portfolio has obtained 11.5% while the 3X leveraged version has obtained -10.9%. This is much less than 3 times the non-leveraged performance and it’s actually an overall loss.

Let’s look at our active strategy now. Using non-leveraged ETFs the strategy performance is 47% while using the 3X leveraged ETFs we achieved 229.4%. This is much higher that the 3 times performance we would expect to see. Here we can see that the daily rebalancing of the leveraged ETFs provided further boost to the performance we obtain by actively managing our exposures.

This was one of the conclusions we drew in the previous article and we are happy to see it confirmed here by numbers, even after the recent period of high volatility.

Understanding volatilities is crucially important, especially in today’s markets. And if you believe that this is true only for those who trade options, you would be very wrong. In our new notes we are explaining all you need to know about volatility (or, better, volatilities) and how you can use them to make more informed trading decisions.

It is usually difficult to monitor all implied volatilities (i.e. the entire volatility surface) so, in order to gauge the market-perceived risk in the market, we usually focus our attention to ATM volatilities. These give us an immediate view of the volatility that the market is anticipating for that specific underlying until the option maturity date.

One point to pay attention to when monitoring ATM volatilities is the following. When the market moves, so does the ATM point. If, for example, on date 1 the market was trading at 453 and on date 2 it falls to 423, the ATM point has changed from 452 to 423 as well.

If we compare the smiles on the two dates, we can see that on date 2 we have an increase in implied volatilities but if we only look at the ATM volatility we are tempted to conclude that volatilities have moved from 19.5% to 29%. Instead, on a fixed strike basis, we can see that the 423 strike has moved from 26.4% to 29% while the 452 just from 19.5% to 20%.

The presence of the skew makes the ATM volatility move along the smile, so especially during a large movement it is important to understand that the movement we observed in the implied volatilities is driven both by a change on a fixed strike basis and the fact that the ATM points shifts upward when there is a negative skew.

The bottom line is that it is important to critically look at the changes in the ATM volatilities to understand what is really happening to implied volatilities.

As many of you might know, VXX is an ETN issued by Barclays that gives exposures to the daily return of a combination of front and second month VIX futures. This has always been our contract of choice when trading the low risk option trading strategy we presented on this website.  

Last March Barclays has announced the indefinite suspension of any further sales from inventory and any further issuances of this note. But does it mean we cannot follow our trading strategy anymore? Luckily not! 

As you can see in our articles, we always mentioned VXX together with VIXY. This is because, apart from some minor differences, these contracts behave the same way. Or, at least, they did until Barclays’ announcement last March. Since then, the VXX has traded at a premium and has not followed the dynamics of the underlying portfolio of VIX futures it is supposed to track. 

The following chart shows clearly this decoupling. 

 

In the past few months we have suggested the subscribers who are following our “VXX option trading strategy” to continue doing so by simply replacing VXX with VIXY. The latter is the natural alternative to VXX and hence allows us to exploit all the characteristics of VIX futures contango we want to leverage in our trading strategy. 

What is implied volatility

The volatility is often used as a measure of risk for a given instrument: if instrument A has a volatility of 10% and instrument B has a volatility of 5%, we consider A riskier than B.

But volatility is a very generic term and can only be estimated. So, different ways to estimate it would likely give a different level of volatility for that same instrument. A market-agreed volatility, instead, is a measure that can be implied from option prices, i.e. from prices that are established by supply and demand in the market.

Options are traded instrument that are directly affected by the volatility of its underlying. To understand why this is the case, it is useful to remember that when we buy an option, we pay a premium and this is the maximum we can lose.

Let’s consider a call option. Once we buy a call, we pay a premium and when the option expires (or is exercised) we receive a positive payoff if the underlying price S is above the strike price K. In the example below, we assume that the strike price is 450 and the premium we paid is 100 USD. The orange continuous line shows our P&L profile at expiry while the dotted line is the T+0 line, i.e. our current P&L profile. Here we can see that while losses are limited to the premium we paid, we start making profit as the market moves above the strike price. At some point, the difference between the underlying price S and the strike K is large enough to compensate the premium we paid and the position will be at a profit.

A similar logic would apply to put options. The final P&L for a call and a put can be summarised below:

Clearly if volatility is very high, we have a higher chance to hit a large P&L on the option because the underlying S might move well beyond the strike K. At the same time, the losses would still be limited to the premium we pay.

The option market compensates for this higher chance of profit by increasing the premium we pay for the options. This mechanism creates a direct relationship between option price and volatilities.

Since option prices are traded in the market, we can use a pricing model to imply the value of volatility that would justify the option price we see. This measure of volatility is called implied volatility.

 

Volatility smile/skew and implied distribution

Differently to all other volatility estimates, this measure is a market-agreed estimate of the expected volatility that the underlying will experience until expiry date. An important point is that this measure is not unique and different strikes will typically have a different implied volatility, even for the same expiry date. As a result, when these implied volatilities are plotted together, they form a curve that is typically not flat. In the equity markets, we usually observe a curve that is skewed on one side, this is why we often talk about volatility skew instead of volatility smile.

But why should options at different strikes imply a different expected volatility for the same underlying over the exact same period of time?

To understand this point, let’s say that we are inverting the Black-Scholes formula (the de facto standard for the pricing of European-style options) to infer the level of implied volatility on each strike for a given expiry date. This model is built on the assumption that the log-returns (a measure for the change in the underlying price) are normally distributed (so the price is log-normally distributed). Under this assumption, we know the probability associated to all possible changes in the underlying price over the period covered by the option and, in particular, we also know the probability associated to extreme movements.

The critical point is this: the normal distribution assumes that extreme movements occur with a relatively low probability if compared to how often these movements are actually observed in the market. In other words, if the model we use to price the option assumes that the underlying movements are normally distributed it is usually under-estimating the risk of potential large movements that might occur.

Traders in the market are aware of this and don’t believe in the assumption of normal distribution. The way they correct for this is by changing the prices of those options that would be mispriced under the assumptions of the Black-Scholes model.

These are typically the OTM options, i.e. those strikes that would be touched only if the underlying makes a large movements. Exactly those kinds of large movements that, under the normal distribution, have very low probability of occurring, but that the market participants expect to happen with a relatively higher probability.

This should help you understand why there is a connection between the volatility smile/skew we observe in the market and the probability distribution of potential future movements in the underlying. The fact that the pricing model makes the wrong assumption about this distribution means that the distribution that is implied from option prices has usually fatter tails, i.e. larger area in the extremes of the distribution (see the chart below for an example).

We mentioned earlier that the curve that is formed by putting together the implied volatilities at all the strikes for a given expiry is typically not flat. Only in a “Black-Scholes consistent” world, where returns are normally distributed, we would see a flat smile.

In the example below, we are looking at the volatility smile/skew on two dates. We can see that from date 1 to date 2 the volatilities at lower strikes have increased further (the negative volatility skew increases). We also see that the smile becomes more convex (i.e. more curved).

 

 

The volatility skew can be monitored by looking at the risk reversal, which is the difference between the volatility at a low delta put (in this example this volatility is 29% on date 2) and a low delta call (24% on date 2). On date 1 they were 26.4% and 22%, respectively, so the risk reversal goes from 4.4% to 5%. The low deltas used are typically 25% or 10%.

A butterfly, on the other hand, is used to monitor the curvature of the smile. In the chart it is the height of the blue and grey rectangles and are calculated by subtracting the ATM volatility from the average of the low delta put and low delta call volatility.

On the right plot we can see the distribution of changes as implied by each volatility smile (here changes are expressed in terms of number of standard deviations from the mean).

Based on what we have seen so far, we can say that changes in the implied volatilities ultimately reflect the expectation of market participants that the underlying might experience a large move with a probability that is different than what a model like Black-Scholes would imply. Any change in these expectations will move the volatility smile/skew and the associated implied distribution will represent the current market expectation of potential movements in the underlying (and this distribution is often far from normal).

But how do we know that one volatility skew implies a probability that is higher than the other? To understand this, let’s look at the distributions in this example. The plot below is a zoom of the left tail in the previous graphs. By looking at the shaded area under these curves we can calculate the probability that the underlying will make a down move higher than 2 standard deviations. This probability is the area under the corresponding curves.

 

 

Clearly this area is higher under the distribution in grey, which is the implied distribution associated with the smile as of date 2. So, the market is expecting a large move like this to be more likely now and this is reflected in volatilities in the corresponding area that have lifted up compared to previous day.

A natural question to ask at this point would be: does the implied distribution have any predictive power? The answer is generally negative.

Typically, if we see for example that the implied probability that the underlying will be below 340 at expiration is 5%, this doesn’t necessarily mean that the market is expecting the underlying to be below that level with a 5% real probability.

There are 2 main reasons for that and we will discuss them below.

The impact of hedging

The first reason, the most relevant, is that implied volatilities are driven by the suppy and demand of options and they can over-react, being driven by dynamics that are not necessarily linked to expectations. To simplify the concept, we can think about the dealers to be in a position where they have sold puts at lower strikes and bought calls at higher strikes. This means that they tend to have a short skew position that can be exemplified by a risk reversal strategy, like below.

 

We can safely assume that a dealer has delta hedged the position by selling the proper amount of underlying (35 units in this case) and that, given the low gamma, this delta hedging does not need to be adjusted that often.

With the delta risk hedged, we can focus on the other major sensitivity: vega. The position has a slightly negative vega, but this significantly changes as the market moves (high vanna exposure). So, when the market starts to sell off and we start to see implied volatilities rising, we also see this short skew position becoming more and more negative vega.

To hedge this higher negative vega exposure, after the market has dropped, the dealers will need to buy vega. Since buying vega means buying options, hence volatility, this hedging activity will push volatilities even higher, particularly at lower strikes.

The skew might be expensive also for another hedging activity taking place. Traders that are usually short volatility might use long skew position, i.e. involving buying puts that are OTM. Should a shock hit the market and volatilities rise, a long skew trade might serve as a hedge for the position.

As we have seen, a skew becoming more negative will translate into an implied distribution that has a left tail that becomes fatter, i.e. the market implied probability of a larger move is increasing. But looking at the reason why volatilities have risen after the market sell off, we know that this might have been driven also by normal hedging activity which doesn’t have much to do with market expectations. For example, a professional trader might be required to hedge the vega exposure even if he expects the markets to recover and volatilities to drop.

So, this example shows that some movements in volatilities are exacerbated by hedging activity that might be triggered by a sudden market move. Using the implied distribution associated to a volatility smile/skew to make any prediction would likely prove not useful, especially after a large movement in the markets.

The risk-neutral world

The second reason why implied distributions should not be used as predictive tools is more technical, and is related to the fact that these are actually risk-neutral probabilities, so not in line with real-world probabilities.

Implied distributions are in this context also called risk-neutral distributions, to highlight the fact that they can describe the likelihood of a given event in a risk-neutral world but not in the real one.

A risk-neutral world is one where all investors are indifferent to risk and don’t require any extra risk premium for the risk they bear. In this world, all assets (irrespective of their risk) will earn the risk-free rate.
Investors’ risk appetite and true/real world probabilities of a given event both play a role in the determination of the risk-neutral probabilities. Since in the real world investors are risk averse, they are more concerned about bad outcomes (for example a market drop), so the associated risk-neutral probabilities are higher than the real ones. Similarly, the implied probabilities associated to good outcomes is lower than the real ones.

This results in an implied distribution that is typically more negatively skewed than the real one. An example could be the grey distribution below. Here we see that the possible values for the underlying at maturity are the same under both the grey (risk-neutral) and the blue (real world) distributions. What changes are the probabilities and the fact that the risk-neutral distribution corrects for the risk aversion that we have in the real world, creates a skewed shape.

 

 

But why we need to use an artificial probability distribution that does not apply to the real-world? The reason is that this choice makes the pricing of options much easier.

Every price is nothing more than the value today of the expected payoff we will receive at maturity. To get the value today, we need a discount rate while to calculate the expectation we need the possible future values of the underlying and the associated probabilities.

The difficult part is to identify the right discount rate to use because this would need to reflect the risk of the position. On the other hand, we know that in a risk-neutral world that discount rate is the risk-free rate. So if we can calculate the expectation by using the risk-neutral probabilities then we can discount that expected payoff at the risk-free rate to get the price of the option.

In practice, to obtain the implied probability density function we can follow these steps:

1. Calculate option prices P at various strikes K by using strikes with a distance ΔK extremely small. If not all strikes are available in the market, use interpolation to find the missing ones.
2. Calculate the difference between consecutive prices ΔP
3. Calculate the ratio between ΔP and ΔK (this can be seen as the first derivative of the price with respect to strike)
4. Calculate the difference between consecutive ΔP/ΔK (as calculated in previous step)
5. Use the difference from previous step and divide by ΔK

At this point, by plotting the results from step 5 against the strikes, we can see the probability distribution as implied by the prices we used.

 

Options trading for everyone 

The signals from our Trading Mate portfolio can be followed using any instrument that offers exposure to a given market. This includes options, an instrument that many still don’t understand in full. 

The content is designed to guide the reader from the basics to more advanced concepts in a very clear and practical way and will be helpful for both the newbie and the experienced option trader who might not be familiar with all the concepts or adjustment techniques presented here. 

 

What will you find in the documents?  

Trade adjustments

Most of the focus will be on options trade management and adjustments, with easy to follow example of how to manage a strategy as the underlying and implied volatilities move. 

Strategy dashboards

The reader will learn how to critically look at options’ greeks and how they change as market moves and time passes. 

 

Clear explanation of important market dynamics

For example what moves implied volatilities, what creates the volatility smile/skew and how they are used to imply a probability distribution for changes in the underlying  

How to use implied distributions to select the best strategies 

Understand how to monitor key volailities to gauge an understanding of the market implied distribution and decide what strategy to put in place.

Easy to understand summary tables

To consolidate all the concepts and have a reference that is easy to consult in the future when needed. 

… and much more.

Access the Option Strategist section here

 

Access the updated portfolio here

 

 

A stock replacement with benefits

A frequent question that is asked about option is whether or not it makes sense to use them as stock replacement, i.e. not to trade volatility or to benefit from option’s time decay but simply to express a directional view on the underlying. 

The answer is definitely positive for one simple reason: buying an option will guarantee that your max loss is the premium you paid.

There is no other way to cap your losses. For example, the widely used stop losses are not guaranteed to be executed at the desired price. If, for example, the market gaps down and below your stop loss, your long position will lose more than what you thought you might lose at your stop loss level.

So, if you expect a stock or index to go up, buying a call would give you the positive delta you need and will have a max loss limited to the premium you paid to enter. Similarly, if you expect a stock or index to go down, buying a put would give you the negative delta you need with, again, the max loss limited to the premium you paid.

 

The option premium is made of 2 components: the intrinsic value and the time value. The first component is easy to understand since, for a call, it is the difference between the current underlying price and the strike price, or viceversa in case of a put option.

The time value, instead, is the additional price that the market is willing to pay on top of the intrinsic value for that specific option. This amount is mostly affected by the residual time to expiry and implied volatility. As such, any time value we pay when we buy an option will be lost by the time the option expires. This is why many people consider option selling as an income strategy, since there are strategies designed to bank the time value of sold options that we hope will expire out-of-the-money.

Let’s look at the option chain below for SPY. The put 450 has a time value of 2.51 while the put 445 has a time value 5.07. Given that the 450 is more in-the-money than the 445, the latter will have a higher time value while the former will have a premium that is mostly made of intrinsic value.

By definition, out-of-the-money options don’t have any intrinsic value so the premium we pay is purely time value.

Now, let’s say that we are bearish on SPY and want to buy a put to express our view. A put strike 455 has a delta of about 0.84 so this option will behave like a short position on 84 units of SPY. In the image below, the left panel shows the corresponding payoff and if you look at the table under the plot, you can see that if market doesn’t move, this position will lose about 110 USD.

Even if this option is in-the-money, the premium will still have some time value and if market doesn’t move this value will be lost, hence the 110 USD loss we see.

How can we achieve the same negative delta (here about 84) without having to pay much time value? To do that we can refer to the option chain we saw earlier, where we can see that the time value we pay on the 450 can be offset by the time value we get by selling the 445. So we can buy 2 puts strike 450 (total delta is about 1.4) and sell 1 put strike 445(delta is 0.54). The overall time value is almost zero and the overall delta is about 0.85, so we can get a delta that is similar to the single leg strategy we saw before. This is illustrated on the right hand side. 

But now let’s look at the table below the plot on the right hand side. The fact that we built the strategy to offset the time values means that if market doesn’t move, we won’t lose anything, since there is no time value that could be lost.

So we have a possible replacement for a short position on about 85 units of SPY without having to pay any time value. Additionally, if you look also at the max loss of the 2 strategies, you can see that the second one has a better risk/reward profile.

The obvious drawback of this approach is that the strategy involves 2 legs (3 options in total) while the original put strategy has just one leg. But with liquid options this is not a problem and the slightly more commissions and bid/ask you pay are more than compensated by the various benefits that this strategy offers.

From a trade management perspective, it is advisable to close the position few days before expiry. In the last few days the gamma of the second strategy is higher if the underlying has moved up 1%. This can easily be seen from the change in deltas in that region: while the single put has a delta that goes from -1 to 0 (on expiry date), the second strategy has a small region where the delta is -2 (because we are long 2 puts while one has expired worthless). So, about 3/5 days to expiry, this is the time where the gamma profile of the second strategy changes compared to the single put trade, so better to close and reposition.  

To summarise, using options for directional trading has the benefit of being a defined risk strategy in any scenario. This is thanks to the optionality that these instruments grant. The price we pay for this benefit is the time value, so finding a way to limit or completely offset this makes options a real “stock replacement with benefits”.  

This strategy can be used on the markets we trade. If interested in current Trading Mate’s exposures, please click on the link below.

Access the updated portfolio here

Micro sized exposure to US Treasury yields

The new contracts will be launched by CME on August 16 2021 and will be based on the “on-the-run” yields, i.e. the yields of the most recently auctioned Treasury securities, at four key tenor points across the curve (2Y Note, 5Y Note, 10Y Note and 30Y Bond).

The contract size is just $10 per each basis point of Treasury yield (i.e. 0.01%). If, for example, the 30Y Treasury Yield is 2%, the contract value will be $2,000.

 

Is this a totally new contract and was it really needed?

The answer to this question is definitely yes. The new micro Treasury Yield futures are indeed a new type of contract and, given the micro size, they will be useful in providing easier access to the Treasury (yield) future market.

Investors can already trade very liquid US Treasury futures, like the 10Y T-Note, the Ultra 10Y T-Note, the T-Bond, the Ultra T-Bond, etc. but these contracts have a face value of $100,000 with prices quoted in points per $1000.

As an example, a Ultra Treasury Bond future with a price of 188’300 will have an exposure of more than $188,000.

Clearly these micro Treasury Yield Futures will be much more accessible to the average investor.

But what makes these contracts somehow new is the fact that they are traded in yield terms and refer to a single on-the-run security. On the other hand, Treasury futures are traded in price terms and refer to a basket of underlying bonds (with maturity range depending on the tenor of the future contract).

Another difference, that is less important for most, is the fact that the new contract will be cash-settled.

 

Can this replace TLT (or TMF) in Trading Mate’s strategies?

Our strategy is using TLT (or TMF for leveraged exposure). TLT is a liquid ETF which holds bonds in the long-end of the Treasury curve thus providing an easy way to gain long-term Treasury exposure.

The chart below shows the results of our strategy applied to TLT trading. Current exposure can be accessed here.

The natural question one might have is whether it is possible to replace TLT with the new Treasury Yield futures. The best proxy that will be offered is the 30Y Yield future as it covers the long-end of the curve. For the reasons summarised above, the TLT is not expected to track exactly the 30Y yields but it will be close enough to be considered as a proxy. The example below shows the TLT price and the inverse of the 30Y yield (in orange).

So, should we use the new micro futures in our strategy we would need to find the equivalent number of futures to use to get the desired exposure. At current levels, 100 units of TLT give the investor a sensitivity of $26.3 for each 1bps move in the underlying yields. Given the new future (in this case 30Y) has a sensitivity of $10 per bps, that implies that to replicate a 100 TLT exposure we would need to use 2.6 future contracts (to be rounded to the nearest inteteger, so in this case 3). With these quantities, for a given bps move in the yield, the two positions will generate a similar P&L. Why similar and not exactly the same? One reason is discussed in the next section.

Some technicality to keep in mind

The TLT sensitivity is not constant but will change as the underlying yields change. This is because the underlying bonds have convexity and this implies that if the yields move a lot, the sensitivity we have calculated when entering the trade might change significanlty thus making the replication via Yield futures less effective.

This is not a major issue in short term trading but can become a factor if the position is held for long period of time. So if that is the case, it is important to regularly recalculate the TLT sensitivity to adjust the quantity of futures to hold in order to replicate the desired exposure.

 

Conclusions

The launch of these new contracts is definitely positive for those who want to have an almost 24h access to the US Treasury market but don’t want to have the large exposures offered by the existing futures.

As such, using these new futures to replace TLT or TMF in our strategies is a good option for those who prefer to trade futures and be able to close at any time of the day. Hedging existing TLT exposures while the cash market is close will also be made easier with these new contracts.

One simply has to calculate the number of futures to hedge the existing exposure and make sure this is rebalanced in case the yields have moved a lot.