Calendar Calculations

Trading can be challenging especially when trying to manage the heat of a moment. In reading lots of material and hearing even more discussion, I find people rarely discuss actual trading details. As opposed to “optional activities,” trading activities include: reading and manipulating charts, running stock/option screens, recording trades, modeling positions, entering orders, and keeping abreast of what needs to be executed when. Perhaps the most important trading activity is the application of simple arithmetic.

Here is my spreadsheet from my last calendar trade:

The “Premium” column shows prices. First, I bought two 1240 put calendars for $7.60 each. I then rolled one to the 1260 call strike for $0.84 resulting in a double calendar. If I want to exit for a 10% profit, what price do I require to close?

At first I thought this was simple: ($7.60 + $0.84) * 1.1 = $9.29 [1]

Wrong. As a combination of two calendars, the double calendar price should be much higher.

Perhaps the answer is ($7.60 + $0.84) * 1.1 * 2 = $18.57 [2]

Wrong again.

Let’s look at the “Amount” column to see if we can figure it that way.

$1,522.70 + $86.44 = $1,609.44 [3]
$1,609.44 * 1.1 = $1,771 [4]
$1,771 / 100 = $17.71

Check:
Profit = $1,771 – $1,609.44 = $161.56
$161.56 / ($1,522.70 + $86.44) = 0.10, or 10%.

Yay!

What was so hard about this? Didn’t I do the same thing with [3] / [4] as I did with [2]?

If I overlay [2] and [3]:

($7.60 + $0.84) * 1.1 * 2 = $18.57 [2]
$1,522.70 + $86.44 = $1,609.44 [3]

How are these different?

The two calendars [2] are $7.60 * 100 * 2 = $1,520, which is about $1,522.70 [3] without transaction fees.

Rolling one for $0.84 [2] should be $0.84 * 100 * 2 = $168, which is not $86.44 [3]. Why?

This is the mistake. The initial cost of $7.60 must be multiplied by two to account for both calendars. The subsequent cost of $0.84 is the entire roll of four contracts traded: no need to multiply by two.

In fact, if I take half of the roll debit and calculate:

($7.60 + ($0.84 / 2)) * 1.1 * 2 = $17.64

If I multiply that by $100/contract then I get $1,764, which is about equal to $1,771 [4] minus transaction fees.

Trading platforms usually do not track running profit/loss of positions. As traders, we must have a reliable means to do this simple arithmetic if we want to make money rather than lose it.

No experienced trader ever said this was easy; here is another House advantage the markets use to take our money.

Correlation Confound (Part 3)

Correlation confound #1 dealt with portfolio diversification.  Today I will discuss correlation confound #2, which has to do with pairs trading.

To design a pairs trade, the first step is said to be finding stocks that are highly correlated. This could involve businesses in the same industry or sub-sector, index ETFs like QQQ and SPY, highly-correlated futures pairs, etc.

The next step is to monitor a chart showing price ratio between the two markets. When the price ratio diverges far enough, I want to go long (e.g. sell an OTM put spread) the relatively cheap market and go short (e.g. sell an OTM call spread) the relatively expensive market. Since the two markets generally move together (i.e. highly correlated), when they move apart I should expect mean reversion where they come together again.

Correlation confound #1 also applies to pairs trading.  Just because markets have been correlated in the past does not mean they will be correlated in the future.  That could cause problems for pairs trades.

Correlation confound #2 is something called cointegration.  Correlation measures the tendency of two variables to move together but it’s not guaranteed that they’ll stay close to each other. Look at the following charts of the gold and silver markets:

Believe it or not, in both charts these markets have the same statistical correlation of +0.75!  If you took on a traditional pairs trade when these markets were far apart with the expectation that they would get closer together, you might be sorely disappointed with the graph on the right but happy with the graph on the left.

Cointegration is a measure of how well assets are tied together. Two correlated variables are allowed to wander arbitrarily far apart but a cointegrated variable is not.  Cointegrated variables are expected to stay parallel to one another since the difference between the two will be corrected over time.

Correlation Confound (Part 2)

In the last post, I defined both words in the title. Today I continue by describing the correlation confound of portfolio diversification.

Combining assets with low correlations in a portfolio may allow me to get more return while taking on the same level of risk. It may also allow me to get the same return with less risk. This is diversification.

Risk, or variability of returns, is what causes people to close positions for the worst possible losses.  Averaging +10% per year is great for a portfolio but if, at some point during the year, you were down 80% then would you still be in the market? In 2012 I described this scenario in terms of maximum adverse excursion. Diversification helps to lower risk and while that may lower returns as well, if it can keep me in the trade mentally then time has repeatedly been shown to work its magic and allow the market to rebound.

To build a diversified portfolio, we are advised to look for assets whose returns have not historically moved in the same direction. What I do not see in most of these discussions is the fact that correlation can change.  Jim Fink addressed this in a 2013 article:

> A large portion of the disappointment can be traced to
> the severe bear markets… when correlations among asset
> classes increased markedly at the worst possible time,
> resulting in all declining in price at the same time.
> [Mebane] Faber uses 2008 as a prime example:
>

>   "The normal benefits of diversification 
>    disappeared as many non-correlated asset 
>    classes experienced large declines 
>    simultaneously. Commodities, REITs, and 
>    foreign stock indices all suffered 
>    drawdowns over 50%."

>
> If only there were a way to avoid exposure to risk assets
> during the most severe bear markets, the problem of
> converging correlations could be avoided and the
> diversification benefits of different asset classes with
> normally low correlations could be fully realized . . .

Correlation confound #1 is changing correlations. If this happens then your best efforts to diversify and minimize losses may not be effective.

Correlation Confound (Part 1)

Correlation is mentioned as a key factor in two different trading/investing contexts. In this mini-series, I’m going to describe correlation, how traders can make use of it, and a couple missing pieces (confounds) to avoid unexpected failures.

I will begin by explaining both words in the title.

Correlation is a measure of how often two variables change together. A correlation of +1 between two stocks means historically, when one stock was up 5% the other was also up 5%. A correlation of -1 means historically, when one stock was up 5% the other was down 5%. A correlation of zero means historically, no relationship between the stocks’ price changes occurred. Correlation can range from -1 to +1.

In science, a confounding variable is “an extraneous variable in an experimental design that correlates with both the dependent and independent variables.”

Ice cream [example] can better help me illustrate this. Suppose a correlation between murder rates and ice cream sales is observed. If murder rates go up (down) when ice cream sales increase (decrease) then ice cream sales drive murder rates, right? This is less likely if some other variable is also found to be correlated with murder rates. That variable would then confound our initial model. Suppose it is also observed that as seasonal temperature increases (decreases), people buy more (less) ice cream and spend more (less) time outdoors where criminals run the streets. It makes logical sense for seasonal temperatures, not ice cream sales, to affect murder rates. Seasonal temperature is a confounding variable.

In the next post I will start to explain confounding variables that prevent correlation from doing its job.

Protect Your Nakeds!

I subscribe to a mailing list that focuses on covered call writing. I often see people fail to acknowledge risk and it makes me a bit uneasy.

This was taken from a 2014 post:

> I will be happy to buy QCOR at 65 if it is put to me.
> Based on reading many of the QCOR reviews I believe
> QCOR is fundamentally very strong… If put to me I
> appreciate Citron’s work that adds to volatility and
> will reward me if and when I sell a CC for a high
> premium.

People short puts on Enron or any other stock that ultimately went bankrupt said the same thing before it went to zero. When the stock is well on its way to zero, no stockholder, naked put trader, or covered call writer will be happy.

Some people use explicit wording instead of a smug “I’ll be fine because I feel comfortable owning the shares” attitude. An example is often given by people advertising covered call services or option educators who say “sell the put at a strike price at which you would be happy to purchase the stock.”

I think either of the above clouds the real issue: risk management.

The bottom line is if I take assignment on a naked put then I’m probably losing money. If I don’t have an exit strategy then I better hope to high heaven the stock doesn’t go to zero! Regardless of how “happy” I say I’ll be if assigned, I’ll feel more and more heat if the stock continues to slide further. If the stock tanks substantially and then trades sideways then I will be highly frustrated sitting on what feels like dead money because I won’t be able to collect meaningful premium on the [now] deep OTM calls.

The Myth of “Unusual Option Activity” (Part 1)

The financial media often treats unusual option activity as a predictor of how large institutions are trading. This may or may not be very misleading, which in my view casts doubt over the entire strategic approach.

Traders will sometimes study the option flow for a stock because they believe this helps them to understand sentiment. Particularly when a stock is extended, some believe option activity can offer predictive signals. For example, if one large block is going against the trend, it can mean the institutions are starting to think trend reversal. On the other hand, increased small-lot activity in the direction of the trend conveys a message that can fool the beginning trader.

What options are predictive of trend persistence versus trend reversal is not at all obvious. For example, heavy put volume on a beaten down stock could be a trader(s) shorting puts in expectation of mean reversion: either reversal to the upside or at least a temporary reprieve before the downtrend resumes. If this trader(s) is also shorting stock against the put sale then the overall position is actually bearish rather than bullish. The trader(s) took advantage of the high implied volatility to collect some extra premium in the hedge but the primary profit generator is downward stock movement (upside has unlimited risk).

Married puts and covered calls are two other examples of misleading option activity. Buying puts in the former suggests a bearish position but having purchased the stock, this is really a bullish position with downside protection. Selling calls in the latter suggests a bearish position but having purchased the stock, this is also a bullish position with [limited] downside protection. In fact, if the stock is purchased before the option is traded then the stock price may be higher to allow for a cheaper put purchase or more expensive call sale: both advantageous to the combined position.

I will conclude with the next post.

Using Implied Volatility to Screen for Option Trades (Part 3)

Last time I presented a generic screen to identify high implied volatility percentile candidates. Today I will conclude discussion on this strategy.

Criterion #3 searches for stocks with a minimum average true range. Average true range is a measure of stock price movement in relation to the previous close. Generally stocks that move very little have options that are very cheap. While a tradeoff exists, profit potential is generally limited when selling cheap options.

Criterion #4 searches for stocks with current implied volatility (IV) percentile in the top 5% of their 12-month IV range. Being mean-reverting, according to theory, IV will be likely to drop in the near future. Short options will profit when IV falls.

Once the screening is complete, the next step is to inspect the price charts of stock candidates. Any stocks that are at solid support levels are candidates for short puts or put credit spreads. Any stocks that are at strong resistance levels are candidates for short calls or call credit spreads. Stocks that seem range-bound, or trading sideways, are candidates for iron condors or naked straddles/strangles.

For each stock that turns up on the screen, we must be sure to identify the earnings announcement date. IV tends to peak just before earnings are announced. After the announcement, IV crashes. This is good for a short premium strategy. However, stocks tend to make big price moves following earnings announcements too. Depending on how much price risk a proposed trade has, we may want to avoid earnings announcements to decrease risk of earnings-induced price shocks.

Finally, look for news that might signal reason for high IV percentile. Pharmaceutical stocks (especially biotechnology) are notorious for big price moves related to FDA approvals or bans. Similar to earnings announcements, we may want to avoid placing trades if an FDA decision is imminent.

Using Implied Volatility to Screen for Option Trades (Part 2)

Since many people believe IV is mean-reverting, I discussed the idea of generating trade ideas by looking for stocks at IV extremes. Today I will continue that discussion.

We want to look for stocks with high IV. Once we have found these candidates, we can then plan trades that take advantage of an anticipated IV drop from these extremely levels back toward average levels.

Here is a key point to differentiate, though: we want stocks with high IV relative to their own average IV as opposed to high on an absolute basis (i.e. compared to other stocks). I will call this IV percentile: where current IV falls within the low-high IV range over the past year. IV percentile of 100 (0) means current IV is at its highest (lowest) level over the past 12 months.

We can also invoke that second trade consideration and determine whether we have a forecast for the underlying stock price. If we are wrong with IV forecast then we might still make money if we are right on stock movement (and vice versa). For the latter, we would look to use bullish or bearish premium-selling strategies (e.g. naked options or credit spreads) if we are decidedly bullish or bearish, respectively. If we believe that price will remain in a range then we can use strategies that are short premium to the upside and downside: short straddles, short strangles, or iron condors.

Any option scanning tool should allow us to scan for IV. You can choose whatever specific criteria you like. Since I have no reason to think any one set of criteria will perform better than any other, here is a generic screen:

1. Last stock price at least $15 (low-priced stocks may have wide strike increments)

2. Average daily volume of the underlying > 1,000,000 (liquidity requirement)

3. Average true range of price between 1-8%

4. IV Percentile > 95

I will conclude with the next post.

Using Implied Volatility to Screen for Option Trades (Part 1)

Today I will discuss one approach to trading options: screening by implied volatility (IV).

The sheer volume of option trading possibilities can be overwhelming. To find good trading candidates, I need to keep in mind the three sources of option profits: price movement of the underlying stock, option supply and demand (IV), and time decay.

Time decay is largely a function of option supply and demand. As demand for options increases, option prices increase. IV measures how expensive options are in terms of expectations for the underlying stock movement. Higher-priced options have more value to lose over time. This decay is value lost by option buyers and value gained by option sellers.

Profitability is therefore a function of two main factors: price movement of the stock and supply/demand for the options. To make money with an option trade, I can either look for stocks whose prices I can predict or look for stocks whose IV changes I can predict. When I choose stock price change or IV as the primary consideration, IV or stock price change will be the secondary consideration, respectively.

Some people believe changes in IV are easier to forecast than directional movement of the stock. IV is believed to have strong mean-reverting tendencies. Whether or not this is true is something we could research (topic for another day). For now, though, it will be sufficient to say those in this camp believe IV to oscillate around an average value and return to that average quickly when IV strays too far away.

Based on this philosophy, screening for stocks that are at higher-than-average IV levels should be a good source of option trading ideas.

I will continue discussion of this trading approach in the next post.

Why Options? (Part 6)

Why options?

I hope the content presented in this blog series changes that question to why not?

Many points of contact from the financial industry deprecate options and their usage. I suspect much of this has to do with historical financial crises where option expertise/trading was involved.

Clearly, though, options have advantages along with the disadvantages.

At the very least, with education and experience trading options can be profitable just like trading stocks.

For some, additional flexibility to follow the trend with options is the deciding factor.