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How Do Bitcoin Futures Work?

Sunday, January 21st, 2018 | Vance Harwood

The CME and Cboe/CFE, two large, well respected, USA regulated futures exchanges, recently started trading Bitcoin futures. These venues make it possible to trade on Bitcoin’s value without being exposed to the uncertainties of the mostly unregulated Bitcoin exchanges.

To understand Bitcoin futures you need to recognize, among some other things, that these futures are not in the business of predicting Bitcoin’s price.

Bitcoin Futures are Not Trying to Predict the Future!

It’s reasonable to assume that a product named a future is attempting to predict the future. For Bitcoin futures, this is definitely not what they deliver. The core utility of the futures markets is not predicting the future prices of their product but rather the secure delivery of a product at a known price, quality, and date. If there’s product seasonality (e.g., specific harvest times) or foreseeable shortages/abundances then future’s prices may reflect that but neither of these factors applies to Bitcoin.

I’m not saying that Bitcoin futures won’t be used by speculators making bets on Bitcoin—they certainly will be— but when you see Bitcoin futures trading higher or lower than the current Bitcoin exchange values (spot value) it’s not a prediction—it’s a reflection of the inner workings of the futures market.

How Are Bitcoin futures prices established?

If you look at the quotes for Bitcoin futures you’ll see at least three things, the expiration code (shorthand for a specific expiration date ) the bid (buy price) and the ask (sell price). If you’re ever confused as to which one to use in your situation it’s easy to sort out—start with the price that’s worse for you.

Important agents interacting with those prices are operating in one of three roles: individual speculator, market maker, or arbitrageur. A key role is market maker—a firm that has agreed to simultaneously act as both a buyer and seller for a specific security. When companies sign up for this role they agree to keep the bid/ask prices relatively close to each other—for example even if they aren’t keen on selling Bitcoins at the moment they can’t just set the ask price to an outrageous level. The agreed-upon maximum bid/ask ranges might be tied to market conditions (e.g., wider when deemed a “fast market”) and might allow time-outs but in general, the market maker agrees to act as a buffer between supply and demand.

The Market Makers

The existence of market makers (e.g., Virtu Financial) refutes a common assertion about futures—that there‘s always a loser for every winner, that it’s a zero-sum game. It’s true that derivatives like stock options and futures are created in matched pairs—a long and a short contract. If two speculators own those two contracts the profits on one side are offset by losses on the other but market makers are not speculators. In general, they’re not betting on the direction of the market. They act as intermediaries, selling to buyers at the higher ask price and buying from sellers at the lower bid price— collecting the difference.

Market makers are challenged in fast markets—when either buyers or sellers are dominating and prices are moving rapidly. When this happens market makers are obligated to continue quoting bid and ask prices that maintain some semblance of an orderly market. If they start accumulating uncomfortably large net long or short inventories they may start hedging their positions to protect themselves. For example, if they are short Bitcoin futures they can buy Bitcoin futures with different expirations or directly buy Bitcoins to hedge their positions. The hedged portion of the market maker’s portfolio is not sensitive to Bitcoin price movements—their profit/losses on the short side are offset by their long positions.

The market maker’s ability to hedge out their exposure demonstrates that futures aren’t inherently a zero sum gain. They can accommodate the market and still be profitable—regardless of the market’s direction.

The Arbitrageur

The arbitrageur is hyper-focused on the price difference between the Bitcoin future and the exchange price. If those prices differ enough they can lock in risk-free profits. You can imagine how much capital is available if risk-free profits are in the offing…

The arbitrageur very carefully calculates the costs of buying or shorting Bitcoin futures while selling short or buying actual Bitcoins.

These calculations include:

      • Time value of money required for margin deposits
      • Fees
      • Transaction costs (bid/ask spread)
      • Contract expiration settlement price risk (Bitcoin futures are cash settled)
      • Borrow costs for shorting Bitcoin if going short
      • The amount of profit that their bosses expect from them.

Normally commodity futures arbitrageurs have to account for things like storage costs (e.g. warehouses, silos), insurance (in-case the storage facility is robbed or burns down), and seasonal price variations but none of these apply to Bitcoin, so somewhat ironically the crazy Bitcoin market is simpler for them.

Knowing their estimated costs and profit requirements the arbitrageur determines a minimum difference they need between the futures’ prices and the spot price before they will enter the market. They then monitor the price difference between Bitcoin futures and the Bitcoin exchanges and if large enough they act to profit on that gap.  For example, if a specific Bitcoin future (e.g., February contract) is trading sufficiently higher than the current Bitcoin exchange price they will short that Bitcoin future and hedge their position by buying Bitcoins on the exchange. At that point, if they have achieved trade prices within their targets, they have locked in a guaranteed profit. They will hold those positions until contract expiration (or until they can cover their short futures and sell Bitcoins at a profit).

They’ll do the complementary transaction if the price of a specific future is enough lower than spot price. They’ll buy futures and short Bitcoins to lock in profits in that case.

Arbitrageurs provide a critical role in futures markets because they’re the adults in the room that keep futures prices attuned to Bitcoin exchange prices. If there are multiple futures providers (Cboe and CME in this case) they’ll also act to keep the futures from the various exchanges aligned with each other.

If Bitcoin futures prices get too high relative to spot arbitragers are natural sellers and if the futures prices get too low they are natural buyers. Their buying and selling actions naturally counteract price distortions between markets. If they’re somehow prevented from acting (e.g., if shorting Bitcoin was forbidden) then the futures market would likely become decoupled from the underlying spot price—not a good thing.

The Term Structure

A key attribute of a futures market is how its contract’s prices vary by expiration date. The succession of futures prices over time is called the “term structure”. If supply is stable (no seasonality or shortages) then typically futures prices will increase with expirations further in the future. This term structure configuration is called “contango” and it accounts for the fact that carry costs (e.g., time value of money) and profit expectations increase with time. Unless there are big changes in interest rates or the way that Bitcoin exchanges work I expect the level of contango in the Bitcoin futures term structure to be small. Bitcoins don’t cost much to hodl (once you have your hardware wallet) and there’s no apparent seasonality. The chart below from VIX Central shows a typical Bitcoin term structure (click on chart to get current data):

Cboe vs CME: Sizes & Settlement

There are two USA regulated Bitcoin futures exchanges in operation. The CME’s contract unit is five Bitcoins whereas the Cboe’s contract unit is one—that’s the biggest difference between these futures. The upfront money to buy or sell short a CME contract will be about five times higher than the Cboe contract. Larger investors won’t care but this will be an issue for smaller investors. Another difference is the spot/settlement process that the exchanges use. In the case of Cboe futures, the contracts will be settled to a 4 pm ET Gemini exchange auction price on the day of expiration, for the CME futures the settlement price is a complex calculation using an hour of volume weighted data from multiple exchanges (currently Bitstamp, itBit, Kraken, and GDAX). With the CME’s approach, it will be harder to manipulate the settlement price but it doesn’t give arbitrageurs a physical mechanism to trade their positions—possibly an issue.

There’s nothing to prevent people from closing out their contracts before final settlement but typically there is some premium remaining until the very end.

Unlike many commodity futures, Bitcoin futures are cash settled rather than physically settled.  Cash settlement is a relatively new development in futures trading, first introduced in 1981 for Eurodollar futures, that addresses the problem of how to settle futures contracts on things that are difficult/impossible to deliver physicially—things like interest rates, large stock indexes (e.g., S&P 500), and volatility indexes (Cboe’s VIX).  Futures physical settlement involves actual shipment/change of ownership of the underlying product to the contract holder but in practice, it’s rarely used (~2% of the time).  Instead, most organizations that are using futures to hedge prices of future production/usage will make separate arrangements with suppliers/customers for physical delivery and just use the futures to protect against contrary price changes.  In practice, the final settlement price of the contract can be used to provide the desired price protection regardless of whether the futures contract specifies physically delivery or cash-settlement.

While “physical” delivery of Bitcoins as part of a futures contract would certainly be possible it raises regulatory and security issues in today’s environment where the cybercurrency exchanges are mostly unregulated, somewhat unreliable, and theft due to security hacks is distressingly common.  By selecting cash settlement the CME and Cboe completely avoid the transfer of custody issues and shift those problems to somebody else—namely the market makers and arbitrageur.


One traditional attraction of trading futures is the ability to use relatively small amounts of money to potentially achieve outsized returns. In many futures markets the margin, the amount of money that your broker requires up-front before executing the trade can be quite small compared to the ultimate value of the contract. For example, as of 22-Dec-2017, each E-mini S&P 500 contract was worth $134K ($50*S&P 500 index value)—this “list price” of the contract is called its notional value.  The CME only requires you to maintain a minimum margin of $4.5K (3.4% of notional) to control this contract (brokers often require additional margin). Margin requirements this low are only possible because the volatility of the S&P 500 is well understood and your margin account balance is adjusted at the end of every trading day to account for the winnings or losses of the day. If your account balance falls below the margin minimum of $4.5K you’ll need to quickly add money to your account or your position will be summarily closed out by your broker. On the plus side, if you’ve predicted the S&P’s direction correctly your profits will be that same as if you completely owned the underlying stocks in the index. A +1% daily move in the S&P500 would yield $1340 in profit even though you only have $4500 invested— a 29% return—this multiplier effect is called leverage.

Currently, Bitcoin futures have very high margin requirements. The Cboe requires 40% of the notional amount for maintenance margin, the CME requires 43%. Your broker will likely require more than that. The culprit behind these high requirements is Bitcoin’s high volatility—until that calms down the exchanges will protect themselves by requiring a bunch of up-front money. If you don’t come up with the money for a margin call they want to close out your position without leaving a negative balance.

Because of the high margin requirements, Bitcoin futures don’t offer much leverage compared to just buying Bitcoins outright. However, Bitcoin futures do offer the trader time-tested exchanges that are not nearly as susceptible to hacks, thefts, and unscheduled downtime.


In the movie “Trading Places,” there’s a wild scene where fortunes are made and lost in the orange juice future pit in a matter of minutes. This scene epitomizes what most of us envision futures trading to look like. The movie depicts a situation where the supply of oranges from the next harvest is unknown—and that is the source of the craziness.

Bitcoins don’t have seasonal variabilities—supply is a known quantity. This supply stability makes Bitcoin futures a lot less dramatic but in the case of Bitcoins this is a real plus—there’s already plenty of drama in the exchanges—the futures market will be the safe and quiet space. A different sort of trading places…

Free Bitcoin/Bitcoin Futures/GBTC Quotes, Charts, & Historical Data

Wednesday, December 27th, 2017 | Vance Harwood

Finding quotes and historical data for Bitcoin, Bitcoin futures, Bitcoin options can be an adventure.  Below I’ve assembled links to the online resources that I’ve been able to find.

In many cases, data is available from multiple sources.  I did not attempt to list all of them.

BTC Quotes

BTC Charts

 BTC Historical Prices

BTC Futures

  • CBOE/CFE  Symbol: XTB
    • Quotes
      • Spot: Gemini exchange
      • Cboe (including bid/ask)
        • Use “Enter Symbol” Field
        • Ticker construction:  XBT/ followed by month-year code (e.g, XBT/F8 = January 2018 expiration)
          • F=Jan, G=Feb, H=Mar, J=April, K=May, M=June, N=July, Q=Aug, U=Sept, V=Oct, X=Nov, Z=Dec
      • Cboe/CFE  (all months)
    • Historical Data  CBOE
    • Term Structure  Chart:  VIX Central/bitcoin
    • Reference exchange:  Gemini
    • Contract Size:  1 Bitcoin, cash settled  (Contract Specifications)
    • Initial customer margin 44%
      • Brokers may require much more margin ( e.g., Interactive Brokers is requiring $40K per contract–around 130%–on short contracts)
    • Trading Hours (USA Central Time)
      Type of Trading Hours Monday Tuesday – Friday
      Extended 5:00 p.m. (Sunday) to 8:30 a.m. 3:30 p.m. (previous day) to 8:30 a.m.
      Regular 8:30 a.m. to 3:15 p.m. 8:30 a.m. to 3:15 p.m.
    • Future settlement quote (once per day)
    • Option availability:  Earliest probably around March 2018


  • CME  Symbol: BTC
    • Quotes
    • Reference exchanges:  Bitstamp, GDAX, itBit, Kraken (combined into a composite quote via BRTI & BRR)
    • Contract Size:  5 Bitcoins, cash settled  (Contract Specifications)
    • Initial customer margin 35%
      • Brokers may ask for much more than this, especial for short contracts
    • Trading Hours:  Scheduled to start trading 18-Dec-2017
      CME Globex: Sunday – Friday 6:00 p.m. – 5:00 p.m. (5:00 p.m. – 4:00 p.m. CT) with a 60-minute break each day beginning at 5:00 p.m. (4:00 p.m. CT)
      CME ClearPort: Sunday – Friday 6:00 p.m. – 5:00 p.m. (5:00 p.m. – 4:00 p.m. CT) with a 60-minute break each day beginning at 5:00 p.m. (4:00 p.m. CT
    • Future settlement quote: BRR (once per day)  methodology) )   Historical Data 

BTC Mutual Funds

  • GBTC  Bitcoin Investment Trust  (A closed-end mutual fund)  Available for trading in regular brokerage accounts and IRAs
    • Shares outstanding & Bitcoin holdings GBTC  (as of 27-Dec-2017  1868700 shares,  171,796 Bitcoins)
    • Quotes Bloomberg  Yahoo 
    • Historical Data  Yahoo
    • Tracking Error with BTC
      • As of 27-Dec-2017  GBTC had premium over BTC of +57%   (GBTC price / NAV as reported by Bloomberg)

BTC Options & Swaps

  • LedgerX offers Bitcoin settled options
    • Their options are “physically” settled with Bitcoin
    •  To participate if appears you must be an institution or a high net worth ($10 million+) individual
    • See this page for a currently listed options and swaps


A Better Way to Model the VIX

Tuesday, November 28th, 2017 | Vance Harwood

Models are useful. They help us understand the world around us and aid us in predicting what will happen next. But it’s important to remember that models don’t necessarily reflect the underlying reality of the thing we’re modeling. The Ptolemaic model of the solar system assumed the Earth was the center of everything but in spite of that spectacular error, it did a good job of predicting the motions of the stars, planets, moon, and sun. It was the best model available for over a thousand years. But new data (e.g., phases of Venus as revealed by Galileo’s telescope) and errors in predicting the motions of the planets demonstrated that the sun-centric Copernican/Kepler models were superior.

There are a lot of models for the Cboe’s VIX. None of them are particularly good at predicting what the VIX will do tomorrow but they can be useful in predicting general behaviors of the VIX. The most popular model for the VIX (although people might not recognize it as a model) is simple mean reversion.

 Simple Mean Reversion

Car gas mileage is a good example of a simple mean reverting process.

Over time your car’s gas mileage will exhibit an average value, e.g., 28 miles per gallon. You don’t expect to get the same mileage with every tank because you know that there are factors that make a difference with your mileage (e.g., city vs highway driving, tire air pressure, and wind direction) but over time you expect your mileage to cluster around that average value. If you get 32 miles per gallon on one tank of gas you reasonably expect that next time you check it will likely be closer to 28. If the values start varying significantly from the average you start wondering if something has changed with the car itself (e.g., needs a tune-up)

A mean-reverting random walk is a relatively simple model and fits some of the basic behaviors of the VIX. Specifically, over time the mean value of the VIX has stayed stable at around 20 and the VIX exhibits range bound behavior—with all-time lows around 9 and all-time highs around 80.

However, there are many aspects of the VIX that aren’t well explained by a simple mean-reverting model. For example, a simple mean reverting process will have its mode value (the most frequently occurring values) close to its mean. This is not the case of the VIX; its mode is around 12.4—a long way away from its mean. The histograms below show that difference visually.


Another VIX behavior that departs from a simple mean-reverting process is the abrupt cessation of values below 9—almost a wall. For a normal mean reverting process you would not expect such a sharp cut off at the low end.

The Acid Test:  How good is the model for predicting the future?

Having a good model for a process is useful because it can help us predict at least some aspects of the future. For example, we can use our average gas mileage to decide whether we need to gas up before entering a long stretch of highway without gas stations.

A simple mean-reverting model is not particularly good at predicting the future moves of the VIX. If the VIX is low (e.g., 12 or below) a simple mean-reverting model predicts that since the VIX is far from its mean that will likely increase soon. But history shows this is usually not the case. Often the VIX can be quite content to hang around 12. This leads to news stories quoting various pundits stating “The VIX is broken” –when in reality they are just using an inferior model.

 As I said earlier there are VIX models out there that address some of these deficiencies. Unfortunately, the ones I know of are complex and not very intuitive. I believe the model that I describe below can improve our intuition considerably without adding too much complexity.

A Better VIX Model

A better way to view the VIX is that it behaves like the combination of two interacting processes: a specialized mean reverting process and a “jump” process. The jump process captures the behavior that all VIX watchers know—its propensity to occasionally have large percentage moves up and down. Since 1990 there’ve been over 86 times where the VIX has increased 30% or more in a 10 day period. The occurrence of these spikes is effectively random with a probability of happening on any given day of around 1.28%. It’s like a roulette table with 78 slots, 77 of them black and one red. If the ball lands on a black the normal reigns—if red then things get crazy. The graph below shows a histogram of the number of days between these 30% spikes in the VIX since 1990.

There’s nothing that prevents reds on consecutive spins nor is there some rule that reds become really likely if you haven’t had a red in a while. The roulette ball has no memory of where it landed on previous spins.

VIX jumps are generally not just one-day events; subjectively it looks like it takes around two weeks before the market reverts to more typical behavior. The model assumes that when a jump occurs it essentially drives the behavior of the VIX for 10 trading days.

The other process, the specialized mean reverting process, addresses the non-jump mode of the VIX—which is historically around 85% of the time. One of the key behaviors it needs to address is the slow relaxation in the mean value of the VIX after a big volatility spike rekindles a generally fearful attitude in the market. This decay process continues (unless interrupted by another VIX jump) until the average monthly VIX values drop into the 11-12 range.

The chart below illustrates this relaxation process.


This characteristic can be modeled by expecting the short term mean of the VIX (when it’s not jumping) to decay exponentially until it reaches its “quiet” mean of around 11.75. It works well to quantify this decay as having a time constant of 150 days.

With this approach, sans jumps, the difference between the current VIX value and its long-term quiet value will decay by 50% in 104 days. So if the VIX is at 30 the model predicts the mean will decline to 20.75 in 104 days [30- (30-11.5)*0.5=20.75].  If there are no jumps for the next 104 days the VIX’s mean would decline to 16.13. If a jump occurs in the interim the short term mean is reset to the VIX’s value at the end of the jump.

The other part of the specialized mean reverting process mimics the day-to-day volatility of the VIX. I used a formal mean reverting diffusion process (Ornstein-Uhlenbeck) to accomplish this. Despite its scary name, you can think of it as a random walk with the thing “walking” being attached to the mean with a spring—similar to walking a dog with a springy leash. The further the dog gets from you the larger the force pulling the dog back to you.

Unlike the simple mean-reverting model often used for the VIX, this process has a much tighter distribution, with the extreme values effectively limited to around +-20% from the mean. When the VIX is quiet this process replicates the firm lower limit on the VIX, a VIX of 9 is -21.74% lower than a quiet mean of 11.75.

Simulating the Model

 To implement/validate this model I estimated the key input variables and then used Excel to simulate 20-year volatility sequences. I then compared these time series to the actual VIX history and tuned the model’s parameters such that the key characteristics (e.g., volatility, mean, mode, decay rates) were similar to the VIX’s historic values.

Resulting histogram of historic VIX values vs the simulated combined process


The next chart shows an example 20 year time series of the simulated VIX combined process compared to the historic VIX. The two series aren’t time synchronized; my intent is to show how the simulated VIX time series has the same visual feel as the real VIX.



This improved model is not a path to riches. It isn’t any better than other models at predicting when VIX jumps will occur. However, this model does help us understand how the VIX behaves over longer time spans. In particular, during times of sustained low volatility, it predicts that the VIX will tend to stay low until the next significant VIX spike and not trend up like the simple mean-reverting model demands.




Quant Corner

  • The mean-reverting diffusion process used is an Ornstein-Uhlenbeck mean reverting diffusion process using a log-normal distribution. The volatility was set at an annualized 112% and the return to mean strength parameter ETA set to 0.3. The mean of the process is determined by the previous day’s VIX value minus the exponential decay factor that will decay the mean down to 11.75 over time if there are no additional jumps (Tau of 150 days). If the mean has decayed down to 11.75 the process acts very similarly to the VIX’s low volatility regimes (e.g., 2004-2006, 2016-2017) with the “return to mean” factor effectively acting to keep the VIX  higher than 9.0
  • The Jump process used (with a few small tweaks) is a compound Poisson process where the probability of a jump sequence is random with a probability of a jump being 1.28% per day. The jump sequence and its daily amplitudes are determined using a technique borrowed from rappers called sampling. Instead of trying to recreate the decidedly non-Gaussian distribution of VIX jumps I reused historic VIX jumps by randomly selecting, and replaying one of the more than 85 jump sequences since 1990 where the VIX jumped more than 30% in 10 days. Each jump sequence is 10 days long, with the first 2 days being the behavior before the jump.

One Reason Why the New VIX Calculation is Better

Thursday, November 30th, 2017 | Vance Harwood

The CBOE changed the way the VIX® was calculated on October 6th, 2014—asserting the change would provide a more accurate assessment of expected volatility.  The new process does look better to me, but I’ve been surprised that the new VIX and the old VIX (listed as VIXMO) sometimes differ by as much as plus/minus 10 percent.

Disagreements between the two indexes are not due to only one factor, but clearly one improvement was to eliminate the week of the month where the VIX was calculated using extrapolation.

The VIX provides a 30 day estimate of volatility, but the S&P 500 options (SPX) used in the VIX calculation have fixed expiration dates.  The CBOE transforms the options’ data into the VIX by using volatility specific interpolation / extrapolation.  For example, if you have a volatility number for options that expire in 10 days and another for options that expire in 38 days you can reasonably assume that the VIX level should be between those two numbers.

One requirement with the old VIX calculation was that it couldn’t use options with less than 7 days to expire.  When the 7 day restriction was reached the calculation switched to using options that had more than 30 and 58 days until expiration.  The chart below shows the calculation right after that switch on January 12th, 2015.


The blue and red bars are the volatility numbers (VINMO & VIFMO) from the S&P options and the green bar is the VIX calculation extrapolated from those two numbers.  For details on that process see: Calculating the VIX Index—the Easy part

Normally this extrapolation was reasonable, but if the market is nervous the shorter term volatility values climb.  January 12th, 2015 was a case in point—notice how the light blue VXST bar (9 day volatility) is higher than the VIX estimate.

The next chart shows the new and old VIX calculations together.  The black dotted line shows the interpolation used for the new VIX calculation; the blue dotted line shows the old VIX extrapolation. The black vertical bar shows the VIX estimate.

VIX cacl-graph

Clearly the two calculations have a major difference of opinion —the new calculation’s result is 7.5% higher (19.60 vs. 18.15).

The CBOE’s new calculation always uses options that expire within a week of the VIX’s 30 days.  The red and orange triangles show their values on January 12th.

The old VIX calculation misses the fact that the shorter term volatility has ramped up.

The CBOE provides VIX style calculations on six different sets of options used in their VIX, VIXMO, and VXST calculations.  These calculations don’t need time extrapolations / interpolations so that source of potential errors is eliminated.   The chart below shows how they mapped out on January 12th, 2015 (click image to enlarge).

vix term c+m

The green dots on the top line represent the CBOE VIX style volatilities over time.  The vertical black bar shows the old VIX calculation and the purple outline above it shows the new VIX calculation.

In this case the new VIX calculation is more accurate, cleanly mapping into the overall volatility term structure.


Additional Resources:

Weekly Options Take Charge

Friday, March 10th, 2017 | Vance Harwood

The volume of CBOE’s Weeklyssm options has grown rapidly since they expanded their listings into equities and Exchange Traded Products in June 2010.  Now weekly options comprise almost 30% of the CBOE’s average daily option volume.  The list of available weekly options is available on the CBOE website.

Among other things option traders take advantage of the Weeklys to position themselves for earnings releases,  harvest rapid premium decay near expiration, and place low cost directional plays.

Three recent press releases suggest that the Options Clearing Corporation (OCC) and the CBOE are moving to the next phase—making up to 5 weeks of options available on popular securities and moving existing options to look more like the Weeklys.  The specific moves are:

  1. Five weeks of Weekly options for many securities (press release)
    •  Initially Weekly options were only made available 9 days before their expiration.  If you needed a later expiration date your only choices were monthly options with their 3rd Saturday of the month expiration, or in some cases quarterlies.    In 2013 the CBOE started making SPX options available with weekly expirations 5 weeks in advance.   Evidently encouraged, they rolled out additional weekly expirations for additional  indexes and stocks (e.g., SPY & AAPL).    Overall I think the advantages of a more regular set of dates will outweigh the  problems with spreading option volume across more option classes.
  2. Friday afternoon expiration for most monthly options
    • The OCC announced a plan to change the expiration date for monthly options—to align with the Weeklys.  Instead of expiring on Saturday, they would expire at the end of trading Friday.   The Saturday expiration always seemed awkward to me, causing confusion on theta calculations and exposing investors to weekend news events.  I suspect it’s a throwback to days when paper actually had to be shuffled to close things out.  This change, planned for February 2015, would render the 3rd Friday of the month options indestinguishable from Weeklys.
  3. Rationalizing ticker symbols with SPX options (press release)
    • There are  three different tickers for SPX options,  SPX, SPXW, and SPXPM. Unlike other options there are weekly options (PM settled) on the same week that the monthly (AM settled) expire.

In general the move to weekly options has been gradual and non-invasive.   One of the side benefits of the rise of the SPX weeklys is that now there are always options series that closely bracket the 30 day volatility window of the VIX calculation.  Using the monthly SPX options there were sometimes longish extrapolations required with suspect accuracy. In October 2014 the CBOE switched the VIX calculation methodology to take advantage of the SPX weeklys availability.   Ultimately this new VIX calculation was needed to support VIX Weekly futures and VIX Weekly options.