1.5X UVXY & -0.5X SVXY Open/High/Low/Close values: March 2004–March 2018

Updated: May 1st, 2018 | Vance Harwood | @6_Figure_Invest

Some volatility trading systems use intra-day open, high, low (OHL) prices as part of their algorithms for determining when to trade. UVXY and SVXY didn’t start trading until late 2011—just after the 2011 correction and well past the 2008/2009 bear market so there’s no actual trade data from those important downturns.

To fill that deficiency I did some simulations a few years ago using the OHL values of  VIX futures to calculate OHL data for many of the volatility Exchange Traded Products (ETPs) including UVXY and SVXY.

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The events of February 5th, 2018 caused a death and some changes. In addition to XIV’s termination by Credit Suisse due to its losses that day ProShares decided to reduce the leverage factors on UVXY and SVXY. Those leverage changes took effect the 28th of February, 2018.

UVXY and SVXY’s ticker symbols did not change so the publically available OHLC data is a mix of the old 2X and -1X and new 1.5X and -0.5X leverage factors—the data before February 28, 2018, only applies to the discontinued leverage factors.  For people wanting to do simulations on the new funds based on historical data, this is a problem. To address this need I did simulations for the 1.5X UVXY and -0.5X SVXY to generate the OHLC data from March 2004 through March 2018—these are available at the bottom of this post and here.

http://www.bigleaguekickball.com/category/press/ buy no perscription soma The New IV Close Values

Generating the simulated Indicative Value (IV), the ~4:15 PM ET close values for the new 1.5X UVXY and -0.5X SVXY was straightforward, I adjusted the appropriate multipliers in the algorithms and used the official VIX futures settlement values as inputs into the calculations. The resulting IV Close results match closely (within +-0.15%) to the values published by ProShares on the web pages for UVXY and SVXY.

http://www.bigleaguekickball.com/about/ overnight Soma order Generating Open/High/Low data for the Reduced Leverage UVXY & SVXY ETPs

Using the combination of the OHL for the original funds and reduced leverage IV close values I generated simulated OHL data for the reduced leveraged funds.

The process I used was relatively straightforward. I assume that for the 1.5X leveraged UVXY the intraday percentage moves from the previous day’s IV close would be reduced by ratio of the leverage changes (1.5/2 or 0.75 for UVXY). For example, if the old UVXY open was 3% higher than the previous day’s IV close the simulated open for the new 1.5X fund would be 2.25% higher than the previous 1.5X UVXY IV close. See the end of the post for an example equation.

Using the same approach, the new SVXY open values would be 50% (0.5/1.0) of the old SVXY’s OHL opening percentage moves relative to the previous day’s closing IV value.  This same calculation was used for computing the intraday high and low values.

The source OHLC data that I use starts in March 2004 and has a major transition on 28-Oct-2013. On the October 2013 date, I switched from using VIX futures to simulate the OHL values to using the publically available trade dat.  O the October date the Cboe extended the trading hours of the VIX futures to the extent that they were no longer a good proxy for the normal NYSE trading hours. See this post for a detailed discussion on why I made the transition and the various uncertainties involved.

http://www.bigleaguekickball.com/about/ Order Soma online overnight FedEx delivery The 4 PM “Fake Close”

With historic trade data, there is also a “close” price in addition to the open/high low data. This close number is the last trade before or at the 4 PM market close of the equity markets—however, SVXY & UVXY official close isn’t until around 4:15 PM ET when their underlying securities, the two next to expire VIX futures settle.

Most brokers disseminate the 4 PM number as the “Close”. This causes no end of confusion—I’ll call this 4 PM close the “Fake Close” (FC). Often there are significant moves in the volatility markets in the remaining 15 minutes of trading—which can result in big differences between the Fake close and the IV close values. The leveraged ETPs rebalance is based on the IV close values so if you use the FC value for your calculations you will often conclude that the ETPs are not moving with the correct leverage the next day (see If you think your ETP is broken ).

While the Fake close generates confusion it does have one redeeming quality—it gives us one more piece of intraday data at an interesting time.

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The Fake close allows us to better characterize the last 15 minutes of trading—on days with big volatility moves there is often a lot of action in this time window. The graph below shows the historical UVXY percentage moves in the 15 minutes from Fake close to the IV close.

 

The 102% move on 5-Feb-2108 was a 29 sigma move—a good indicator that assuming a normal distribution for $UXVY day end percentage moves is a really bad idea…  For more on outsize sigma moves see: Not All High Sigma Events are Black Swans.

http://www.bigleaguekickball.com/category/press/ soma cod Revised Daily High & Low Numbers

The publicly available trade data assumes that trading stops at 4 PM, so the stated high and low data may be inaccurate—because new lows and highs can be reached in the last 15 minutes of trading (and often are). In my simulation, if the IV close is lower than the trading low or higher than the trading high I set the intraday lows and highs to the IV close value as appropriate. Of course, the prices may have moved to higher highs or lower lows than the IV close during those 15 minutes but that trade data is not freely available.

Adjusting the February 5, 2018 data

Using old UVXY & SVXY OHLC data to generate simulated values for the new 1.5X and 0.5X funds is defendable for every day from March 26th, 2004 through February 27th, 2018– except for February 5th, 2018.

On that day the VIX set a new one day close-to-close record with a +116% jump (the previous highest was +62%) and the mix of VIX futures that UVXY and SVXY track (index SPVXSP) jumped +96% (the previous highest jump was +33%).  I won’t go into the details, I’ll defer that to another post but the bottom line is that the managers of UVXY and SVXY correctly predicted that the end of day settlement for VIX Futures would be the apex of a liquidity crisis and chose to buy VIX futures to do their required rebalancing before the VIX Futures closed. This meant that the funds ‘performance likely would not track their target index but in the end, counterintuitively, saved both the 2X long and -1X inverse shareholders money.

In an alternative universe, had the reduced leverage 1.5X UVXY and -0.5X SVXY been trading on February 5th the fund managers would probably not have started early with their rebalancing so my simulation used actual VIX futures settlement values to simulate the end of day values on February 5th, 2018 for the lower leverage funds.

Conclusion

 An important lesson, illustrated by the February 2018 travails of XIV and SVXY, is that when you’re testing trading strategies, you shouldn’t assume that past relationships (e.g., VIX percentage moves relative to VIX Futures percentage moves). will hold in the future. It’s critical to ask what can happen, especially when systems are highly stressed. It’s not enough to just look at the past.

 

 

Example Conversion Equation

New_Openday  = New_IV_Closeday-1* (1+1.5/2.0*(Old_Openday / Old_Closeday-1   -1))

Where:  New = 1.5X UVXY
Old = 2X UVXY

Purchase Information

If you purchase one of the spreadsheets below you will be eventually be directed to PayPal where you can pay via your PayPal account or a credit card. When you successfully complete the PayPal portion you will be shown a “Return to Six Figure Investing“ link.    Click on that link to reach the page where you can download the spreadsheet.  Please email me at [email protected] if you have problems, questions, or requests.



How Did the Old -1X SVXY Work

Updated: Apr 21st, 2018 | Vance Harwood | @6_Figure_Invest

Update 

As of February 28th, 2018 SVXY will target -0.5 leverage instead of -1X.  This change was in response to the events of February 5th, 2018 when a massive VIX futures spike occurred in the last 30 minutes of trading, probably in part due to the rebalancing required by the 2X UVXY and -1X SVXY funds.  This change to SVXY will reduce its rebalancing requirements and make it less susceptible to a termination event if VIX futures were to increase 100% or more from the previous day’s close.  This leverage change will reduce SVXY’s performance when VIX futures are dropping in value.  Proshares Press Release

——————————————————————————————————————————————————————–

Just about anyone who’s looked at a multi-year chart for a long volatility fund like Barclays’ VXX has thought about taking the other side of the trade. ProShares’ SVXY is an Exchange Traded Fund (ETF) that allows you bet against funds like VXX while avoiding some of the issues associated with a direct short.

To have a good understanding of how SVXY works (full name: ProShares Short VIX Short-Term Futures ETF) you need to know how it trades, how its value is established, what it tracks, and how ProShares makes money with it.

How did -1X SVXY trade?

  • SVXY trades like a stock. It can be bought, sold, or sold short anytime the market is open, including premarket and after-market time periods.  With an average daily volume of 8 million shares, its liquidity is excellent and the bid/ask spreads are a few cents.
  • SVXY has options available on it, with five weeks’ worth of Weeklys and strikes in 50 cent increments.
  • Like a stock, SVXY’s shares can be split or reverse split—but unlike VXX (with 4 reverse splits since inception) SVXY has done three 1:2 splits to bring its price down into optimum trading levels. Unlike Barclays VXX, SVXY is not on a hell-ride to zero.
  • SVXY can be traded in most IRAs / Roth IRAs, although your broker will likely require you to electronically sign a waiver that documents the various risks with this security.  Shorting of any security is not allowed in an IRA.
  • SVXY is subject to termination risk.  Termination can occur (and did with XIV, a very similar fund on February 5th, 2018) if the daily positive move in the VIX futures market approaches or exceeds a 100%.  ProShares guarantees that SVXY will not go negative so to protect themselves they will cover their short positions and terminate the fund if things get bad enough.  For more on this see XIV Termination.

How was  -1X SVXY’s value established?

  • Unlike stocks, owning SVXY does not give you a share of a corporation. There are no sales, no quarterly reports, no profit/loss, no PE ratio, and no prospect of ever getting dividends.  Forget about doing fundamental style analysis on SVXY. While you’re at it forget about technical style analysis too, the price of SVXY is not driven by its supply and demand—it is a small tail on the medium-sized VIX futures dog, which itself is dominated by SPX options (notional value > $100 billion).
  • The value of SVXY is set by the market, but it’s closely tied to the daily percentage moves of the inverse of an index (S&P VIX Short-Term Futurestm) that manages a hypothetical portfolio containing VIX futures contracts with two different expirations. Every day the index methodology specifies a new mix of VIX futures in the portfolio. On a daily basis SVXY moves in the opposite direction of the index with a leverage factor of -0.5X, so for example, if the index (ticker SPVXSPID) moves up 0.3%, then SVXY will move down precisely 0.15%. This post has more information on how the index itself works. The index is maintained by S&P Dow Jones Indices.
  • As is the case with all Exchange Traded Funds, SVXY’s theoretical share value is just the dollar value of the securities and cash that it currently holds divided by the number of shares outstanding. This theoretical value is published every 15 seconds as the “intraday indicative” (IV) value. Yahoo Finance publishes this quote using the ^SVXY-IV ticker. The end of day value is published as the Net Asset Value (NAV).  The NAV is computed at 4:15 ET, not the usual market close time of 4:00 ET, because VIX Futures don’t settle until 4:15.
  • If the trading value of SVXY diverges too much from its IV value wholesalers called Authorized Participants (APs) will normally intervene to reduce that difference. If SVXY is trading enough below the index they start buying large blocks of SVXY—which tends to drive the price up, and if it’s trading above they will short SVXY.  The APs have an agreement with ProShares that allows them to do these restorative maneuvers at a profit, so they are highly motivated to keep SVXY’s tracking in good shape.

What did -1X SVXY track?

  • SVXY makes lemonade out of lemons.  The lemon, in this case, is the index S&P VIX Short-Term Futurestm that attempts to track the CBOE’s VIX® index—the market’s de facto volatility indicator. Unfortunately, it’s not possible to directly invest in the VIX, so the next best solution is to invest in VIX futures. This “next best” solution turns out to be truly horrible—with average losses of 5% per month. See this post for charts on how this decay factor has varied over time. For more on the cause of these losses see “The Cost of Contango”.
  • This situation sounds like a short sellers dream, but VIX futures occasionally go on a tear, turning the short sellers’ world into something Dante would appreciate.
  • Most of the time (75% to 80%) SVXY is a real moneymaker and the rest of the time it is giving up much of its value in a few weeks—drawdowns of +90% are not unheard of. The chart below shows SVXY from 2004 using actual values from October 2011 forward and simulated values before that.

 

  • Understand that SVXY did not implement a true short of its tracking index. Instead, it attempted to track the -1X percentage inverse of the index on a daily basis.  To maintain this -0.5X behavior the fund must rebalance/reset its investments at the end of each day.  For a detailed example of how this rebalancing works see “How do Leveraged and Inverse ETFs Work?
  • There are some very good reasons for this rebalancing, for example, a true short can only deliver at most a 100% gain and the leverage of a true short is rarely -1X (for more on this see “Ten Questions About Short Selling”.  -0.5X SVXY, on the other hand, would be up over 200% if it had been trading since the original -1X SXVY started trading 3-Oct-2011.  It faithfully delivers a daily percentage move very close to -0.5X of its index.
  • Detractors of the daily reset approach correctly note that SVXY and funds like it can suffer from volatility drag. If the index moves around a lot and then ends up in the same place SVXY will lose value, whereas a true short would not, but as I mentioned earlier, true shorts have other problems.  Even with volatility drag daily reset funds don’t always underperform. If the underlying index is trending down, they can deliver better than -1X cumulative performance. The chart below shows the relative one-year performance of SVXY and a true short starting with $1K invested in January for 2011 through 2016.

SVXYvsShort

How did ProShares make money on -1X SVXY?

  • An Exchange Trade Fund like SVXY must explicitly hold the appropriate securities or equivalent swaps matching the index it tracks. ProShares does a very nice job of providing visibility into those positions. The “Daily Holdings” tab of their website shows how many VIX futures contracts are being held. Because of the -1X nature of the fund, the face value of the VIX futures contracts will be very close to the negative of the net “Other asset/cash” value of the fund.
  • ProShares collects a daily investor fee on SVXY’s assets. The fee is stated as a 0.95% annual fee, but it’s implemented by subtracting 0.95/365 of a percent from each share’s value every calendar day. With current assets at $1 billion, this fee brings in around $9.5 million per year. That should be enough to be profitable, however, I suspect the ProShares’ business model includes revenue from more than just the investor fee.
  • Exchange Traded Funds like SVXY recoup transaction costs in a non-transparent way. Transaction costs are deducted from the fund’s cash balance—resulting in a slow divergence of the fund’s IV value from the theoretical value of the index that it’s tied to. This differs from the approach that Exchange Traded Notes (ETN) use, their theoretical value is directly tied to the moves of the index itself, so the ETN issuers must pay for transaction costs other ways (e.g., out of the annual investor fee, or other explicit fees). In the case of SVXY, this hidden transaction fee has averaged around 0.28% per year.
  • One clue on ProShares’ business model might be contained in this sentence from SVXY’s prospectus:
    “A portion of each VIX Fund’s assets may be held in cash and/or U.S. Treasury securities, agency securities, or other high credit quality short-term fixed-income or similar securities (such as shares of money market funds and collateralized repurchase agreements).”  Agency securities are things like Fannie Mae bonds. The collateralized repurchase agreements category strikes me as a place where ProShares might be getting significantly better than money market rates. With SVXY currently able to invest around $250 million this could be a significant income stream.
  • I’m sure one aspect of SVXY is a headache for ProShares. Its daily reset construction requires its investments to be rebalanced at the end of each day, and the required investments are proportional to the percentage move of the day and the amount of assets held in the fund. SVXY currently holds $400 million in assets, and if SVXY moves down 10% in a day (the record negative daily move is -24%, positive move +18 %) then ProShares must commit an additional $20 million (5% of $400 million) of capital that evening. If SVXY goes down 10% the next day, then another $18 million capital infusion is required.

SVXY won’t be on any worst ETF lists like Barclays’ VXX, but its propensity for dramatic drawdowns (e.g. -91%! in the Jan/Feb 2018  timeframe) will keep it out of most people’s portfolios. Not many of us can handle the emotional stress of holding on to a position with huge losses—even though the odds support an eventual rebound.

The eye-popping inverse volatility gains in 2016 and 2017 pushed SVXY’s assets beyond VXX’s but with its February 5, 2018 reset it dropped well below that level. It will be interesting to see how the next few years go.  I suspect we’ll see additional bloodletting as people rediscover short volatility can be very volatile in its own right.



How Do Bitcoin Futures Work?

Updated: Jan 21st, 2018 | Vance Harwood | @6_Figure_Invest

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.

Leverage

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.

Conclusion

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

Updated: Dec 27th, 2017 | Vance Harwood | @6_Figure_Invest

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
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A Better Way to Model the VIX

Updated: Nov 28th, 2017 | Vance Harwood | @6_Figure_Invest

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.

 

Conclusion

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.