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Statistical Arbitrage opportunities using Option Contracts on Exchange Traded Funds (ETFs)

Statistical Arbitrage opportunities using Option Contracts on Exchange Traded Funds (ETFs).

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Statistical Arbitrage opportunities using Option Contracts on Exchange Traded Funds (ETFs)

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  1. Statistical Arbitrage opportunities using Option Contracts on Exchange Traded Funds (ETFs)

  2. The strategy will trade options on certain Cash, Leveraged and Inverse Leveraged ETF’s (Exchange Traded Funds), which provide long and short, mostly leveraged, exposure to the daily return of various indexes • Because of ”non-linear” and ”asymmetrical” composition of Leveraged and Inverse Leveraged ETF’s, some inefficiencies exist in relational pricing of “ETF Groups” and especially the pricing of their derivatives (options) • The universe of Indexes considered will be initially confined to the 3 major indexes – S&P 500, Dow Jones Industrial, and NASDAQ Composite; thus there will be 3 ”Groups” in the strategy, each consisting of 5 ETF’s Strategy Description

  3. The 3 ”ETF Groups” are: 1) S&P 500 Group – SPY, SSO (2x), SDS (-2x), UPRO (3x), and SPXU (-3x) 2) Dow Jones Industrial Group – DIA, DDM (2x), DXD (-2x), UDOW (3x), SDOW (-3x) 3) NASDAQ Comp Group – QQQQ, QLD (2x), QID (-2x), TQQQ (3x), SQQQ (-3x) • Roughly 10 calls and 10 puts with strikes closest to the price of the underlying instrument will be considered • Therefore, each ”ETF Group” will roughly have 100 options contracts out of which our positions will be construed Strategy Description (cont’d)

  4. A typical position in each ”ETF Group” will consist of 4 to 7 instruments, with either 1 equity ETF and 4 to 5 option positions, or all option positions • As a general rule, the options with 30-60 days left to expiration will be traded, with some use of weekly options and LEAPS to take advantage of changes in volatility skew • Positions will be selected and entry and exit points will be based on the specific goals and targets of the managed fund (mostly risk profile)* * Most of the analysis is done in Mat Lab and Excel and we choose the position that gives the best Sharpe Ratio but that also considers liquidity, total Buying Power, already existing exposure, and some other factors A ”typical” position description

  5. Sample Position and Margin Requirement

  6. The above sample position is a ”Unitary Position” and can be scaled up according to the fund’s size. • The Margin Requirement shown is after margin relief provided by the clearing broker to ”Professional Traders” for Spreads and Strategies => the fund is only charged haircut on total Risk of the position which is less than haircut required if every position were treated on a stand alone basis (would have to deposit $4,500 without margin relief for the unitary sample position above) • Consequently, as options are not marginable ”explicitly”, the fund uses leverage via margin relief and controls risk via percentage of assets invested and cash reserves, as well as volatility sensitivity parameters that trigger entry and exit points Discussion of Leverage and Sample Margin Requirement

  7. Typical P&L Profile of a Unitary Position

  8. This chart represents what would happen UPON EXPIRATION IF ALL THE PRICES REMAINED THE SAME UNTIL EXPIRATION • As such, an orange line represents a Profit on expiration if the prices of all the ETF’s in the ”Group” remained the same • Therefore, we can only consider this chart as a very rough approximation of how ”real” risk/reward break down will look like. • In reality, the position is closed out 2 days to 2 weeks after it has been established (depending on the index volatility and price movement) • Due to ”volatility drag”, the above graph will gradually sliding down or ”sagging” with time passage Discussion of the Profit & Loss Graph

  9. The next slide will show all the possibilities of the P&L chart “movement” and will present the P&L area from the 20 different randomly generated scenarios of possible underlying index movements*. * It is impossible to predict the prices of Leveraged and Inverse Leveraged ETF’s based on the price of underlying index at some future finite day because these prices will be determined by DAILY movements (because of ”non-linearity” and ”asymmetry” concepts) which are impossible to predict and can only be simulated • It will show the same chart or ”area” from 3 different points of view. The axis of 0 to 20 is the axis that shows the 20 randomly generated underlying index movements (between now and expiration). The axis of 0 to 80 shows the QQQQ value at the end of the period, or at expiration. The 3-rd axis is the P&L. Discussion of the Profit & Loss Graph (cont.)

  10. The strategy will perform the worst in the relatively low volatility environment with the underlying index ending up at the level where it was when the position was established. • Relatively low volatility is defined as movements of 1-2% in the underlying index that take several days to develop, kind of a slow seesaw movement of the underlying index that ends where it started. In this environment, the relationship of ETF’s gets ”misaligned” and the position loses value due to ”volatility drift”. • The worst case scenario is conservatively estimated as a loss of about 2% of total capital per month and as sensitivity of positions to volatility is adjusted to historical recent volatility more than 3 consecutive months of losses are unlikely. • If the volatility is very low (as opposed to relatively low) the strategy will be making a profit indicated by an orange line on the original P&L chart. If the volatility is relatively high the strategy will have plenty of chances to close the position early and often with good profit. Discussion of risk and the worst case scenario

  11. P&L and Risk as a function of Volatility

  12. The universe of ETF’s and options traded are highly liquid (especially the cash and 2x Leveraged and Inverse ones) => therefore, the strategy is fully scalable. • Similar to the ”area” graphs presented in risk analysis there are ”areas” of opportunities every day in each Group => the fund will establish, adjust and close several positions in each ”Group” every day => we can have dozens of positions in every “Group” at any given time • As a result, number of trades will be contracting during low volatility periods and expanding during high volatility times, rather than 1 core position that is closed before the next one is opened. Liquidity and Position Limits

  13. The strategy can be classified as ”Quantitative Arbitrage” and as ”Market Neutral” strategy. As such, it will be a great addition to any firm that engages in any kind of directional trading and investing. • The strategy eliminates market risk (Beta neutral) and cushions any swings in returns of many directional strategies. It will also significantly lower a risk profile of virtually any strategy, including any other ”market neutral” strategies Market Neutrality Feature

  14. The historical returns of the strategy over the 4 years are in the vicinity of 16% annualized, and it is on track to produce 18%+ return in 2012 as of the end of October 2012 • The returns were spread out fairly evenly over the years so there is not a lot of variance in annual results. The monthly/quarterly variance of returns is fairly even as well, and mostly depends on volatility of equity indexes • The largest draw-dawn the strategy ever produced was 7.5%, and only had 1 negative quarter • The strategy is geared up to produce 15-20% annualized returns with low variance of returns and total risk under 10%. Historical Returns and Risk Profile

  15. The 3 core ”ETF Groups” have the best risk/reward and liquidity profiles because they represent the broadest indexes and contain 5 ETF’s. The current groups will serve as the foundation on which the strategy can be expanded to other ”groups” of inter-connected equity instruments, as well as other derivate products. • Other ETF Groups showed very promising results but carry a riskier profile with a better potential profit opportunity, just by virtue of them tracking more volatile indexes, being less liquid and some of them having less ETF’s in each Group (2-3). Therefore, the other ETF groups will have higher volatility of returns. • Also, the fund will consider using Index Options, Binary Options (including FLEX), Index Futures, and Options on Index Futures to build on quantitative concepts of the strategy Closing Thoughts

  16. The examples of the potential other ”Groups” are below: • Asset Category of ”Sectors” => 1) DIG and DUG (tracking US Oil & Gas Index) 2) SEF, SKF, UYG (tracking Dow Jones US Financials) • Asset Category ”Market Capitalization” => 1) MDY, MVV and UMDD,MZZ and SMDD (tracking S&P MidCap 400) • Asset Category of ”Commodity” => 1) UCO and SCO (tracking a Crude Oil Sub-Index of DJ- UBS Commodity Index) Closing Thoughts (cont.)

  17. Furthermore, we significant opportunities exist in ”options arbitrage” on ETF’s across Asset Sub-categories (i.e. 20+ Year Treasury ETF Group vs. 7-10 Year Treasury ETF Group), across Asset Categories (i.e. US Oil and Gas Index ETF Group vs. DJ-UBS Oil Index and DJ-UBS Commodity Index), and across options on different instruments (i.e. Options on Currency Futures vs. Options on Currency ETF Group) => although the risk profile in these strategies will probably be higher as these relationships are subject to a lot of exogenous pressures Closing Thoughts (cont.)

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