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Condor Options is a New York-based research and trading firm focusing on market neutral trading strategies. Condor Options publishes an educational newsletter teaching iron condors and volatility-based options trading, with a focus on risk management and quantitative analysis. Jared Woodard is... More
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  • Should Market-Neutral Options Traders Diversify?

    We generally restrict iron condor trades in our paid newsletter and managed accounts to index products. For those who prefer ETFs, we look at SPY, DIA, IWM, QQQQ; otherwise, SPX, RUT, NDX, DJX are bigger proxies, or on the futures side of things we’ll look at the Emini S&P 500 or Nasdaq 100 (ES and NQ). The reason we trade index products is that diversification reduces the impact of company-level surprises: an iron condor on, say, RIMM will get punished if someone discovers that Blackberries cause some disease (in addition, that is, to destroying users’ basic social skills), while a condor on some Nasdaq 100 product won’t be affected in the same way.

    That’s a good argument for trading condors on indexes rather than individual stocks, but what about the vast territory in between? Member DA raises the question this way:

    I know you only trade iron condors on the SPY ETF; can you suggest another ETF that doesn’t have a strong correlation with the S&P500 for one to trade iron condors on in conjuction with the SPY. I think DIA, MNX, IWM all seem to be strongly correlated, but what do you think of XLE or FXI?

    In other words, what about sector or country ETFs (or other products) that offer the same or similar levels of diversification, but are also not so correlated to the U.S. market? The thought here is that a bad month for an SPY position might be a good month for an XLE trade.

    I see the attraction of this approach, but I’m not sure how helpful it would be in the long run. If you’re the sort of trader who sells options or option spreads on a consistent basis, I think you’ll find that adding foreign or sector-based products to your regime may yield disappointing results. Here are some betas for popular ETFs (source: thinkorswim):
     

    Product Beta
    FXI 1.9
    EEM 1.5
    EFA 1.19
    XLE 0.81
    XLI 1.19
    XLF 1.55
    USO 0.4
    GLD 0.53

    If your goal is to trade some products that zig when the market zags – or that simply do their own thing – many foreign and sector-based products won’t fit the bill. I included some commodities at the end of the table to suggest that, for real diversification with reduced correlation, non-equity assets deserve a look.

    There’s another, more important reason why I don’t trade non-index products in the newsletter. Unlike many traders who rely on technical analysis or fundamentals to generate trade ideas, I don’t enter positions based on chart patterns or P/E ratios. Instead, the strategy followed in the newsletter is designed to collect the volatility risk premium in U.S. equity index options. It is entirely possible that a similar premium exists in other markets or even in other asset classes, but we would only trade options on those assets in the newsletter given sufficient evidence of a such a premium.

    Of course, if you’re a fan of technical, fundamental, or some other school of analysis, it bears noting that any option spread is in principle applicable to any underlying asset, given the right conditions. The classic example in the case of iron condors is of a technical prognosticator who believes, for whatever reason, that an asset is likely to trade within a range over some period of time. If your thesis is correct, it hardly matters whether the asset under consideration is a highly liquid sector product or a Burkina Faso ETF.

    Aug 31 1:26 PM | Link | Comment!
  • Using Diagonal Spreads to Front-load Directional Risk

    A large block trade in the S&P 500 ETF (NYSEARCA:SPY) reveals what one investor expects for the market over the coming weeks.  Chris McKhann has the details:

    In one trade of single blocks, executed all at the same time, an institutional trader sold 448,014 of the S&P 500 ETF (SPY) August 100 calls for 1.39, bought 298,676 of the October 106 calls for 1.37, and bought 149,338 of the September 103 calls for 1.51. All told, that is 896,028 contracts!

    This is a diagonal spread that will profit if the most is the SPY remains at 100 until expiration. But the trade is protected to the downside, and increased implied volatility will increase the profits to the downside. The trader may well be bullish, but not in the near term, and using this strategy to get into the long calls in a highly leveraged way.

    This particular trade ended up being busted, but it’s still worth analyzing. The thesis here is of the “short now, long later” variety: while I described this trade on Twitter as “big money calling a top,” a more accurate characterization might well be that someone who missed out on this summer’s rally is hoping to play catch-up. The risk profile below should help clarify the way this trade would play out:

    This diagonal spread is neutral-to-bearish over the next seven days: the trade will be profitable if SPY is between roughly 96 and 101 at August expiration (the green line above), with the premium from the expired 100 calls offsetting some of the cost of the long calls.  Don’t let the three legs cause confusion: the trade essentially shorts two front-month at the money calls in order to buy two out-month out of the money calls.  After August expiration, this becomes a straightforward long position (blue line) with limited downside risk but plenty of upside potential. Of course, the trader might plan to sell additional calls in later months, but let’s assume there will be no other modifications. Given that the calls don’t start seeing significant gains until SPY is above 103, this trade might best be suited for someone who was very bullish and wanted to participate in the next leg of the new glorious bull market that is upon us, without risking much to do so. This position essentially shifts the biggest risk into the front month by betting against a SPY breakout over the next week, which looks like a smart tactical play.
    Disclosure: No positions

    Aug 14 3:25 PM | Link | Comment!
  • Explaining Asymmetric Volatility

    Measurements of volatility typically refer to the standard deviation of returns over a specified period. That obviously includes returns both below and above the mean. In practice, however, investors tend to be concerned primarily with downside risk, leading them to regard returns differently: positive and negative logarithmic returns that are equally distant from the mean are not treated as such by investors. Negative surprises have a much greater effect on volatility than do positive ones – witness the explosion of interest in 2008 in all things VIX and volatility-related.

    This helps explain the phenomenon of vertical volatility skew.  In equity markets, the long-only bias resulting from the structure of mutual funds and other institutional factors means that investors are considerably more nervous, on any given day, about a potential 5% decline than they are fearful (or greedy) about the possibility of a 5% rally.  That uneven fear causes investors to overpay for put protection and creates a persistent “volatility smile” in which the implied volatility for deep out-of-the-money options will tend to be significantly higher than at- and in-the-money options in the same expiration cycle – with a volatility “smirk” occurring when skew is more exaggerated on the put side.  In commodities, expectations can differ dramatically, such that the smirk is tilted to the call side.

    In “How Asymmetric is U.S. Stock Market Volatility?“, Ederington and Guan explore the asymmetry of volatility not by analyzing skewness, but by tracing the effects of equally large positive and negative return shocks on implied volatility, realized volatility, and models that attempt to predict volatility for asymmetric time series:

    This paper explores differences in the impact of equally large positive and negative surprise return shocks in the aggregate U.S. stock market on:  1) the volatility predictions of asymmetric time series models, 2) implied volatility, and 3) realized volatility.  Both asymmetric time series models and implied volatility predict an increase in volatility following large negative surprise returns and ex post realized volatility normally rises as predicted.   However, while asymmetric time series models, such as the EGARCH and GJR models, predict an increase in volatility following a large positive return shocks (albeit a much smaller increase than following a negative shock of the same magnitude), both implied and realized volatility generally fall sharply. While asymmetric time-series models predict a decline in volatility following near-zero returns,
    both implied and realized volatility are normally little changed from levels observed prior to the stable market.  Reasons for the differences are explored.

    So the problem tackled here is that the stochastic models developed precisely to deal with the asymmetry of stock markets don’t seem to make correct predictions when it comes to these discrete events.  Which metric does the best job? The implied volatility read straight from option prices – the VIX, actually – appears to correspond to future realized volatility more closely in the types of situations under review.  Every approach gets the impact of large negative shocks correct: volatility rises sharply.  But following a large positive return, the time series models predict a small increase in volatility even though implied and realized volatility tend to decline afterward.  The authors offer an explanation that the GARCH models may tend to overweight extreme return observations.  They conclude that the adage that “volatile markets beget volatile markets” may not be exactly right: “Volatile markets do tend to follow bear markets but implied and realized volatility both tend to fall following bull markets.  Following stable markets (near-zero returns), implied and realized volatility are little changed from the levels observed prior to the near-zero return.”

    Most traders we know aren’t relying heavily on GARCH or related models for volatility predictions anyway, but it is helpful to get some confirmation of the usefulness of options prices for estimating future volatility.  Microcosmically, then, options markets appear efficient in the weak sense (even if markets in general seem these days like a maniacally inefficient means for structuring society).

    Jul 01 7:01 AM | Link | Comment!
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