CHAPTER 13
Delta-Neutral Trading
Trading Realized Volatility
So far, we’ve discussed many option strategies in which realized volatility is an important component of the trade. And while the management of these positions has been the focus of much of the discussion, the ultimate gain or loss for many of these strategies has been from movement in a single direction. For example, with a long call, the higher the stock rallies the better.
But increases or decreases in realized volatility do not necessarily have an exclusive relationship with direction. Recall that realized volatility is the annualized standard deviation of daily price movements. Take two similarly priced stocks that have had a net price change of zero over a one-month period. Stock A had small daily price changes during that period, rising $0.10 one day and falling $0.10 the next. Stock B went up or down by $5 each day for a month. In this rather extreme example, Stock B was much more volatile than Stock A, regardless of the fact that the net price change for the period for both stocks was zero.
A stock’s volatility—either high or low volatility—can be capitalized on by trading options delta neutral. Simply put, traders buy options delta neutral when they believe a stock will have more movement and sell options delta neutral when they believe a stock will move less.
Delta-neutral option sellers profit from low volatility through theta. Every day that passes in which the loss from delta/gamma movement is less than the gain from theta is a winning day. Traders can adjust their deltas by hedging. Delta-neutral option buyers exploit volatility opportunities through a trading technique called gamma scalping.
Gamma Scalping
Intraday trading is seldom entirely in one direction. A stock may close higher or lower, even sharply higher or lower, on the day, but during the day there is usually not a steady incremental rise or fall in the stock price. A typical intraday stock chart has peaks and troughs all day long. Delta-neutral traders who have gamma don’t remain delta neutral as the underlying price changes, which inevitably it will. Delta-neutral trading is kind of a misnomer.
In fact, it is gamma trading in which delta-neutral traders engage. For long-gamma traders, the position delta gets more positive as the underlying moves higher and more negative as the underlying moves lower. An upward move in the underlying increases positive deltas, resulting in exponentially increasing profits. But if the underlying price begins to retrace downward, the gain from deltas can be erased as quickly as it was racked up.
To lock in delta gains, a trader can adjust the position to delta neutral again by selling short stock to cover long deltas. If the stock price declines after this adjustment, losses are curtailed thanks to the short stock. In fact, the delta will become negative as the underlying price falls, leading to growing profits. To lock in profits again, the trader buys stock to cover short deltas to once again become delta neutral.
The net effect is a stock scalp. Positive gamma causes the delta-neutral trader to sell stock when the price rises and buy when the stock falls. This adds up to a true, realized profit. So positive gamma is a money-making machine, right? Not so fast. As in any business, the profits must be great enough to cover expenses. Theta is the daily cost of running this gamma-scalping business.
For example, a trader, Harry, notices that the intraday price swings in a particular stock have been increasing. He takes a bullish position in realized volatility by buying 20 off the 40-strike calls, which have a 50 delta, and selling stock on a delta-neutral ratio.
Buy 20 40-strike calls (50 delta) (long 1,000 deltas)
Short 1,000 shares at $40 (short 1,000 deltas)
The immediate delta of this trade is flat, but as the stock moves up or down, that will change, presenting gamma-scalping opportunities. Gamma scalping is the objective here. The position greeks in
Exhibit 13.1
show the relationship of the two forces involved in this trade: gamma and theta.
EXHIBIT 13.1
Greeks for 20-lot delta-neutral long call.
The relationship of gamma to theta in this sort of trade is paramount to its success. Gamma-scalping plays are not buy-and-hold strategies. This is active trading. These spreads need to be monitored intraday to take advantage of small moves in the underlying security. Harry will sell stock when the underlying rises and buy it when the underlying falls, taking a profit with each stock trade. The goal for each day that passes is to profit enough from positive gamma to cover the day’s theta. But that’s not always as easy as it sounds. Let’s study what happens the first seven days after this hypothetical trade is executed. For the purposes of this example, we assume that gamma remains constant and that the trader is content trading odd lots of stock.
Day One
The first day proves to be fairly volatile. The stock rallies from $40 to $42 early in the day. This creates a positive position delta of 5.60, or the equivalent of being long about 560 shares. At $42, Harry covers the position delta by selling 560 shares of the underlying stock to become delta neutral again.
Later in the day, the market reverses, and the stock drops back down to $40 a share. At this point, the position is short 5.60 deltas. Harry again adjusts the position, buying 560 shares to get flat. The stock then closes right at $40.
The net result of these two stock transactions is a gain of $1,070. How? The gamma scalp minus the theta, as shown below.
The volatility of day one led to it being a profitable day. Harry scalped 560 shares for a $2 profit, resulting from volatility in the stock. If the stock hadn’t moved as much, the delta would have been smaller, and the dollar amount scalped would have been smaller, leading to an exponentially smaller profit. If there had been more volatility, profits would have been exponentially larger. It would have led to a bigger bite being taken out of the market.
Day Two
The next day, the market is a bit quieter. There is a $0.40 drop in the price of the stock, at which point the position delta is short 1.12. Harry buys 112 shares at $39.60 to get delta neutral.
Following Harry’s purchase, the stock slowly drifts back up and is trading at $40 near the close. Harry decides to cover his deltas and sell 112 shares at $40. It is common to cover all deltas at the end of the day to get back to being delta neutral. Remember, the goal of gamma scalping is to trade volatility, not direction. Starting the next trading day with a delta, either positive or negative, means an often unwanted directional bias and unwanted directional risk. Tidying up deltas at the end of the day to get neutral is called going home flat.
Today was not a banner day. Harry did not quite have the opportunity to cover the decay.
Day Three
On this day, the market trends. First, the stock rises $0.50, at which point Harry sells 140 shares of stock at $40.50 to lock in gains from his delta and to get flat. However, the market continues to rally. At $41 a share, Harry is long another 1.40 deltas and so sells another 140 shares. The rally continues, and at $41.50 he sells another 140 shares to cover the delta. Finally, at the end of the day, the stock closes at $42 a share. Harry sells a final 140 shares to get flat.
There was not any literal scalping of stock today. It was all selling. Nonetheless, gamma trading led to a profitable day.
As the stock rose from $40 to $40.50, 140 deltas were created from positive gamma. Because the delta was zero at $40 and 140 at $40.50, the estimated average delta is found by dividing 140 in half. This estimated average delta multiplied by the $0.50 gain on the stock equals a $35 profit. The delta was zero after the adjustment made at $40.50, when 140 shares were sold. When the stock reached $41, another $35 was reaped from the average delta of 70 over the $0.50 move. This process was repeated every time the stock rose $0.50 and the delta was covered.
Day Four
Day four offers a pleasant surprise for Harry. That morning, the stock opens $4 lower. He promptly covers his short delta of 11.2 by buying 1,120 shares of the stock at $38 a share. The stock barely moves the rest of the day and closes at $38.
An exponentially larger profit was made because there was $4 worth of gains on the growing delta when the stock gapped open. The whole position delta was covered $4 lower, so both the delta and the dollar amount gained on that delta had a chance to grow. Again, Harry can estimate the average delta over the $4 move to be half of 11.20. Multiplying that by the $4 stock advance gives him his gamma profit of $2,240. After accounting for theta, the net profit is $2,190.
Days Five and Six
Days five and six are the weekend; the market is closed.
Day Seven
This is a quiet day after the volatility of the past week. Today, the stock slowly drifts up $0.25 by the end of the day. Harry sells 70 shares of stock at $38.25 to cover long deltas.
This day was a loser for Harry, as profits from gamma were not enough to cover his theta.
Art and Science
Although this was a very simplified example, it was typical of how a profitable week of gamma scalping plays out. This stock had a pretty volatile week, and overall the week was a winner: there were four losing days and three winners. The number of losing days includes the weekends. Weekends and holidays are big hurdles for long-gamma traders because of the theta loss. The biggest contribution to this being a winning week was made by the gap open on day four. Part of the reason was the sheer magnitude of the move, and part was the fact that the deltas weren’t covered too soon, as they had been on day three.
In a perfect world, a long-gamma trader will always buy the low of the day and sell the high of the day when covering deltas. This, unfortunately, seldom happens. Long-gamma traders are very often wrong when trading stock to cover deltas.
Being wrong can be okay on occasion. In fact, it can even be rewarding. Day three was profitable despite the fact that 140 shares were sold at $40.50, $41, and $41.50. The stock closed at $42; the first three stock trades were losers. Harry sold stock at a lower price than the close. But the position still made money because of his positive gamma. To be sure, Harry would like to have sold all 560 shares at $42 at the end of the day. The day’s profits would have been significantly higher.
The problem is that no one knows where the stock will move next. On day three, if the stock had topped out at $40.50 and Harry did not sell stock because he thought it would continue higher, he would have missed an opportunity. Gamma scalping is not an exact science. The art is to pick spots that capture the biggest moves possible without missing opportunities.
There are many methods traders have used to decide where to cover deltas when gamma scalping: the daily standard deviation, a fixed percentage of the stock price, a fixed nominal value, covering at a certain time of day, “market feel.” No system appears to be absolutely better than another. This is where it gets personal. Finding what works for you, and what works for the individual stocks you trade, is the art of this science.
Gamma, Theta, and Volatility
Clearly, more volatile stocks are more profitable for gamma scalping, right? Well . . . maybe. Recall that the higher the implied volatility, the lower the gamma and the higher the theta of at-the-money (ATM) options. In many cases, the more volatile a stock, the higher the implied volatility (IV). That means that a volatile stock might have to move more for a trader to scalp enough stock to cover the higher theta.
Let’s look at the gamma-theta relationship from another perspective. In this example, for 0.50 of theta, Harry could buy 2.80 gamma. This relationship is based on an assumed 25 percent implied volatility. If IV were 50 percent, theta for this 20 lot would be higher, and the gamma would be lower. At a volatility of 50, Harry could buy 1.40 gammas for 0.90 of theta. The gamma is more expensive from a theta perspective, but if the stock’s statistical volatility is significantly higher, it may be worth it.
Gamma Hedging
Knowing that the gamma and theta figures of
Exhibit 13.1
are derived from a 25 percent volatility assumption offers a benchmark with which to gauge the potential profitability of gamma trading the options. If the stock’s standard deviation is below 25 percent, it will be difficult to make money being long gamma. If it is above 25 percent, the play becomes easier to trade. There is more scalping opportunity, there are more opportunities for big moves, and there are more likely to be gaps in either direction. The 25 percent volatility input not only determines the option’s theoretical value but also helps determine the ratio of gamma to theta.
A 25 percent or higher realized volatility in this case does not guarantee the trade’s success or failure, however. Much of the success of the trade has to do with how well the trader scalps stock. Covering deltas too soon leads to reduced profitability. Covering too late can lead to missed opportunities.
Trading stock well is also important to gamma sellers with the opposite trade: sell calls and buy stock delta neutral. In this example, a trader will sell 20 ATM calls and buy stock on a delta-neutral ratio.
This is a bearish position in realized volatility. It is the opposite of the trade in the last example. Consider again that 25 percent IV is the benchmark by which to gauge potential profitability. Here, if the stock’s volatility is below 25, the chances of having a profitable trade are increased. Above 25 is a bit more challenging.
In this simplified example, a different trader, Mary, plays the role of gamma seller. Over the same seven-day period as before, instead of buying calls, Mary sold a 20 lot.
Exhibit 13.2
shows the analytics for the trade. For the purposes of this example, we assume that gamma remains constant and the trader is content trading odd lots of stock.
EXHIBIT 13.2
Greeks for 20-lot delta-neutral short call.
Day One
This was one of the volatile days. The stock rallied from $40 to $42 early in the day and had fallen back down to $40 by the end of the day. Big moves like this are hard to trade as a short-gamma trader. As the stock rose to $42, the negative delta would have been increasing. That means losses were adding up at an increasing rate. The only way to have stopped the hemorrhaging of money as the stock continued to rise would have been to buy stock. Of course, if Mary buys stock and the stock then declines, she has a loser.
Let’s assume the best-case scenario. When the stock reached $42 and she had a −560 delta, Mary correctly felt the market was overbought and would retrace. Sometimes, the best trades are the ones you don’t make. On this day, Mary traded no stock. When the stock reached $40 a share at the end of the day, she was back to being delta neutral. Theta makes her a winner today.
Because of the way Mary handled her trade, the volatility of day one was not necessarily an impediment to it being profitable. Again, the assumption is that Mary made the right call not to negative scalp the stock. Mary could have decided to hedge her negative gamma when the stock reach $42 and the position delta was at −$560 by buying stock and then selling it at $40.
There are a number of techniques for hedging deltas resulting from negative gamma. The objective of hedging deltas is to avoid losses from the stock trending in one direction and creating increasingly adverse deltas but not to overtrade stock and negative scalp.
Day Two
Recall that this day had a small dip and then recovered to close again at $40. It is more reasonable to assume that on this day there was no negative scalping. A $0.40 decline is a more typical move in a stock and nothing to be afraid of. The 112 delta created by negative gamma when the stock fell wouldn’t be perceived as a major concern by most traders in most situations. It is reasonable to assume Mary would take no action. Today, again, was a winner thanks to theta.
Day Three
Day three saw the stock price trending. It slowly drifted up $2. There would have been some judgment calls throughout this day. Again, delta-neutral trades are for active traders. Prepare to watch the market much of the day if implementing this kind of strategy.
When the stock was at $41 a share, Mary decided to guard against further advances in stock price and hedged her delta. At that point, the position would have had a −2.80 delta. She bought 280 shares at $41.
As the day progressed, the market proved Mary to be right. The stock rose to $42 giving the position a delta of −2.80 again. She covered her deltas at the end of the day by buying another 280 shares.
Covering the negative deltas to get flat at $41 proved to be a smart move today. It curtailed an exponentially growing delta and let Mary take a smaller loss at $41 and get a fresh start. While the day was a loser, it would have been $280 worse if she had not purchased stock at $41 before the run-up to $42. This is evidenced by the fact that she made a $280 profit on the 280 shares of stock bought at $41, since the stock closed at $42.
Day Four
Day four offered a rather unpleasant surprise. This was the day that the stock gapped open $4 lower. This is the kind of day short-gamma traders dread. There is, of course, no right way to react to this situation. The stock can recover, heading higher; it can continue lower; or it can have a dead-cat bounce, remaining where it is after the fall.
Staring at a quite contrary delta of 11.20, Mary was forced to take action by selling stock. But how much stock was the responsible amount to sell for a pure short-gamma trader not interested in trading direction? Selling 1,120 shares would bring the position back to being delta neutral, but the only way the trade would stay delta neutral would be if the stock stayed right where it was.
Hedging is always a difficult call for short-gamma traders. Long-gamma traders are taking a profit on deltas with every stock trade that covers their deltas. Short-gamma traders are always taking a loss on delta. In this case, Mary decided to cover half her deltas by selling 560 shares. The other 560 deltas represent a loss, too; it’s just not locked in.
Here, Mary made the conscious decision not to go home flat. On the one hand, she was accepting the risk of the stock continuing its decline. On the other hand, if she had covered the whole delta, she would have been accepting the risk of the stock moving in either direction. Mary felt the stock would regain some of its losses. She decided to lead the stock a little, going into the weekend with a positive delta bias.
Days Five and Six
Days five and six are the weekend.
Day Seven
This was the quiet day of the week, and a welcome respite. On this day, the stock rose just $0.25. The rise in price helped a bit. Mary was still long 560 deltas from Friday. Negative gamma took only a small bite out of her profit.
The P&(L) can be broken down into the profit attributable to the starting delta of the trade, the estimated loss from gamma, and the gain from theta.
Mary ends these seven days of trading worse off than she started. What went wrong? The bottom line is that she sold volatility on an asset that proved to be volatile. A $4 drop in price of a $42 dollar stock was a big move. This stock certainly moved at more than 25 percent volatility. Day four alone made this trade a losing proposition.
Could Mary have done anything better? Yes. In a perfect world, she would not have covered her negative deltas on day 3 by buying 280 shares at $41 and another 280 at $42. Had she not, this wouldn’t have been such a bad week. With the stock ending at $38.25, she lost $1,050 on the 280 shares she bought at $42 ($3.75 times 280) and lost $770 on the 280 shares bought at $41 ($2.75 times 280). Then again, if the stock had continued higher, rising beyond $42, those would have been good buys.
Mary can’t beat herself up too much for protecting herself in a way that made sense at the time. The stock’s $2 rally is more to blame than the fact that she hedged her deltas. That’s the risk of selling volatility: the stock may prove to be volatile. If the stock had not made such a move, she wouldn’t have faced the dilemma of whether or not to hedge.
Conclusions
The same stock during the same week was used in both examples. These two traders started out with equal and opposite positions. They might as well have made the trade with each other. And although in this case the vol buyer (Harry) had a pretty good week and the vol seller (Mary) had a not-so-good week, it’s important to notice that the dollar value of the vol buyer’s profit was not the same as the dollar value of the vol seller’s loss. Why? Because each trader hedged his or her position differently. Option trading is not a zero-sum game.
Option-selling delta-neutral strategies work well in low-volatility environments. Small moves are acceptable. It’s the big moves that can blow you out of the water.
Like long-gamma traders, short-gamma traders have many techniques for covering deltas when the stock moves. It is common to cover partial deltas, as Mary did on day four of the last example. Conversely, if a stock is expected to continue along its trajectory up or down, traders will sometimes overhedge by buying more deltas (stock) than they are short or selling more than they are long, in anticipation of continued price rises. Daily standard deviation derived from implied volatility is a common measure used by short-gamma players to calculate price points at which to enter hedges. Market feel and other indicators are also used by experienced traders when deciding when and how to hedge. Each trader must find what works best for him or her.
Smileys and Frowns
The trade examples in this chapter have all involved just two components: calls and stock. We will explore delta-neutral strategies in other chapters that involve more moving parts. Regardless of the specific makeup of the position, the P&(L) of each individual leg is not of concern. It is the profitability of the position as a whole that matters. For example, after a volatile move in a stock occurs, a positive-gamma trader like Harry doesn’t care whether the calls or the stock made the profit on the move. The trader would monitor the net delta that was produced—positive or negative—and cover accordingly. The process is the same for a negative-gamma trader. In either case, it is gamma and delta that need to be monitored closely.
Gamma can make or break a trade. P&(L) diagrams are helpful tools that offer a visual representation of the effect of gamma on a position. Many option-trading software applications offer P&(L) graphing applications to study the payoff of a position with the days to expiration as an adjustable variable to study the same trade over time.
P&(L) diagrams for these delta-neutral positions before the options’ expiration generally take one of two shapes: a smiley or a frown. The shape of the graph depends on whether the position gamma is positive or negative.
Exhibit 13.3
shows a typical positive-gamma trade.
EXHIBIT 13.3
P&(L) diagram for a positive-gamma delta-neutral position/l.
This diagram is representative of the P&L of a delta-neutral positive-gamma trade calculated using the prices at which the trade was executed. With this type of trade, it is intuitive that when the stock price rises or falls, profits increase because of favorably changing deltas. This is represented by the graph’s smiley-face shape. The corners of the graph rise higher as the underlying moves away from the center of the graph.
The graph is a two-dimensional snapshot showing that the higher or lower the underlying moves, the greater the profit. But there are other dimensions that are not shown here, such as time and IV.
Exhibit 13.4
shows the effects of time on a typical long-gamma trade.
EXHIBIT 13.4
The effect of time on P&(L).
As time passes, the reduction in profit is reflected by the center point of the graph dipping farther into negative territory. That is the effect of time decay. The long options will have lost value at that future date with the stock still at the same price (all other factors held constant). Still, a move in either direction can lead to a profitable position. Ultimately, at expiration, the payoff takes on a rigid kinked shape.
In the delta-neutral long call examples used in this chapter the position becomes net long stock if the calls are in-the-money at expiration or net short stock if they are out-of-the-money and only the short stock remains. Volatility, as well, would move the payoff line vertically. As IV increases, the options become worth more at each stock price, and as IV falls, they are worth less, assuming all other factors are held constant.
A delta-neutral short-gamma play would have a P&(L) diagram quite the opposite of the smiley-faced long-gamma graph.
Exhibit 13.5
shows what is called the short-gamma frown.
EXHIBIT 13.5
Short-gamma frown.
At first glance, this doesn’t look like a very good proposition. The highest point on the graph coincides with a profit of zero, and it only gets worse as the price of the underlying rises or falls. This is enough to make any trader frown. But again, this snapshot does not show time or volatility.
Exhibit 13.6
shows the payout diagram as time passes.
EXHIBIT 13.6
The effect of time on the short-gamma frown.
A decrease in value of the options from time decay causes an increase in profitability. This profit potential pinnacles at the center (strike) price at expiration. Rising IV will cause a decline in profitability at each stock price point. Declining IV will raise the payout on the Y axis as profitability increases at each price point.
Smileys and frowns are a mere graphical representation of the technique discussed in this chapter: buying and selling realized volatility. These P&(L) diagrams are limited, because they show the payout only of stock-price movement. The profitability of direction-indifferent and direction-neutral trading is also influenced by time and implied volatility. These actively traded strategies are best evaluated on a gamma-theta basis. Long-gamma traders strive each day to scalp enough to cover the day’s theta, while short-gamma traders hope to keep the loss due to adverse movement in the underlying lower than the daily profit from theta.
The strategies in this chapter are the same ones traded in Chapter 12. The only difference is the philosophy. Ultimately, both types of volatility are being traded using these and other option strategies. Implied and realized volatility go hand in hand.