Search Menu Abstract Conventional estimates of the costs of taking liquidity in options markets are large. Nonetheless, options trading volume is high. We resolve this puzzle by showing that options price changes are predictable at high frequency, and many traders time executions by buying selling when the option fair value is close to the ask bid.
Price impact measures are also affected. These findings alter conclusions about the after-cost profitability of options trading strategies.
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Dollar percentage spreads were considerably wider what do you think about options well in-the-money out-of-the-money options. Conventionally measured effective half-spreads averaged 10 cents per share or 6. Given these quoted and effective spreads, the costs of taking liquidity in the options market seem high. But despite these apparently high costs, U.
For example, duringapproximately 3. The fact that presumably sophisticated investors are trading so many options at such seemingly high costs is a puzzle. Although the costs of options market making can help explain why options spreads should be higher than the spreads of their underlying stocks Battalio and Schultza second puzzle is that existing theories are unable to explain the observed patterns of spreads.
For example, the high dollar spreads of in-the-money ITM options and the relation between spreads and moneyness cannot be explained by hedge rebalancing costs incurred by options market makers, because hedges of well ITM options rarely need to be rebalanced.
Similarly, the pattern cannot be explained by difficult to hedge gamma and vega risks that options market makers bear when they hold inventories of options, because well ITM options are not exposed to these risks. Through the put-call parity relation ITM calls puts have gamma and vega risks similar to those of their corresponding out-of-the-money OTM puts callsbut much different spreads.
The differences between the spreads of options and their underlying stocks also cannot be explained by differences in the adverse selection component of the spread unless informed traders are much more common in the options market than in the stock market and they choose to trade ITM options that have less embedded leverage that other options. Finally, option volume increased substantially while quoted spreads decreased little during our sample period, which seems inconsistent with both theories in which reductions what do you think about options trading costs increase trading volume and theories in which increases in trading volume lead to lower costs per unit.
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This paper helps resolve these puzzles. This predictability is enhanced using lagged changes in stock and options prices and limit order book information. The predictors are all publicly available, and we interpret the expected future midpoints computed using the models as estimates of options fair values.
Options traders exploit this predictability in timing their executions. Executions at the ask price tend to occur when the estimate of the fair value the expected future midpoint is close to but less than the quoted ask price, and executions at the bid price tend to occur when it is close to but greater than the quoted bid price. Traders who exploit this predictability are able to take liquidity at low costs, as we explain next.
This trade timing does not imply the existence of frequent what do you think about options or arbitrage opportunities. Almost all of it occurs within the quoted spread. But if the timing of executions is correlated with the errors in using the midpoint as a proxy then the estimates of trading costs will be biased.
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This happens, for example, if executions are more likely to occur after some traders have entered aggressive limit orders, or if some traders are slow to cancel stale limit orders. For example, an execution at the ask price might occur when the estimate of fair value is two cents below the ask price but the quote midpoint is five cents below the ask price.
In this case the effective half-spread based on the fair value is only two cents but the conventional estimate based on the midpoint would be five cents.
We estimate the effective half-spread taking account of execution timing by using the predicted future midpoint as the estimate of fair value. The estimates help resolve the puzzle of why options trading volume is so high despite the seemingly very high trading costs: trading costs of investors who use execution algorithms to time executions are much lower than the conventional estimates of costs.
Averaging over trades by both timers and nontimers, the effective half-spread that accounts for trade timing is just 5. Because most execution timing is likely carried out using execution algorithms, we sometimes refer to the spread paid by execution timers as the algo spread.
Traders who do not time executions include retail investors who lack the sophistication or ability to time executions and also sophisticated investors who are able to time executions but desire immediacy of execution. Execution timing can serve as a discrimination mechanism resulting in different trading costs for these two groups of investors.
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The average effective spread of investors who do not time executions the nonalgo spread is equal to the conventional estimate based on the midpoint. This is also the spread paid by sophisticated investors who are able to time executions but desire immediacy.
Thus, the difference between the conventional and algo spreads is an estimate of both the extra cost paid by investors who are unable to time executions and the extra cost paid by investors who desire or require immediacy. Execution timing helps resolve the puzzle of why dollar spreads of ITM options are so much larger than those of OTM options even though such options are not exposed to the gamma and vega risks that are sometimes offered as an explanation for high options bid-ask spreads.
Indeed, the difference between spreads for OTM and ITM options drops by a factor of two after accounting for execution timing. Furthermore, trading costs for execution comparison of binary options are similar for all moneyness categories.
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We also provide measures of price impact that take account of execution timing. Conventional measures of price impact are estimated from the difference between the midpoints a short time after the trade and at the time of the trade.
We replace the pretrade midpoint with our estimate of value, the expected future midpoint, and obtain estimates of price impact that are less than one-half the size of the conventional measure.
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Our estimates of the costs of taking liquidity are upper bounds on the costs of trading by competing in the limit order book LOB because traders will only compete in the LOB if the expected costs of doing so, including the opportunity costs of unfilled limit orders, are less than the costs of taking liquidity.
Investors who want to take liquidity by timing their trades do not need to wait long, as the option quote midpoint converges more than halfway to our estimate of option fair value in fewer than 10 minutes. Why do option market makers not update quotes frequently? Even if liquidity providers are faster than most liquidity takers, if they are slower than only one they are at risk to get picked off.
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In addition, market frictions, such as minimum tick sizes, prevent market makers from continuously centering their quotes on the fair value. Thus, market makers can facilitate trading by cost sensitive investors by changing their quotes infrequently. Finally, taking account of the reduction in trading costs due to execution timing can alter conclusions about the net of trading cost profitability of options trading strategies.
We show this by backtesting two strategies that sell volatility by writing at-the-money straddles and holding them until expiration. Selling straddles according to these strategies produces high returns if transactions costs are disregarded. These strategies remain highly profitable, though of course less profitable, for traders who time executions and pay the algo effective half-spread.
However, both strategies have statistically insignificant returns if one pays transactions costs equal to the conventional effective half-spread.
These results provide guidance in interpreting the results of recent work on the returns of options trading strategies, including Driessen, Maenhout, and VilkovBali and MurrayCao and HanDoran, Fodor, and JiangBoyer and VorkinkMuravyevand Cao et al. Our approach for estimating options fair values is also potentially useful in research that involves estimating and calibrating options pricing models to market prices e.
This paper is also related to the literature that attempts to explain option bid-ask spreads using proxies for initial delta hedging costs, hedge rebalancing costs, and asymmetric information Jameson and Wilhelm ; George and Longstaff ; Cho and Engle ; De Fontnouvelle, Fishe, and Harris ; Kaul et al.
Although this literature has had some success, our findings help explain why the success is less than complete because the theories are unlikely to explain the bias in the conventional effective spread that stems from using the midpoint as a proxy for the value.
Data and Summary Statistics The main data are from Nanex, a firm specializing in delivering high quality data feeds. The Nanex data consist of all trades and intraday bid and ask quotes at 1-minute frequency for all U. The options quote data include the top of the limit order book for each options exchange, the stock quote data consists of the NBBO, and the timestamps are synchronized across markets.
A limitation of this data set is that in order to reduce storage requirements it includes only 1-minute snapshots of quotes rather than every quote update, and for each day includes only the quotes for options contracts with at least one trade during that day. Even so, the data set is very large: it is 15 What do you think about options in compressed form and more than TB uncompressed.
Internet Appendix Table IA. We include options trades with prices greater than ten cents. The first and last 5 minutes of trading are what do you think about options to avoid the opening and closing rotations. We winsorize all variables at 0.
After applying all filters, the final sample consists of approximately million option trades, or an average of about 4. Table 1 Summary statistics for options trades.