Types of Binary Options.
Binary options are trades with a fixed risk and reward. When you win a binary trade, you receive either a fixed amount of cash or a fixed amount of an underlying financial instrument. While this is true of all binary options, there are a number of variations on this theme, all of which have different conditions. What are the basic types of binary options you’re likely to encounter as you start learning how to trade?
Types of Binary Payouts.
There are different ways you can categorize binary options; one way to do so is according to payout. There are two ways you can receive your payment: cash or an underlying financial instrument. ” Cash or nothing ” is the name of the first type of payout, while ” asset or nothing ” is the name of the second kind. With these types of options, if you lose your trade, you “receive nothing.” When you see this phrase, however, don’t make the mistake of taking it too literally—it should read that you “receive nothing and forfeit your investment.”
American vs. European Style Binary Options.
You may also choose to classify binary options into American- and trades. With the trades, options may be exercised immediately once the asset reaches the predetermined strike price. So while the binary trade will still have a maturity date, you don’t have to wait for the asset to reach the strike price at the maturity period—you win as long as it does so before or at the expiration time. trades on the other hand do not allow you to profit unless the asset hits the strike price (or passes it in the direction you wagered) at the proper moment; if the asset hits the strike price before the maturity period is up but the trade reverses on you when the period expires, you still lose.
Common Types of Binary Options.
Four other types of trades you need to learn about are referred to as One Touch, No Touch, Double One Touch , and Double No Touch . “Touch” in these names refers to the underlying asset touching the strike price, which you may also hear referred to as a “trigger.” As you might deduce, the names refer to the conditions under which you might win a binary options trade.
One Touch and No Touch.
One Touch is the most basic of all these types of trades—it’s exactly what we’ve been talking about to this point. If the asset touches the strike price value a single time within the specified window, you win your trade. When you enter a No Touch trade, you’re wagering the exact opposite. Instead of betting that the asset will touch a certain strike price, you’re wagering that it will not touch a certain strike price. If within the specified time window price does not touch the price you indicated, you win the trade.
This might make you think, “Why not just pick a strike price that’s ridiculously far away from the current price and which is incredibly unlikely to ever be reached?” The reason this won’t work is that the payoff will be ridiculously low for such a bet, and will not be enough to make your trade worthwhile—you still have to pay the spread, and it won’t make up for it.
Double One Touch and Double No Touch.
Double One Touch you can think of as a trade where you’re hedging your bets. You choose two different strike prices in this scenario. If either strike price is reached during the expiration period, you win your trade. You might use this method when you expect price to break out but you aren’t sure in which direction. You could place a strike price on either side of the current price and see which way it goes. If price consolidates instead of hitting one of your targets, then you will lose your trade. If you place your strike prices ridiculously close to the current price, your payoff will be too small to justify your trade—again, there’s no cheating the system. No broker is going to pay you for making a bet on something which is more or less inevitable.
Double No Touch is another type of trade in which you choose two strike prices, only this time you’re betting that price will touch neither of the two values you set. If either value is touched during the specified time period, you lose your trade. If neither strike price is touched, then you win your trade. When would you choose a binary trade like this? One application might be during a time period when you believe that price is consolidating and isn’t going to be trending much above or below its current value. Traditionally it’s tough for traders to profit during consolidation (which tends to be a lot of what goes on in any market), but this is one way you could potentially do it.
Now you should feel more familiar with the types of binary options that are out there. This is by no means an exhaustive list, however, since there are many other ways in which it is possible to vary the conditions under which you trade. For example, the simplest type of trade might be one in which you double your investment if you win a trade or lose 100 percent of it if you don’t. But you could also enter into a trade where you win or lose a certain percentage of your investment.
Any variation on the type of binary option or the way in which you manage your money demands to be tested thoroughly before you start trading live with your own money. So when you’re backtesting and demo testing new methods of trading, try out some different types of binary trades as well, and understand that every time you vary the conditions under which you trade, you will have an impact on whether or not you’re going to be profitable—even using the same method. So different types of trades call for different methodologies. You may need to tweak your system for every type of trade you make, even if one method has the potential to work for multiple types of binary options.

Empirical tests of stochastic binary choice models.
This paper provides an experimental test of stochastic choice models of decisions. Models that admit Fechnerian structure are tested through the repeated pairwise choice problems. Results refute the Fechner hypothesis that characterizing the probability of selecting a given prospect increases in how strongly it is preferred to alternative choices. However, the experimental data lend support to characterizing an individual’s binary choice probability as some scalable functions of the von Neumann–Morgenstern utilities in the risky context.
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Availability of data.
The original experimental data are available upon request.
Notes.
Here, for expository purposes, I assume that A is convex, where A is a mixture set in the official model setups.
See Remark 1 in Ryan (2018) for a detailed discussion on this matter.
I conducted a parametric random effects regression analysis to detect whether subjects behave differently in the later experimental repetitions, as opposed to the earlier repetitions, for the binary choice problems in MMT 4. No significant impact from either the ranking of the repetitions or the different subjects was found on the choice pattern for the \(\ \) pairs.
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Acknowledgements.
The author thanks Pavlo Blavatskyy, Denis Bouyssou, David Butler, Simona Fabrizi, John Hey, Steffen Lippert, Alan Rogers, Matthew Ryan, and Ronald Peeters for their valuable comments. The author also thanks the audiences at the 20th Annual Society for check out the post right here Advancement of Economic Theory Meeting hosted by Seoul National University, at the International Summer School on ‘Preferences, Decisions and Games’ hosted by LIP6, Sorbonne University, at the 8th Microeconomic Theory Workshop hosted by Victoria University of Wellington; and at the 2018 Australia New Zealand Workshop in Experimental Economics, as well as the participants in research seminars at the University of Auckland and at Xi’an Jiaotong-Liverpool University for their helpful suggestions. The author also thanks the two anonymous referees and the editor for very useful comments on this paper.
Funding.
This research was funded by the University of Auckland Faculty Research Development Fund #3717595.
Author information.
Affiliations.
International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Business Building, South Campus, 8 Chongwen Road, Suzhou Dushu Lake Science and Education Innovation District, Suzhou Industrial Park, Suzhou, 215123, People’s Republic of China.
Centre for Mathematical Social Science at the University of Auckland, Auckland, New Zealand.