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Lattice models can accommodate a broader array of inputs with respect to employee exercise patterns, as well as volatility, dividend, and interest rate assumptions, over the option's contractual term.
Because of their flexibility, the financial community has long used lattice models for valuing options and other equity instruments. For example, a trader valuing an option that expires in three months might enter a single value for each of the six assumptions used in the Black-Scholes model. Using a lattice model, the same trader could enter a dynamic forecast with different volatility estimates for different sub-periods (e.g., days or weeks) of the option's three-month life. By incorporating the additional information from this dynamic forecast versus the single average volatility forecast that is input into the Black-Scholes model, the trader attempts to arrive at a more precise value for the option.
In a similar manner, lattice models can incorporate far more detailed assumptions about employees' future exercise patterns than the Black-Scholes model. The Black-Scholes model reduces all possible employee exercise patterns to a weighted-average that is used as a single input—the expected term—while lattice models can incorporate a range of inputs describing possible exercise behavior. A simple lattice model might incorporate an array of values for each of the four inputs related to employee exercise behavior:
  • Contractual term of the option – the maximum period for which the option can be held
  • Vesting period – the shortest period until the option can be exercised
  • Exercise multiple – also known as the suboptimal exercise factor, the exercise multiple is an assumption about "early exercise" behavior or patterns based on stock-price appreciation rather than the time that has elapsed since the grant date. It is described as the expected ratio of stock price to exercise price at the time of exercise. Early exercise refers to the exercise of an option prior to the end of the contractual term.
  • Post-vesting termination rate – the likelihood that an employee will be compelled to make an exercise decision prior to the conclusion of the option's contractual term

A more complex lattice model could incorporate considerably more information. Generally, lattice models incorporate the full contractual term of an option, and not simply the expected period until the option is settled (as in the Black-Scholes model).
For these reasons, ASC 718-10-55-17 through ASC 718-10-55-18 recognize that, in many cases, lattice models may provide a more accurate value of employee stock options than the Black-Scholes model. However, while a company might be able to calculate a slightly more refined value using a lattice model, it may not be worth the extra effort to achieve only a slightly different result. Therefore, very few companies currently use a lattice model to value "plain vanilla" at-the-money stock options. For those options, a Black-Scholes model is typically used. However, for companies valuing in-the-money options (such as those assumed in a business combination) that do not otherwise have a market condition, use of a lattice model may be justified. As noted in SC 8.2, awards with market conditions or payoff conditions that limit exercisability typically are valued using a lattice model or Monte Carlo simulation (refer to SC 8.6). 
Companies considering using a lattice model often engage an outside consultant to develop the model and analyze the necessary assumptions. Even when a valuation consultant is engaged, it is important for management to understand the valuation methodology and ensure the assumptions used in the model and the results of the valuation comply with the requirements of ASC 718 and SAB Topic 14.
In addition to the various assumptions that can be input into a lattice model, several different mathematical types of lattice model exist, including the binomial model, the trinomial model, finite-difference methods, and other versions of the lattice approach. There is also a related approach involving randomly generated simulated stock-price paths through a lattice-type structure called a Monte Carlo simulation. This section of the guide focuses on the binomial model, the simplest of these approaches, and we touch on Monte Carlo models in the next section. The binomial model accommodates a large number of potential future price points for the underlying stock over the option's contractual term, which can be varied depending upon the number of price points necessary to accurately simulate the real distribution of the stock's potential market prices.

8.5.1 A highly simplified binomial model

To better understand how binomial models work, consider the assumptions in Figure SC 8-5 regarding a stock option grant.
Figure SC 8-5
Stock option grant
Stock price on grant date
$100
Exercise price
$100
Vesting period (cliff vesting)
3 years
Contractual term
10 years
Expected term
6 years
Expected volatility of the underlying common stock
30%
Expected dividend yield on stock
0%
Risk-free interest rate (continuously compounded)
3%
The Black-Scholes model using the assumptions in Figure SC 8-5 yields an estimated fair value of $35.29. Employee early exercise patterns, post-vesting cancellations, and the other factors affecting the expected term assumption are reflected only indirectly in the expected term of six years. Regardless of expected stock price fluctuations, the Black-Scholes model assumes all option-holders will exercise their options six years after the grant date. It does not consider the full distribution of potential exercise times, which in this case, range from three years (the vesting date) to ten years (the contractual term), nor does it consider any possible correlation between stock price appreciation and the likelihood that employees will exercise their options (exercise multiple).
The first step in the application of the binomial model entails calculating the possible terminal values of the option (i.e., the possible intrinsic values at the end of its contractual term). This binomial model calculates a number of potential future stock prices based on the volatility and risk-free interest rate assumptions. Figure SC 8-6 illustrates this by assuming the stock price moves in discrete one-year intervals over the option's 10-year contractual life (one-year intervals were used for simplicity). A lattice model would normally use smaller time-steps and thus would encompass a smoother distribution of potential stock prices over many more possible values.
Binomial lattice models require two computations, called “binomial tree-diagrams,” in order to value a stock option. Figure SC 8-6 illustrates the first tree-diagram, in which the stock price begins at $100 (stock price on grant date) and increases or decreases according to certain assumptions over the ten-year period of the option’s contractual life. Figure SC 8-7, Figure SC 8-8, Figure SC 8-9, and Figure SC 8-10 illustrate different versions of tree-diagrams, in which the option value is calculated backwards from possible option-values on the settlement date to the theoretical starting value for the option.
In Figure SC 8-6, the binary forks in the tree-diagram determine the assumed annual prices to which the stock can move. Had the tree-diagram been drawn with more nodes (e.g., monthly or daily prices), these finite price points would resemble a smooth probability distribution. For basic tree-diagrams such as those presented in Figure SC 8-6, Figure SC 8-7, Figure SC 8-8, Figure SC 8-9, and Figure SC 8-10, the model simplifies reality by assuming the stock price must fall within a given range. This range widens over time. The size of the range is driven primarily by the volatility assumption, although risk-free interest rates may also influence these values in some versions of the lattice model. For example, at time t3 (the vesting date) the stock prices are assumed to be within a range from $269 to $44 based largely on the 30% volatility assumption. If the volatility was assumed to be 50%, the range of possible stock prices at t3 would be from $490 to $24. This wider range would result in a higher fair value for the option, because option value is derived only from the upside potential for stock price appreciation.
Figure SC 8-6
Simplified binomial model of potential stock prices
In Figure SC 8-7, option values are calculated "backwards" in time from time t6 to time t0. For simplicity, this figure demonstrates a simple valuation over the option's expected term of six years. Normally, a lattice model would simulate the entire contractual term (as illustrated in Figure SC 8-8, Figure SC 8-9 and Figure SC 8-10). However, Figure SC 8-7 is presented only over the expected term in order to provide a comparison to the fair value determined using the Black-Scholes model.
Figure SC 8-7
Binomial model of option prices with a six-year expected term
Figure SC 8-7 provides possible option values (rounded to the nearest dollar) at the end of each year of the option's life up to the expected term of the option. The possible values are based on the possible stock prices at time t6 (the expected term) illustrated in Figure SC 8-6. The option value at time t6 in Figure SC 8-7 is equal to the greater of (a) the stock price at the corresponding point in Figure SC 8-6 minus $100 (the exercise price of the option) or (b) zero—i.e., the intrinsic value of the option in each stock price scenario at the expected point of exercise.
The option values for points in time (tn) prior to time t6 are calculated by working backwards through the tree using established formulas. These formulas involve weighting the two possible values from the two possible nodes following any given node in the tree and discounting to reflect the time value of money. The weightings applied to each possible upward or downward move in the tree are calculated from the volatility and risk-free interest rate assumptions and resemble probabilities. In financial theory, these weightings are called risk-neutral probabilities (which differ from actual probabilities). Using the weightings to work backwards from the terminal (intrinsic) values at t6, the option's grant date fair value at t0 is derived from the various potential option values between t6 and t0.
In this example, the grant date fair value of the option obtained from this simple six step lattice model with an expected term of six years is $35.88 (rounded to $36), which is close to the $35.29 value calculated using the Black-Scholes model. Given identical assumptions, the results from a binomial model should draw even closer to the Black-Scholes result as the number of time-points or nodes shown in the binomial tree increases, because a large binomial tree approximates the type of continuous distribution assumed by the Black-Scholes model. However, because of the additional flexibility to incorporate more varied assumptions with lattice models, it is likely that the fair value estimates would not be as close in practice as in this example if varying assumptions about employee exercise behavior depending on stock price over the full contractual life of the option were used in the lattice model.
In practice, a binomial model would typically incorporate a large number of shorter time periods (often daily) to reflect a realistic range of possible prices that a share could achieve over the option's contractual term, which could result in several thousand total nodes. In addition, various probabilities could be assigned to each node to reflect the impact that a particular stock price scenario (node) is expected to have in conjunction with exercise and post-vesting termination assumptions.
A more robust result can be achieved by using an iterative technique called a Monte Carlo simulation (see SC 8.6), rather than developing a complex, full lattice model. This involves the use of a large sample (e.g., 1,000,000 or more) of possible outcomes through a randomly generated process that reflects the proportional distribution of each outcome's probability and formula-based rules regarding expected exercise patterns. When using one of these models, the fair value of the award is estimated by averaging the results of the sample outcomes to minimize sampling error. Accordingly, it is important that the number of outcomes used is sufficiently large.

8.5.2 Varying exercise patterns in option-pricing models

The example in Figure SC 8-7 still assumes a single value for the expected term of the option rather than the more varied employee exercise behavior that would occur in reality, which may include the correlation between possible stock price appreciation and the expected time of exercise. However, the main reason to build a binomial model is to incorporate such assumptions over the option's contractual term. Because complex exercise pattern assumptions are not reflected in Figure SC 8-7, the estimates of fair value produced by the Black-Scholes model and the simplified binomial model will converge given a sufficient number of nodes.
One method to incorporate early-exercise behavior assumes exercises based on stock price appreciation. As mentioned previously, a lattice model would simulate exercise behavior over the entire contractual term, rather than simply using the single average expected term as illustrated in Figure SC 8-7. Figure SC 8-8 shows another option valuation binomial tree-diagram, in which exercise is assumed to occur whenever the stock price reaches $200 (i.e., the stock to exercise price multiple of 2.0 is a "threshold" at which exercise is assumed to occur at a date prior to the end of the contractual term). The option value tree-diagram now covers the entire 10-year contractual life of the option instead of the six-year expected term as in Figure SC 8-7, since the option values must be simulated over the contractual life of the option in case the assumed exercise multiple is not reached. At time t10 (the end of the option's contractual life), the option is assumed to be exercised immediately if it has any intrinsic value at that point. If the stock price is less than the exercise price at time t10, the option expires worthless (i.e., the value is zero).
Figure SC 8-8
Option tree—ten-year contractual term with a 2.0 assumed exercise multiple
The values along the top boundary in Figure SC 8-8 will equal the option's intrinsic value (the greater of the stock price minus $100, or zero), similar to the values at time t6 in Figure SC 8-7. This boundary may be thought of as the "exercise frontier" (i.e., the points along the price-time continuum at which exercise is assumed to occur). As exercise is assumed to occur at these boundary points, no nodes above that line are necessary. The calculation proceeds "backwards" from the terminal values using risk-neutral probabilities and discounting for the time value of money. While the time-horizon imposed by the option's 10-year contractual life is reflected in this example, the constraint imposed by the three-year cliff vesting assumption has no effect because the highest potential stock-price at time t2 (the last node before vesting in our simple one step per year example) is $193, which is less than the assumed exercise threshold of $200. Refer to the corresponding node in Figure SC 8-6, which illustrates the potential stock prices; the values in Figure SC 8-8 above represent potential option-values.
The calculation shown in Figure SC 8-8 results in a fair value of approximately $42 or 17% higher than the approximately $36 fair-value (based on the static six-year expected term) from Figure SC 8-7. The use of an early-exercise assumption (i.e., the single average six-year expected term) will generally reduce the estimated fair value of an option as compared to a model that considers the full contractual life of ten years (on other than a dividend-paying stock, which can make it advantageous to exercise early in some circumstances). However, depending on where the assumed exercise multiple is set when exercise behavior is modeled based on stock-price appreciation, an option's fair value could be higher or lower than that of an otherwise similar option with an assumed static expected term.
To explore the relationship between this type of early-exercise assumption and an option's fair value, Figure SC 8-9 presents another example, identical to the scenario presented in Figure SC 8-8, except that exercise is assumed to occur whenever the price of the underlying stock reaches $130 (i.e., when the assumed exercise multiple reaches 1.3).
Figure SC 8-9
Option tree—ten-year contractual term with a 1.3 assumed exercise multiple
The calculations in Figure SC 8-9 result in a fair value of approximately $27, 36% lower than the fair value of approximately $42, calculated in Figure SC 8-8 (using an assumed exercise multiple of 2.0). This dramatic decrease shows the sensitivity of fair value to the assumed exercise multiple—by essentially truncating the model for significantly more valuable payouts by using a lower exercise multiple, the fair value of the award is much lower. However, the calculation in Figure SC 8-9 may require further adjustment to reflect the terms and conditions of the award. Specifically, the exercise frontier shown in Figure SC 8-9 includes potential exercise scenarios as early as one year after grant (i.e., at a price of $139 at t1, as shown in Figure SC 8-6), which precedes the three-year cliff vesting date. Therefore, the unadjusted fair value calculation is based on assumptions that are inconsistent with the terms and conditions of the award and must be adjusted.
Figure SC 8-10 illustrates the adjusted calculation for the exercise multiple of 1.3 limited by the option's three-year vesting condition. This results in an exercise frontier with three segments—a vertical barrier at time t3, to reflect the vesting condition, a horizontal barrier from t3 to t10, to reflect the exercise multiple of 1.3, and another vertical barrier at t10, to reflect the contractual term of 10 years. If the stock price were to go to its highest possible node at the end of the second year (time t2), the option would be exercised at the end of the next year, because the stock price will be above $130, with intrinsic value greater than $30 ($130 stock price minus $100 strike price) regardless of whether the stock prices moves up or down from time t2 to time t3. The resulting calculation moves the estimated fair value to $34.56 (rounded to $35), very near to its estimated fair value in the original binomial lattice using a six-year static expected term (approximately $35.88, rounded to $36 in Figure SC 8-7).
Figure SC 8-10
Option tree—ten-year contractual term with a 1.3 assumed exercise multiple limited by the three-year cliff-vesting condition
The results of the calculations in Figure SC 8-8, Figure SC 8-9 and Figure SC 8-10 are affected by the use of one-year time-steps in the lattice model. These time-steps are intended to illustrate the workings of the model. As noted earlier, a more realistic model would use shorter time intervals (e.g., daily) resulting in significantly more nodes. The model in Figure SC 8-7 with one-year time steps resulted in a valuation fairly close to the Black-Scholes value using a simple six-year expected term. In contrast, for the exercise assumptions in Figure SC 8-8, Figure SC 8-9 and Figure SC 8-10, a lattice model with smaller time intervals could produce values that differ by as much as 20% from those shown above. This is because the lattice values with the longer intervals may yield a stock price that well-exceeds an assumed exercise threshold in a single step when the option would theoretically be exercised at a lower price when shorter intervals are used. The values shown in the figures above are rough approximations illustrating the general relationship between results and model inputs with three-year cliff vesting and stock price volatility of 30%, as well as the exact calculations on a simplified basis (note the relationships will vary with different vesting schedules and volatility assumptions).
The examples shown above depict a constant exercise-frontier (except as affected by vesting or expiration of an option). In a more elaborate binomial model, the assumed early-exercise frontier may have a different slope or may be a probability distribution curve, rather than a straight line, that varies with both the price of the underlying stock and time. The binomial model can also incorporate additional assumptions regarding post-vesting cancellations, as discussed in SC 9.3.3.
For complex binomial models that reflect the correlation of stock price and early exercise, software applications may be employed to perform such modeling. As discussed further in SC 9.1, developing these models and the underlying assumptions manually will require considerable time and effort.
The lattice model also may be used to develop an implied expected term assumption, which is a required disclosure under ASC 718. The analysis of exercise patterns in a lattice model may yield an expected term that is shorter (or longer) than the expected term used in an otherwise similar Black-Scholes model. There are several methods to infer a single expected term from a lattice model, such as the method included in ASC 718-10-55-30, which solves for an implied expected term in the Black-Scholes model such that the Black-Scholes model's fair value equals the lattice model's fair value. Using this method, with an assumed exercise multiple of 2.0, the expected term assumption inferred in Figure SC 8-8 is approximately 8.2 years. Using the risk-neutral expected life method, the inferred expected term assumption is approximately 8.3 years. For typical options, the theoretical, inferred, risk-neutral expected term is much longer than the more realistic, and easily interpreted, implied Black-Scholes expected term.
There is a third method that would involve using a risk-adjusted expected rate of return in conjunction with early exercise assumptions built into the lattice model. The expected term assumption disclosed for companies using lattice models will therefore vary based upon the method used to infer it. The method used to infer the expected term should be applied consistently.

8.5.3 Using lattice models

Because lattice models are flexible, they can accommodate a variety of situations and assumptions. Four specific adaptations of lattice models are:
  • Dynamic assumptions: Assumptions about volatility, the risk-free interest rate, and the dividend yield, which can vary over the award's contractual term.
  • Awards with market conditions: Specific nodes of the lattice can be "turned off" to exercises to model an assumption that the option vests only if the underlying stock (or total shareholder return) reaches a pre-set level by a pre-set time (often called path-dependent models).
  • Awards with caps: Maximum value awards impose a contractual cap on the gain that employees may realize (e.g., the gain is capped at twice the grant date stock price). Lattice models are required to value such awards (or alternatively a Monte Carlo simulation model could be used). For an option, this is because the timing of early exercise for options with caps is generally more correlated with stock price appreciation as compared to ordinary options. As a result of this correlation and the limit on the gain that an employee may realize (for either an option or other award), the fair value of a maximum value option may be significantly lower than an ordinary option or uncapped award.
  • Incorporated patterns of early exercise: Assumptions that may include the correlation between the stock price and the time of exercise, forced early exercise due to post-vesting termination, and the probability of exercise over the full period from the vesting date to the option's contractual expiration date (see SC 8.5.4 for an illustrative example).

When valuing options with service conditions only, the primary reason to use lattice models instead of the Black-Scholes model is to incorporate more detailed assumptions about employee exercise behavior. Companies considering whether to use a lattice model or the Black-Scholes model should consider their specific circumstances. For options on shares of a company with a relatively low stock price volatility and a longer vesting schedule, a simple lattice model may not yield a significantly more refined estimate of fair value than a Black-Scholes model using an appropriately developed weighted-average expected term assumption. Further, not all companies will have the necessary historical data required to support a more complex lattice model. These factors, taken together with the option to use a simplified method to calculate the expected term for "plain vanilla" options (as described in SC 8.4.1), may make the Black-Scholes model the more practical approach for valuing many "plain vanilla" options.
In general, the development of appropriate assumptions—inputs to the valuation model—is more critical than the model—Black-Scholes or lattice—for many typical option grants. SC 9 discusses the factors to be considered in the development of assumptions.

8.5.4 Incorporating exercise patterns into a lattice model

To understand various techniques for incorporating early exercise patterns into a lattice model, consider a simplification that is used in many of the illustrations that appear in ASC 718. The exercise of 100% of the options is assumed to occur when the underlying stock reaches a certain price. Using this assumption is similar to using a single value for the expected term, except that it assumes options are exercised when a specific stock price is reached, instead of after a specific time period. An appropriate lattice model, at a minimum, should capture early exercise patterns as a function of at least four factors: (1) the assumed exercise multiple(s), (2) the vesting period, (3) the contractual term, and (4) the assumed post-vesting termination rate(s). These factors replace the single expected term assumption that is used in the Black-Scholes model.
Companies that intend to utilize lattice models should consider how much complexity to incorporate into the models and the effort necessary to gather the additional information needed to support the more sophisticated assumptions. In practice, simpler lattice models do not always produce results that are more refined or even as reliable as a Black-Scholes model with simple but well-supported assumptions.
As described earlier, the exercise-multiple (or suboptimal exercise factor) is an assumption about early exercise behavior based on stock price appreciation rather than the time that has elapsed since the grant date. This factor is called suboptimal because traditional financial theory suggests that the optimum behavior is to hold an option until its contractual expiration date. Although suboptimal from a financial theory perspective, it may nevertheless be reasonable for an employee to exercise stock options early, given the fact that typical employee options cannot be sold or hedged and considering individual employee's risk tolerance or liquidity needs. For example, a suboptimal exercise factor of 1.5 assumes that employees will voluntarily exercise options granted at-the-money when the price of the underlying stock price rises 50% above its price on the grant date. Typically, larger suboptimal exercise factors are associated with higher volatility stocks. Because of the sensitivity of an option's fair value to the early exercise assumption, it is particularly important that any suboptimal exercise factor in a lattice model be reasonable in the context of the specific company circumstances, the nature of the award, and the relevant employee demographics.
In addition to the other assumptions, lattice models should include an assumed post-vesting termination rate. Under most option plans, employees who terminate their employment have a short period (e.g., 90 days) to exercise their vested options. Lattice models typically assume that employees subject to truncation of the option's contractual term will exercise their options immediately upon termination if the options are in the money, and that out-of-the money options will always be cancelled upon termination.
In order to maximize the precision provided by a lattice model, more complex assumptions may need to be developed to reflect suboptimal exercise factors that change during the option's contractual term. For example, for an option with a three-year vesting provision and a ten-year contractual term, the assumed suboptimal exercise factor might be 1.8 in years 4-5, 1.5 in years 6-7, 1.4 in years 8-9, and 1.2 in year 10. Such an assumption reflects the notion that employees may demand larger payoffs to exercise options in the early years after grant but settle for less gain as the contractual term nears its end. Extending this concept even further, probability of early exercise can be added to the model to create a distribution of early exercise factors. For instance, in the above example for years 4-5, instead of assuming all employees will exercise when the stock price reaches 1.8 times the grant price, it could be assumed that, on average, one-third of the options will be exercised at a suboptimal exercise factor of 1.3, one-third at 1.6 and one-third at 1.9.
The following sections illustrate the use of suboptimal exercise factor(s) and the assumed post-vesting termination rate in a lattice model.

8.5.5 Dynamic suboptimal exercise factors in a lattice model

Figure SC 8-11 expands the binomial approach to reflect suboptimal exercise factors that change during the option's contractual term. This version of the lattice model uses a probability distribution of early exercises as it considers a scenario where employees would voluntarily exercise their options early (suboptimally) at stock price appreciation levels that vary by post-vesting sub-period. This distribution of early exercise patterns might be refined over time with the company's new grants to reflect the observed variance around the expected level of stock price appreciation that results in early exercise. Figure SC 8-11 illustrates an equally-weighted probability distribution using three different suboptimal exercise factors for each of four post-vesting sub-periods.
This example assumes that employees will, on average, exercise one-third of the outstanding vested options on each trading day when the stock price is at least equal to the lowest suboptimal exercise factor, an additional one-third of the outstanding vested options will be exercised when the stock price is at least equal to the midpoint suboptimal exercise factor, and the remaining one-third will be exercised when the stock price is at least equal to the highest suboptimal exercise factor. This probability calculation occurs at each node of the lattice to simulate trading days. In other words, the assumption is that there is a 33% probability of early exercise of the outstanding vested options on the trading days when the stock price is between the lowest and middle suboptimal exercise-factors, a 67% probability of exercise when the stock price is between the middle and highest suboptimal exercise factors and a 100% probability if the highest stock price level has been reached. In addition, a small number of employees will be assumed to terminate employment after vesting, meaning their options will be exercised immediately (if in-the-money) or cancelled (if out-of-the money).
This example uses a much more detailed binomial lattice than was used in the previous examples (Figure SC 8-6, Figure SC 8-7, Figure SC 8-8, Figure SC 8-9 and Figure SC 8-10). In order to incorporate an early exercise assumption, the binomial model used with the assumptions shown below has 252 nodes per year (to reflect the number of market trading days in a year) over a full ten-year period, so there are approximately three million possible nodes, as opposed to the 28 nodes in Figure SC 8-7. 
Figure SC 8-11 illustrates a binomial model with probability-based exercise distributions of suboptimal exercise factors.
Figure SC 8-11
Binomial model with probability-based exercise distributions of suboptimal exercise factors
Stock price on grant date
$100
Exercise price
$100
Vesting period (cliff vesting)
3 years
Contractual term
10 years
Expected volatility of the underlying common stock
30%
Expected dividend yield on stock
0%
Risk-free interest rate (continuously compounded)
3%

Years after grant date
Suboptimal exercise factors
Annual post-vesting termination rate
At least 3 but less than 5
1.3, 1.6, 1.9
3%
At least 5 but less than 7
1.2, 1.5, 1.8
3%
At least 7 but less than 9
1.1, 1.4, 1.7
3%
At least 9 but less than 10
1.05, 1.25, 1.45
3%
Monte Carlo techniques were used to simulate probability-based early exercise in Figure SC 8-11. The assumed suboptimal exercise factors decline over the option’s contractual term. This assumption is designed to replicate an effect observed by economists; namely, that employees may demand larger payoffs before voluntarily exercising their options when there is a longer time remaining in the contractual term for them to exercise. It is assumed that employees will exercise all in-the-money options by the expiration date. Figure SC 8-11 also assumes a constant post-vesting termination rate for simplicity.
Based on the assumptions in Figure SC 8-11, the binomial lattice model produces a fair value per option of $36.21. The increase over the fair value of $34.56 derived in Figure SC 8-10 based on a single suboptimal exercise factor of 1.3 reflects the higher suboptimal exercise factors in the earlier years from grant date (lower probability of early exercise). These fair values are both relatively close to the Black-Scholes model fair value for these options (as determined in Figure SC 8-5 using a 6 year expected term) of $35.29. For purposes of comparison, the implied expected term corresponding to this example equals 6.3 years. This implied expected term was calculated using the method described in ASC 718-10-55-30 (the expected term necessary for the Black-Scholes value to equal the lattice model value).
Companies should be cautious about using a single suboptimal exercise factor in their models, as they may underestimate fair value unless there is sufficient support for the assumption that there is a single level of price appreciation (measured as a proportion of exercise price) at which early exercise by employees actually tends to occur. However, a company will have difficulty either assessing reasonableness or estimating the effects of using various types of lattice models without actually building such models—like the example in Figure SC 8-11—and doing the work necessary to develop and support appropriate assumptions. In the absence of a lattice model that incorporates complexities, such as probabilistic exercise, companies may be better served by using the Black-Scholes model with well-supported assumptions rather than attempting to implement a simplistic lattice model, especially for "plain vanilla" awards with longer vesting schedules.

8.5.6 Option-pricing models for awards with market conditions

The terms of some awards require that vesting or exercisability depend on achieving a market condition. For example, an option with a market condition may provide that the option cannot be exercised unless the stock price rises by 50% from the grant date price. Performance shares (generally, a promise to issue shares, or entitle employees to vest in share awards, if certain performance targets are met) may also contain market conditions. Awards with market conditions require the use of a lattice model or a Monte Carlo simulation to estimate fair value. For example, a restricted stock unit may contain a provision that vesting is contingent on the company's total shareholder return exceeding the total shareholder return of a specified peer group over a stated number of years.
Figure SC 8-12 illustrates an option that will vest only after the stock has traded at $150 or more for twenty consecutive trading days and the employee completes three years of service. The option will lapse if the stock does not reach its targeted price within three years of the grant date. The award includes a service condition and a market condition.
Figure SC 8-12
Option that vest after three years of service if a targeted stock price is achieved within three years
Stock price at grant
$100
Exercise price
$100
Targeted (threshold) stock price
$150 (for 20 consecutive trading days)
Vesting period (cliff-vesting)
After 3 years of service if achievement of targeted stock price within 3 years of grant date
Expected term
Date of achievement of targeted stock price plus 3.5 years, which may vary from about 3.6 to 6.5 years depending when target price is reached (assumption not relevant if target price not reached because option will not vest)
Full contractual term
10 years
Expected annual volatility of the underlying stock
30%
Expected annual dividend yield on stock
0%
Risk-free interest rate (continuously compounded)
3%
A Monte Carlo simulation or lattice model should be used to estimate the fair value of an option with this type of market condition because it is the only way to simulate the many possible ways stock prices can move to meet the targeted stock price.
Using a Monte Carlo technique with daily stock price intervals to simulate an appropriately large binomial model yields a fair value estimate of $24.26. This fair value estimate is considerably less than the valuations of similar options without a market condition (see Figure SC 8-6, Figure SC 8-7, Figure SC 8-8, Figure SC 8-9 and Figure SC 8-10).
On the other hand, the estimate of $24.26 is greater than the valuation that would result if the actual stock price had to be at or above the targeted stock price on a specific vesting date, for example, three years after grant (with otherwise similar assumptions as in Figure SC 8-12). These differences should be intuitive in that an option with a market condition is clearly worth less than an option that vests over the same time regardless of stock-price appreciation. Further, an option that can achieve the target stock price anytime during a three-year period offers the holder greater flexibility (possible early vesting, with potential gains in the case of early steep stock-price appreciation) and thus should be worth more than an option that vests only if the stock price is at or above its target price upon completion of three years of service.
Other assumptions in the valuation model for awards with market conditions should be tailored to reflect the award's terms. For example, in Figure SC 8-12, because the options vest based on stock price movements, using a single expected term assumption would not be reasonable. Rather, a lattice model or Monte Carlo simulation is needed to reflect the fact that exercise could occur early if the stock price reaches $150 relatively early in the required three-year service period. The model uses a simplified exercise assumption of three and one-half years after achieving the target stock price to reflect the contingent nature of the vesting date and a typical holding period after vesting. More refined early exercise assumptions could also be appropriate.
A lattice model or Monte Carlo simulation should also be used to value an award that involves the achievement of multiple possible market conditions. For example, an option that will vest if the share price doubles within the next two years or if it triples within the next five years and the employee stays with the company until either condition is met should be viewed as one award, with a fair value determined by a lattice model or Monte Carlo simulation.
Market conditions are typically modeled using an approach that incorporates a Monte Carlo simulation (involving a series of random trials that may take different future price paths over the award's contractual life based on appropriate probability distributions). Conditions are imposed on each Monte Carlo simulation to determine if the market condition would have been met for the particular stock price path. For example, in modeling the market condition in Figure SC 8-12, each simulated stock price path was checked to determine whether the stock reached the $150 threshold during the vesting period.
The point at which the stock price achieves the threshold in each scenario in the simulation is also important in determining fair value. This technique for modeling awards with market conditions is called path-dependent modeling because it simulates many possible stock price paths through the lattice (or simulation) to arrive at the outcome. The award's grant date fair value is determined by taking the average of the grant date fair values under each of the scenarios in the Monte Carlo simulation.
In addition, the median path of successful trials for the market condition is used to develop the derived service period (as described in SC 2). The pattern of expense recognition will depend on this value in many cases (see SC 2.6.2 for more details).
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