Please research the variables that impact the pricing of options. Focus your energy on comparing the attributes of the two widely accepted models used for option pricing: Black-Scholes and Binomial Models. Your paper should be completed in Word and be approximately one to two pages in length following APA format. My Text book name is Practical Financial Management by William R. Lasher. Thank you in advance for your time and services.

Listed below is some information on the two models The Black and Scholes Model:

The Black and Scholes Option Pricing Model didn't appear overnight, in fact, Fisher Black started out working to create a valuation model for stock warrants. This work involved calculating a derivative to measure how the discount rate of a warrant varies with time and stock price. The result of this calculation held a striking resemblance to a well-known heat transfer equation. Soon after this discovery, Myron Scholes joined Black and the result of their work is a startlingly accurate option pricing model. Black and Scholes can't take all credit for their work, in fact their model is actually an improved version of a previous model developed by A. James Boness in his Ph.D. dissertation at the University of Chicago. Black and Scholes' improvements on the Boness model come in the form of a proof that the risk-free interest rate is the correct discount factor, and with the absence of assumptions regarding investor's risk preferences.

In order to understand the model itself, we divide it into two parts. The first part, SN(d1), derives the expected benefit from acquiring a stock outright. This is found by multiplying stock price [S] by the change in the call premium with respect to a change in the underlying stock price [N(d1)]. The second part of the model, Ke(-rt)N(d2), gives the present value of paying the exercise price on the expiration day. The fair market value of the call option is then calculated by taking the difference between these two parts.

Most companies pay dividends to their share holders, so this might seem a serious limitation to the model considering the observation that higher dividend yields elicit lower call premiums. A common way of adjusting the model for this situation is to subtract the discounted value of a future dividend from the stock price.

European exercise terms dictate that the option can only be exercised on the expiration date. American exercise term allow the option to be exercised at any time during the life of the option, making american options more valuable due to their greater flexibility. This limitation is not a major concern because very few calls are ever exercised before the last few days of their life. This is true because when you exercise a call early, you forfeit the remaining time value on the call and collect the intrinsic value. Towards the end of the life of a call, the remaining time value is very small, but the intrinsic value is the same.

This assumption suggests that people cannot consistently predict the direction of the market or an individual stock. The market operates continuously with share prices following a continuous Itô process. To understand what a continuous Itô process is, you must first know that a Markov process is "one where the observation in time period t depends only on the preceding observation." An Itô process is simply a Markov process in continuous time. If you were to draw a continuous process you would do so without picking the pen up from the piece of paper.

Usually market participants do have to pay a commission to buy or sell options. Even floor traders pay some kind of fee, but it is usually very small. The fees that Individual investor's pay is more substantial and can often distort the output of the model.

The Black and Scholes model uses the risk-free rate to represent this constant and known rate. In reality there is no such thing as the risk-free rate, but the discount rate on U.S. Government Treasury Bills with 30 days left until maturity is usually used to represent it. During periods of rapidly changing interest rates, these 30 day rates are often subject to change, thereby violating one of the assumptions of the model.

This assumption suggests, returns on the underlying stock are normally distributed, which is reasonable for most assets that offer options.

A binomial model is characterized by trials which either end in success (heads) or failure (tails). These are sometimes called **Bernoulli** trials .

Suppose we have *n* Bernoulli trials and *p* is the probability of success on a trial. Then this is a **binomial model**if

- 1.
- The Bernoulli trials are independent of one another.
- 2.
- The probability of success,
*p*, remains the same from trial to trial.

Don't those two assumptions look familiar? They should! This is nothing more than the rules for a random sample applied to a particular case; i.e., the sample items are independent of one another and conditions don't change from sample item to sample item.

The **binomial random variable** , *X*, is just the number of successes in the *n* trials. Over the *n* trials, there could be one success, two successes, etc., up to *n* successes. So the range of *X* is the set *{0, 1, 2, ... , n}*. We will often write *X* is *bin(n,p)** ,* which is read "*X* is binomial n, p". We can determine (obtain an explicit formula) for the probability model of *X*.

For this class, we have written some class code to obtain these probabilities. An example will demonstrate it. Suppose we want the probability of getting 7 heads in ten flips of a fair coin. That is, *X* is *bin(10,.5)* and we want *P( X = 7)*. In class code the input is:

- 1.
*k = 7*- 2.
*p = .5*- 3.
*n = 10*

Choose **probability** from the analysis menu, select the appropriate probability distribution and enter these values. You should get the results:

P( X = k) P( X <= k) 0.1171875 0.9453125

Hence, the probability of getting 7 heads in 10 flips of a fair coin is .1171875. Also, the probability of getting at most 7 heads in 10 flips of a fair coin is .9453125. At most 7 heads in 10 flips is the same event as 7 or less heads. These later probabilities will be useful.

As another example, suppose we have a fair spinner with the numbers 1 through 10 on it. Suppose success is a 1 or 2 or 3, while 4 through 10 are failures; i.e., we win if 3 or less is spun. Now suppose the spinner is spun six times. Let *X* be the number of times we win. Then *X* is *bin(6,.3)*. Lets determine the distribution of *X* by using the probability module. It's easy. Our input is *n=6*, *p = .3* and we let *k*vary from 0 through 6. Try it! You'll get (rounded to 4 places):

range: x 0 1 2 3 4 5 6 p(x) 0.1176 .3025 .3241 .1852 .0595 .0102 .0072

Notice how the distribution peaks around 2 and then decreases. This will always happen for a binomial.

In general, for a *bin(n,p)* the probabilities of a binomial will increase until ** np** and then decrease. The probability distribution will be symmetric if

Further, the mean of a *bin(n,p)* is and the variance is

Text book name is Practical Financial Management by William R. Lasher.

Subject | Business |

Due By (Pacific Time) | 06/02/2013 1000 |

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