The Journal of finance through time, this is not a necessary assumption for the implementation of the model. Thus, if one can simply specify the pattern of H* and H"through time it is possible to value the option The T'SoPM can be used as a method for obtaining exact values of opti hen the magnitudes of h* and h- are known in advance. As a practical matter, the values of H* and H will not be known, but must be estimated. For example if the probabilities associated with the occurrence of the + and -states remain stable over time along with the magnitudes of H* and H, then the two-state model implies a binomial distribution for the returns of the stock. It is well known that both the Normal and Poisson distributions can be viewed as limiting cases of the Binomial. Thus, the Binomial distribution can be employed as an approx imation procedure for deriving option prices when the actual distribution of returns is assumed to be either Normal or Poisson We will illustrate how the values of H and H can be determined when the binomial distribution is used as an approximation to the lognormal distribution If the magnitudes of the relative price changes in our model and their associated probabilities remain stable from one period to the next, then the distribution of returns which is generated after T time periods will follow a log-binomial distribution with a mean μ=T[h6+h(1-的]=T(h-h)日+h], and variance T(h-h)26(1-的), here 6= the probability that the price of the stock will rise in any period, h=ln(H+) h=In(h) and u for the entire four periods would be 324 and -003, respectively, if a f a In the last four-period example where H'= 1.175 and H-"=85, the value of of equal to 5 is assumed It is also possible to determine the values of H*and H that are implied by the values of 4, 0, 0, and T. By solving (8)and (9) in terms of these parameters and recognizing that H exp(h), we obtain the following implied values of H+ and H-exuT+T√ (10) H=exy{/T-(a/m)V(1-6) (11) As T becomes large, the log-binomial distribution will approximate a lognormal distribution with the same mean and variance