# Oscillatory Reduction in Option Pricing Formula Using Shifted Poisson and Linear Approximation

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2 EPJ Web of Conferences price is a Geometric Brownian motion. Let () is a nondividend stock price in time and () is a stochastic process with independent and stationary increments and satisfies: () = (0) (), 0 Mathematically, the European option is defined by a payoff function () 0 and maturity date is. At time, the option holder will receive (()). We assume the interest rate is constant. Then the option price at time t is () Π()()], 0 <. * Sign in equation (1) states that the expected value is taken based on equivalent martingale measure appropriated to the observation of stock prices. So the equation price of the underlying asset is () = () () (), 0 <. (1) (2) [()] = [(() ) ] = In classical model, {()} is Wiener process. And for >0, random variables () has normal distribution with mean, variance and skewness =0. In this paper we discuss if 0. The idea for this skewness values is in the Complete Market martingale measure [7] are equivalent and lead to a unique option price. Unfortunately, the calculation of this option has a problem in close to zero, because the calculation process produces oscillations that can be seen in Figure 1. For this reason we would replace equality of martingale measure with linear approximation, which contains a calculation of Black-Scholes option price with the adjustment of skewness coefficient(). To construct the above idea, let {()} is a stochastic process with independent and stationary increments and satisfies: (4) (5) Since () is a process that has independent and stationary increments thus, the condition of equation (2) can be stated as (0) = () [(1) (0)](0) () = (0) () (0) (). Note that, (0) = 0, we have (0) = (0) () (0) (0) = (0) () (0)1 = (0) () (0). (0) We assume, = (0) (), =(0). Since and are the independent events, then (0) () (0) = (0) (). Use the above equation so we have Since (0) is deterministic 1= (0) (). (0) [()] =0 [()] = [() ] = () = () where, () = + +. Then we construct three-parameter-family defined as For, and we use () =() +, 0. =, =. Since equation (4) and (5) are satisfies, then we have (, ) = () = (()) = ()) =exp[() +]. Using equation (6), then equation (9) becomes (, ) = (6) (7) (8) (9) (10) 1= (). (3) To simplify notation of the calculation, we use =1 and calculate the option price at time =0. Let (, ) =[ () ] is moment generating function for () that satisfies: 3 Discussions In modified Black-Sholes formula for determining the option price we use two methods, namely Shifted Poisson models and then transforming into a Linear Approximation form as follows: (, ) =(), > 0, where () = (, 1) [()] = p.2

3 ICASCE Modification of the Black-Scholes Formula Using Shifted Poisson Model According [8] and [9] Shifted Poisson Model [10] is used to modify the Black-Scholes formula by letting: () =(). As already given in equation (8) then we can calculate the value of, [()] =( ) ( ) = = = =. = {()} equals to the unchanged value of k and c, but replaced with, which can be selected and satisfy equation (3). Then we get as follow () =1 (). =1 () =1 () =1. We know that () =exp (( 1)), then we have. =1 =1 ln [ + ( 1) ] =ln1 [ + ( 1) ] =0 = + 1. According to equation (1), then the price at time =0 and =1is () Π(1) (0). As has been proved in the previous equations, above equation becomes, Π(1). Because the expected value using the Poisson distribution, the above equations become, Π(1) = = = Π(1)!! Π((0) () )! Π((0) ). (11) Calculation of the European call option with exercise price and the payoff function Π() = ( ),where indicates a positive part of. Then, =if > 0 and = 0 if 0. Then the option price using Shifted Poisson model is = Rate of interest = Poisson intensity (0) = Stock Price = Exercise Price =. ((0) )! Or to simplify the calculation of equation (12) we can convert as follow (0) 1 (+) ; 1 (+) ; (0) = Stock Price = Exercise Price = (0) = Rate of interest = = =. This results in accordance with [11]. 3.2 Oscillation in Shifted Poisson Model But in equation (12) un-continuities occur when = 0 for some value of. Using equation (8) and value of, we have, = (12) ± 4 ( ) ± +4 2( =. ) 2 The meaning of the above equation is, this equation is a monotone sequence that converges to 0. And for skewness () close to zero, oscillation occur as shown in Figure 1. So the solution to reduce the oscillation is by transforming Shifted Poisson model into Linear Approximation form for option prices that contain the skewness p.3

4 EPJ Web of Conferences Figure 1. Option Price Calculation Using Shifted Poisson Model. 3.3 Linear Approximation in Option Price Calculation The basic idea of the linear approximation is by using k value containing the skewness in the MacLaurin expansion. Shifted Poisson transformed into the MacLaurin expansion: ln () = ( 1) = () ( 1) Using = = then the above equations become ln () = = () with = and () = ( )( ) + +, so the above equation can be converted into We can notify that exp[()] =1+() + (()) +, so we have () =exp (1 +() + ). (13) In equation (13) it can be seen that exp + is a form of the moment generating function of standard normal random variable, exp + = [exp()], where =+ with mean and variance. So we get exp = 1 ϕ. By using Maclaurin expansion and partial integral to the above equation, we obtained European call option pricing formula with Linear Approximation method, which can be written as (0) 1 Φ 1 Φ + (0)ϕ 1+ ϕ 1 (14) + () =exp + exp[()]. (0) = Non-dividend stock price at time 0 = Exercise price p.4

5 ICASCE 2013 Figure 2. Option Price Calculation Using Linear Approximation Method. = ln[/(0)] = Rate of interest = Variance = Mean Φ(. ) = Cumulative standard normal distribution function = = Skewness ϕ(. ) = Probability standard normal function In the European call option pricing formula with Linear Approximation method (14) it can be seen that the first term on the formula that is, (0) 1 Φ 1 Φ is a formula of the Black-Scholes model. Then the second term of the European call option pricing formula with Linear Approximation method is the rate of change between European call option pricing formula using Black-Scholes model, where =0, with a European call option pricing formula with Linear Approximation method, where 0. This results in accordance with [11]. Exercise Price (K) Table 1. Option Prices Comparison Black- Scholes Shifted Poisson Linear Approximation Results and Conclusion European call option price using Linear Approximation method for various values of skewness can be seen in Figure 2. By comparing the Black-Scholes formula, Shifted Poisson model and the Linear Approximation with various exercise prices we obtained the option prices Table 1. Black-Scholes formula has generally been used by both academics and investors in the European call option price calculation. Limitations of this formula are the factors that affect the price of this option is assumed to have normal distribution, but in fact the underlying assets do not have normal distribution, in other words, the underlying asset is influenced by skewness factor (γ). By using shifted Poisson models were modified Black- Scholes formula, but for the value of skewness is close to zero oscillation occurs which complicates the calculation process. Improvisation of this model is convert it to a Linear form so for skewness is close to zero option price calculation still produce an accurate value, which is close to the Blcak-Scholes option price. References 1. Martinkutė-Kaulienė, R. Business, Management and Education. 10 (2): (2012) 2. Miller, L., & Bertus, M., Business Education & Accreditation. 5 (2013) 3. T.Sunaryo, Manajemen Resiko Finansial, (Salemba Empat, Jakarta, 2009) 4. Zhu, Z., & Hanson, F. B, Social Science Research Network. (2013) 5. Gerber, H., & Landry, B., North American Actuarial Journal. 1 (1997) 6. Black, F., & Scholes, M., Journal of Political Economy. 81 (1973) 7. Hull, J. C., Options, Futures, and Other Derivatives. (Pearson Prentice Hall, New Jersey, 2012) 8. Gerber, H., & Shiu, E. Insurance: Mathematics and Economics. 18 (1996) p.5

6 EPJ Web of Conferences 9. Boyle et al. Financial Economics: With Applicationsin Insurance and Pensions. (Schaumburg: in press, 1997) 10. Lin, S. Introductory Stochastic Analysis for Finance and Insurance. (John Willey & Sons, Inc, Canada, 2009) 11. Gerber, H., & Shiu, E., North American Actuarial Journal. 1 (1997) p.6

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