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A Jump-Diffusion Model for Option Pricing

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A Jump-Diffusion Model for Option Pricing A Jump-Diffusion Model for Option Pricing S. G. Kou Department of Industrial Engineering and Operations Research, 312 Mudd Building, Columbia University, New York, New York 10027 [email protected] rownian motion and normal distribution have been widely used in the Black–Scholes Boption-pricing framework to model the return of assets. However, two puzzles emerge from many empirical investigations: the leptokurtic feature that the return distribution of assets may have a higher peak and two (asymmetric) heavier tails than those of the normal distribution, and an empirical phenomenon called “volatility smile” in option markets. To incorporate both of them and to strike a balance between reality and tractability, this paper proposes, for the purpose of option pricing, a double exponential jump-diffusion model. In particular, the model is simple enough to produce analytical solutions for a variety of option-pricing problems, including call and put options, interest rate derivatives, and path- dependent options. Equilibrium analysis and a psychological interpretation of the model are also presented. (Contingent Claims; High Peak; Heavy Tails; Interest Rate Models; Rational Expectations; Overre- action and Underreaction) 1. Introduction fractal Brownian motion, and stable processes; see, for Despite the success of the Black–Scholes model based example, Mandelbrot (1963), Rogers (1997), Samorod- on Brownian motion and normal distribution, two nitsky and Taqqu (1994); (b) generalized hyperbolic empirical phenomena have received much attention models, including log t model and log hyperbolic recently: (1) the asymmetric leptokurtic features—in model; see, for example, Barndorff-Nielsen and other words, the return distribution is skewed to the Shephard (2001), Blattberg and Gonedes (1974); left, and has a higher peak and two heavier tails than (c) time-changed Brownian motions; see, for exam- those of the normal distribution, and (2) the volatility ple, Clark (1973), Madan and Seneta (1990), Madan smile. More precisely, if the Black–Scholes model is et al. (1998), and Heyde (2000). An immediate prob- correct, then the implied volatility should be constant. lem with these models is that it may be difficult to In reality, it is widely recognized that the implied obtain analytical solutions for option prices. More volatility curve resembles a “smile,” meaning it is a precisely, they might give some analytical formulae convex curve of the strike price. for standard European call and put options, but any Many studies have been conducted to mod- analytical solutions for interest rate derivatives and ify the Black–Scholes model to explain the two path-dependent options, such as perpetual American empirical phenomena. To incorporate the asymmet- options, barrier, and lookback options, are unlikely. ric leptokurtic features in asset pricing, a variety In a parallel development, different models are of models have been proposed:1 (a) chaos theory, also proposed to incorporate the “volatility smile” in option pricing. Popular ones include: (a) stochastic 1 Although most of the studies focus on the leptokurtic features volatility and ARCH models; see, for example, Hull under the physical measure, it is worth mentioning that the leptokurtic features under the risk-neutral measure(s) lead to the and White (1987), Engle (1995), Fouque et al. (2000); “volatility smiles” in option prices. (b) constant elasticity model (CEV) model; see, Management Science © 2002 INFORMS 0025-1909/02/4808/1086$5.00 Vol. 48, No. 8, August 2002 pp. 1086–1101 1526-5501 electronic ISSN KOU A Jump-Diffusion Model for Option Pricing for example, Cox and Ross (1976), and Davydov The “volatility smiles” phenomenon is illustrated in and Linetsky (2001); (c) normal jump models pro- §5.3. The final section discusses some limitations of posed by Merton (1976); (d) affine stochastic-volatility the model. and affine jump-diffusion models; see, for example, Heston (1993), and Duffie et al. (2000); (e) models based on Lévy processes; see, for example, Geman 2. The Model et al. (2001) and references therein; (f) a numerical 2.1. The Model Formulation procedure called “implied binomial trees”; see, for The following dynamic is proposed to model the asset example, Derman and Kani (1994) and Dupire (1994). price, St, under the physical probability measure P: Aside from the problem that it might not be easy to find analytical solutions for option pricing, espe- dSt Nt = dt+ dWt+ d V − 1 (1) cially for path-dependent options (such as perpet- St− i ual American options, barrier, and lookback options), i=1 some of these models may not produce the asymmet- where Wt is a standard Brownian motion, Nt is ric leptokurtic feature (see §2.3). a Poisson process with rate , and Vi is a sequence The current paper proposes a new model with of independent identically distributed (i.i.d.) nonneg- the following properties: (a) It offers an explana- ative random variables such that Y = logV has an tion for two empirical phenomena—the asymmetric asymmetric double exponential distribution2 3 with leptokurtic feature, and the volatility smile (see §§3 the density and 5.3). (b) It leads to analytical solutions to many −1y 2y option-pricing problems, including European call and fY y = p · 1e 1y≥0 + q · 2e 1y<0 put options (see §5); interest rate derivatives, such 1 > 1 2 > 0 as swaptions, caps, floors, and bond options (see §5.3 and Glasserman and Kou 1999); path-dependent where p q ≥ 0, p +q = 1, represent the probabilities of options, such as perpetual American options, bar- upward and downward jumps. In other words, rier, and lookback options (see §2.3 and Kou and +, with probability p Wang 2000, 2001). (c) It can be embedded into a ratio- logV = Y =d (2) nal expectations equilibrium framework (see §4). (d) It −−, with probability q has a psychological interpretation (see §2.2). where + and − are exponential random variables The model is very simple. The logarithm of the with means 1/ and 1/ , respectively, and the nota- asset price is assumed to follow a Brownian motion 1 2 tion =d means equal in distribution. In the model, plus a compound Poisson process with jump sizes all sources of randomness, Nt, Wt, and Y s, are double exponentially distributed. Because of its sim- assumed to be independent, although this can be plicity, the parameters in the model can be easily relaxed, as will be suggested in §2.2. For notational interpreted, and the analytical solutions for option simplicity and in order to get analytical solutions pricing can be obtained. The explicit calculation is for various option-pricing problems, the drift and made possible partly because of the memoryless the volatility are assumed to be constants, and property of the double exponential distribution. the Brownian motion and jumps are assumed to be The paper is organized as follows. In §2, the model is proposed, is evaluated by four criteria, and is com- 2 pared with other alternative models. Section 3 studies If 1 = 2 and p = 1/2, then the double exponential distribution is the leptokurtic feature. A rational expectations equi- also called “the first law of Laplace” (proposed by Laplace in 1774), while the “second law of Laplace” is the normal density. librium justification of the model is given in §4. Some 3 Ramezani and Zeng (1999) independently propose the same jump- preliminary results, including the Hh functions, are diffusion model from an econometric viewpoint as a way of given in §5.1. Formulae for option-pricing problems, improving the empirical fit of Merton’s normal jump-diffusion including options on futures, are provided in §5.2. model to stock price data. Management Science/Vol. 48, No. 8, August 2002 1087 KOU A Jump-Diffusion Model for Option Pricing one dimensional. These assumptions, however, can be have arbitrage opportunities, and thus are not self- easily dropped to develop a general theory. consistent (to give an example, it is shown by Rogers Solving the stochastic differential equation (1) gives 1997 that models using fractal Brownian motion may the dynamics of the asset price: lead to arbitrage opportunities). The double exponen- tial jump-diffusion model can be embedded in a ratio- 1 Nt nal expectations equilibrium setting; see §4. St = S0 exp − 2 t + Wt V (3) 2 i 2. A model should be able to capture some impor- i=1 tant empirical phenomena. The double exponential Note that EY = p − q , VarY = pq 1 + 1 2 + p + 2 jump-diffusion model is able to reproduce the lep- 1 2 1 2 1 q , and tokurtic feature of the return distribution (see §3) 2 2 and the “volatility smile” observed in option prices EV = EeY (see §5.3). In addition, the empirical tests performed 2 1 in Ramezani and Zeng (1999) suggest that the double = q + p 1 > 1 2 > 0 (4) 2 + 1 1 − 1 exponential jump-diffusion model fits stock data bet- ter than the normal jump-diffusion model. Andersen The requirement > 1 is needed to ensure that 1 et al. (1999) demonstrate empirically that, for the S&P EV < and ESt < ; it essentially means that 500 data from 1980–1996, the normal jump-diffusion the average upward jump cannot exceed 100%, which model has a much higher p-value (0.0152) than those is quite reasonable. of the stochastic volatility model (0.0008) and the There are two interesting properties of the dou- Black–Scholes model <10−5. Therefore, the combina- ble exponential distribution that are crucial for the tion of results in the two papers gives some empiri- model. First, it has the leptokurtic feature; see Johnson cal support of the double exponential jump-diffusion et al. (1995). As will be shown in §3, the leptokur- model.4 tic feature of the jump size distribution is inherited 3. A model must be simple enough to be amenable by the return distribution.
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