`ISRN Applied MathematicsVolume 2011, Article ID 518172, 7 pageshttp://dx.doi.org/10.5402/2011/518172`
Research Article

## Fourier Transform of Lookback Option Price

Department of Mathematics, China Jiliang University, Hangzhou 310018, China

Received 4 September 2011; Accepted 9 October 2011

Copyright © 2011 Cheng Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The Fourier transform of the damped price of Lookback option under B-S model is presented. Thus, the Lookback option across a range of strikes can be simultaneously priced via FFT algorithm. FFT algorithm is more efficient than both Monte Carlo simulation method and the integral of the usual pricing formula. In addition, by FFT algorithm, investors can easily capture the sensitivity of option prices when the strike prices vary as to make reasonable investment decisions.

#### 1. Introduction

Recently, the Fourier transform of option prices is of great interest to many researchers. Carr and Madan [1] use damped option price method to get the Fourier integral representation of standard European call and put option value. Motivated by the work of Carr and Madan [1], we use similar methods to give the Fourier transform of the damped price of Lookback option.

Although the Lookback option price has the explicit integral formula, the FFT algorithm is more efficient than the usual integral computations. In Section 1, we first get the characteristic function of the log maximum of stock prices on a time interval by a large amount of calculations, then we use the characteristic function to obtain Fourier transform of the damped price of Lookback option. Although the formulation is somewhat cumbersome, it is composed only of elementary functions, and it is readily applied on desktop computers. In Section 2, FFT algorithm is outlined to calculate the inversion of the Fourier transform obtained in Section 1. Thus, the Lookback option prices for a range of strikes can be obtained by only one FFT computation. In Section 3, we make a simple numerical experiment.

Here, we need the characteristic function of the log maximum of stock prices on a time interval, so we only consider the Black-Scholes model.

As usual, we assume that the stock price under the equivalent martingale measure (EMM) satisfies where is the risk-free rate, is the volatility, and is a -Brownian motion. The payoff at maturity of a Lookback call option with fixed strike is where .

We first give a lemma on Brownian motion which will be used in the next section.

Lemma 1.1 (see [2]). Suppose that is a standard -Brownian motion, is a constant, and , then the joint density function of is

#### 2. Fourier Transform of Damped Option Value

From (1.1), So, and , where .

To obtain the characteristic function of , we first calculate the density function of .

For simplicity of writing, we use to denote , , , and to denote the distribution function, density function, and characteristic function of standard normal distribution , respectively, in the following.

Lemma 2.1. The density function of is

Proof. From Lemma 1.1, in the instruction, the density function of is
Since so The proof is completed.

Proposition 2.2. The characteristic function of is

Proof. From Lemma 2.1, the characteristic function of is
Since the first term in the brackets is and the second term in the brackets is so the characteristic function of is The proof is completed.

Recall that and , then from Proposition 2.2, we can easily obtain the characteristic function of (denoted by ) as stated in the following theorem.

Theorem 2.3. The characteristic function of is
Let denote the log strike price, that is, , and the Lookback call option price at time-0. To obtain a square-integrable function, one uses the damped option price [1]; that is, let for . The discussion for the choice of in Carr and Madan [1] is applicable here. In Section 3, one makes a simple numerical experiment for .

We write as the Fourier transform of , as the density function of , and as the characteristic function of . Then, we have From Theorem 2.3, we have where .

#### 3. Using FFT to Price Lookback Option

thus can be calculated by taking the Fourier inversion transform where denotes the real part of a complex number and the second equality is due to that is odd in its imaginary part and even in its real part.

The above integral can be computed using FFT. A numerical approximation for is where . Discussions on the errors in the numerical computing are presented in Lee [3].

The FFT is an efficient algorithm that computes the sum of the following form: The interesting values of the strike price is around the forward price, that is, , so we calculate the sum (3.2) for .

For (3.2) to be transformed to the form of (3.3), we let (then ranges from 1 to ), , and be an integer power of 2.

Then, (3.2) turns to be where is the counterpart of in (3.3). So, (3.4) can be calculated by FFT quickly.

#### 4. Simple Numerical Experiment

In our example, ; ; sigma = 0.3; ; ; delta, and we use matlab software as a computing tool.

Motivated by Lord et al. [4], we let ranges from 1 to 3, increasing 0.1 each step. Then, we calculated the option prices against strikes around the forward future price for every . Fix , and we find that for each , the curves of option prices against strikes are almost the same curve, as Figure 1 shows. It shows the results are relatively stable for different .

Figure 1: Option prices against strikes .

We take and compare the results with Monte Carlo method. See Figure 2, where in FFT algorithm and Monte Carlo method do simulations. Although the results are similar, FFT is much more efficient than Monte Carlo simulation.

Figure 2: The results of FFT and Monte Carlo.

#### 5. Conclusion

Although the Lookback option price has the explicit integral formula, the FFT algorithm is more efficient than the usual integral computations. Also, using FFT calculation once, practitioners can directly capture the price sensitivity of an option with varying strike prices. Using the same technique above, we can also obtain the Fourier transform of Lookback call and put options with floating strikes under Black-Scholes model. For asset prices under Lévy processes, Feng and Linetsky [5] take the asset prices on discrete time points to get the approximate price of Lookback options; Kou [6] give a survey of several discretization method to price Barrier and Lookback options.

#### Acknowledgment

This paper was supported by NSF 10901137, China.

#### References

1. P. Carr and D. Madan, “Option valuation using the fast Fourier transform,” Journal of Computational Finance, vol. 2, no. 4, pp. 61–73, 1999.
2. F. C. Klebaner, Introduction to Stochastic Calculus with Applications, Imperial College Press, London, UK, 2nd edition, 2005.
3. R. Lee, “Option pricing by transform methods: extensions, unification, and error control,” Journal of Computational Finance, vol. 7, no. 3, pp. 51–86, 2004.
4. R. Lord, F. Fang, F. Bervoets, and C. W. Oosterlee, “A fast and accurate FFT-based method for pricing early-exercise options under Lévy processes,” SIAM Journal on Scientific Computing, vol. 30, no. 4, pp. 1678–1705, 2007.
5. L. Feng and V. Linetsky, “Computing exponential moments of the discrete maximum of a Lévy process and lookback options,” Finance and Stochastics, vol. 13, no. 4, pp. 501–529, 2009.
6. S. G. Kou, “Chapter 8 discrete barrier and lookback options,” Handbooks in Operations Research and Management Science, vol. 15, pp. 343–373, 2007.