The pathwise method calculates the derivative of the option price with respect to the parameter in question. Instead, the derivative of the probability density of the underlying variable is calculated instead of the derivative of the option price. The main difficulty is that the discontinuity of the option price when using the above standard approaches will cause many technical problems.
The main problem is that the discontinuity of the option price will cause many technical problems using the above standard approaches. We define an option priceu(α) as the probabilistic representation of the payoff function Φ that depends on the stochastic process XT given by. If α is the initial price x, then we have the Delta which is defined as the derivative of the option price with respect to the initial price, and is denoted by.
Literature review
Structure of the dissertation
In Chapter 5 we apply some important properties of the Malliavin calculus to calculate the general representation formulas of the price sensitivities 'Greeks' such as Delta, Gamma and Vega, considering standard Brownian motion. We introduce the Gaussian Hilbert spaces [18], which are real or complex inner product spaces that are also a complete metric space with respect to the distance function induced by the inner product. We also look at some properties of Malliavin calculus, such as the Malliavin derivative and the Skorohod integral in the Brownian sense of motion.
Then L2(Ω) is a Hilbert space with inner product hf, gi=. The Hilbert space is complete with respect to convergence in the norm topology:. A Gaussian linear space can always be completed to a Gaussian Hilbert space, which is well indicated by the following theorem. The Gaussian Hilbert space W(H) generated by Y has a well-known characterization given in the following theorem.
Wiener’s construction of Brownian motion and the stochastic integral
The Mallaivin derivative
The space of polynomials in elements of W(H)
We note that W(H) is not an algebra, that is, the product of Gaussian random variables generally have non-Gaussian distributions, so most elements of P(H) are not in W(H).
The Malliavin derivative on P (H)
Extending the Domain of the derivative
If A is closable and we know F on a subset S of the space, then we can extend A to an operator on the closure of S by defining AF = G whenever there exists a sequence Fn −→F so that AFn −→G. In general, it will not be defined on the whole of L2(Ω) and will not be continuous. Let ϕ(x) =ϕ∗ψ, where ψ is an approximation of the identity for >0 , x∈R where ψ is an infinitely continuous positive function with support in [−1,1] such that R
On the other hand, from the dominated convergence theorem, we have that for every k ≥1, (B) converges to zero as →0. Thus, for any weakly convergent subsequence, the limit must be equal to τ and this implies the weak convergence of the entire sequence.
Skorohod integral
The Clark-Ocone formula
In addition, if F ∈D1,2, it turns out that the processφ can be expressed as a Malliavin derivative of F. Then we have any ϕ∈L2([0, T]×Ω) using Itˆo isometry and that the expected value of an Itˆo integral is zero. 2 The above theorem shows that the Malliavin derivative provides an identification of the integrand in the martingale representation theorem in a Brownian motion framework.
Therefore, the hedge portfolio is naturally related to the Malliavin derivative D of the terminal payoff. In this chapter, we discuss the existence, uniqueness, and smoothness of solutions of stochastic differential equations. Let Ω =C([0, T],R) and P be the Wiener measure, and let F be the completion of the Borel σ−field Ω with respect to P.
We want to show that there is a unique continuous solution (3.4) such that for all t ∈ [0, T] the random variable Xt belongs to the space D1,2. Moreover, if the coefficients (3.4) are infinitely differentiable and their partial derivatives of all types are uniformly bounded, then the process Xt belongs to D1,2.
Existence and uniqueness of solutions
Weak differentiability of the solution
WEAK DIFFERENTIABILITY OF THE SOLUTION 31By applying Doob's maximum inequality and Burkholder's inequality and making use of. Consider n-dimensional stochastic differential equation of the form (3.4) under the usual conditions on the coefficients. By applying Itˆo's lemma, we see that the solution to stochastic differential (3.4) is given by.
If the coefficientsb and σ are constants, the process Xt in the integral form is given by. We apply the Clack Ocone formula [30] to the calculation of the replicating portfolios in the sense of the Malliavin calculus and provide some examples based on the different types of payoff functions. We also assume that the drift coefficient b and the diffusion coefficientσ are uniformly bounded and that the functiong is a measurable function and that there exists a constant C > 0 such that.
The following representation theorem for the hedging portfolio φ can be considered as a special case of the Clark-Ocone formula [7]. That gn are smooth functions such that the partial derivatives ∂x∂ gn are uniformly bounded, gn→g uniformly and ∂x∂ gn(x)→ ∂x∂ g(x) for allx6∈A as n→. In practice, such an assumption is not easy to verify, especially in the case when dimension < n.
We first show that the law of XˆT is absolutely continuous with the Lebesgue measure on Rd, denoted by|.|d. Now we can conclude that the law of ˆXT is absolutely continuous with respect to |.|d, which if we combine with the fact that |A|ˆd= 0, we see that P( ˆXT ∈A) = 0. I chapter 1 , we gave a digital option as an example where the payoff function is discontinuous.
Replicating portfolios (general case)
Examples
The first is the specific case of the European option where the payoff function B is given by. We consider an option whose payoff function is the average of the stock price given by S¯T1 = 1.
Construction of Hedging portfolio
Black-Scholes model
Here, the goal is to calculate a replicative portfolio for the Black-Scholes model based on the calculation of the Malliavin derivatives of the corresponding stochastic processes. The main tool we will use to calculate the re-portfolio is the Clark-Ocone formula from Theorem 2.5.9. If c(t) = 0, then the condition H˜TB ∈ D1,2 implies that the portfolio imitating the random variable B is given by.
CONSTRUCTION OF THE HEDGING PORTFOLIO BEFORE HEDGING 49 where the last equality is obtained using (2.15).
Replication of European call option
Now for DtST, we obtain the Malliavin derivative of the solution of the stochastic differential equation given by (4.52). We first define the option price u(·) as the probabilistic representation of the payoff function Φ given by. The goal is to obtain the partial derivative of the option price u(·) with respect to the underlying factor.
The second term is uniformly integrable since the partial derivative of the payoff function Φ is assumed to be bounded. Example 5.0.4 Now for a geometric Brownian motion given by the stochastic differential equation of the following type. This is actually the second derivative of the option price with respect to the initial price x and it is given by the following result [22].
To calculate the partial derivative of the option price u(x) with respect to the diffusion coefficient matrix σin the ˜σ direction, we consider the stochastic perturbed process {Xt : 0≤t ≤T} defined by. This is actually the partial derivative of the process Xt with respect to given by. The following proposition gives the partial derivative of the perturbed option price u(x) with respect to the small parameter at= 0 given by.
From Lemma 5.0.1 we note that the partial derivative of the option price u(x) with respect to the small parameter is actually obtained by differentiating within the expectation operator. If we choose versions of the process {Xt : 0≤t≤T} that are continuously differentiable with respect to Using the integration by parts formula from statement 2.5.4 we conclude that.
VARIATIONS IN THE DIFFUSION COEFFICIENT 63 Example 5.1.4 The last example of price sensitivity is Vega (V).
Greeks w.r.t the correlation in a stochastic volatility model
The independent case
We note that in all cases where we have calculated the price sensitivities, there are no direct calculations of the derivative of the payoff functions. All the results we obtained indicate that the efficiency of the Malliavin calculus in calculating price sensitivities does not depend on the type/nature of the payoff function. Indeed, using the parts integration formula, we have seen that a Greek can be represented as the expectation of the product of the payoff function and Malliavin's weight function.
This study was based on the calculation of hedging portfolios and price sensitivities, known as Greeks, in the case where we have discontinuous payoff functions using the Malliavin calculus approach. With this formula, we avoid the direct derivation of the functional, instead we get the product of the functional and the so-called Malliavin weighting functions. The integration-by-parts formula plays a major role in calculating price sensitivity.
As a result, we constructed the first variational process which is the partial derivative of the stochastic differential equation with respect to the initial condition. For the implementation of Malliavin's calculus in mathematical finance, we used the Clark-Ocone formula to obtain the general representation formula of the iteration strategy. The general formula was applied to various types of European-type profit functions, where we realized that the hedge portfolio is naturally related to the Malliavin derivative of the terminal profit.
The properties of Malliavin's calculus are used to calculate a general price sensitivity representation formula that includes Delta (∆), Gamma (Γ) and Vega (V). We considered 2-dimensional correlated Brownian motion, where we calculated the sensitivity of the chance with respect to ρ. We conclude the study by considering 3-dimensional uncorrelated Brownian motion and recalculate the Delta, Vega and Rho of the option price.
We would also like to apply similar concepts to American-type option prices where the exercise of the option occurs at or before the expiration time T.
