Clark-Ocone Formula Research Articles

Hedging lookback-barrier option by Malliavin calculus in a mixed fractional Brownian motion environment

We present a Malliavin calculus approach to a mixed fractional Brownian motion option hedging model, that adequately describes, e.g., the hedging of a lookback-barrier option with the floating strike price. The Markovian setup and smooth stochastic differentials are necessary components in the payoff function for classical Δ-hedging of a contingent claim. This is in contrast to the Malliavin calculus approach, which may be used to any type of path-dependent options. Based on the fractional Clark–Ocone formula, an entirely probabilistic computation is developed to obtain the closed-form solution of the hedging portfolio for a lookback-barrier option with the floating strike price. Numerical experiments are performed to demonstrate the performance of our proposed hedging model, and we also conduct sensitivity analysis to investigate the correlation between model parameters and the hedging portfolio.

On the stochastic integral representation of Brownian functionals

Abstract In this paper, we study the question of representing one class of Brownian functionals as a stochastic Itô integral with an explicit form of the integrand. The considered class of functionals also includes functionals that are not smooth in the sense of Malliavin, to which both the well-known Clark–Ocone formula (1984) and its generalization, the Glonti–Purtukhia formula (2017), are inapplicable.

Application of fuzzy Malliavin calculus in hedging fixed strike lookback option

<abstract><p>In this paper, we develop a Malliavin calculus approach for hedging a fixed strike lookback option in fuzzy space. Due to the uncertainty in financial markets, it is not accurate to describe the problems of option pricing and hedging in terms of randomness alone. We consider a fuzzy pricing model by introducing a fuzzy stochastic differential equation with Skorohod sense. In this way, our model simultaneously involves randomness and fuzziness. A well-known hedging strategy for vanilla options is so-called $ \Delta $-hedging, which is usually derived from the Itô formula and some properties of partial differentiable equations. However, when dealing with some complex path-dependent options (such as lookback options), the major challenge is that the payoff function of these options may not be smooth, resulting in the estimates are computationally expensive. With the help of the Malliavin derivative and the Clark-Ocone formula, the difficulty will be readily solved, and it is also possible to apply this hedging strategy to fuzzy space. To obtain the explicit expression of the fuzzy hedging portfolio for lookback options, we adopt the Esscher transform and reflection principle techniques, which are beneficial to the calculation of the conditional expectation of fuzzy random variables and the payoff function with extremum, respectively. Some numerical examples are performed to analyze the sensitivity of the fuzzy hedging portfolio concerning model parameters and give the permissible range of the expected hedging portfolio of lookback options with uncertainty by a financial investor's subjective judgment.</p></abstract>

Application of Malliavin Calculus in Mean-Variance Hedging Strategy

This paper considers an approach of Malliavin calculus to obtain the hedging ratio for mean-variance hedging (MVH) strategy under the stochastic volatility model with pure jump Lévy process (SVJ). Specifically speaking, there exists a correspondence between the martingale representation theorem and the Clark-Ocone formula for a square integrable contingent claim. Therefore, we can replace the diffusion term coefficients with the functions containing Malliavin derivatives to get a closed-form representation for the MVH strategy. By fast Fourier transform (FFT) algorithm, some numerical examples are performed to analyze the sensitivity of MVH strategy concerning strike price and current time.

Higher-order deep solver of non-linear PDEs implied by a non-linear discrete Clark-Ocone formula

Higher-order deep solver of non-linear PDEs implied by a non-linear discrete Clark-Ocone formula

Higher-Order Error Estimates of the Discrete-Time Clark–Ocone Formula

In this article, we investigate the convergence rate of the discrete-time Clark–Ocone formula provided by Akahori–Amaba–Okuma (J Theor Probab 30: 932–960, 2017). In that paper, they mainly focus on the \(L_{2}\)-convergence rate of the first-order error estimate related to the tracking error of the delta hedge in mathematical finance. Here, as two extensions, we estimate “the higher order error” for Wiener functionals with an integrability index 2 and “an arbitrary differentiability index.”

Optimal Hedging Strategies for Options in Electricity Futures Markets

In this paper, we derive optimal hedging strategies for options in electricity futures markets. Optimality is measured in terms of minimal variance and the associated minimal variance hedging portfolios are obtained by a stochastic maximum principle. Our explicit results are particularly useful for electricity retailers, who have sold an option to a client, and now want to hedge the payoff of this option by investing into an electricity futures and into the issued option itself. Another innovative aspect of the paper lies in the derivation of the time dynamics of the stochastic option price process by Malliavin calculus methods and the Clark-Ocone formula. We also apply our theoretical results to several practical examples. Our investigations are based upon the popular arithmetic multi-factor electricity spot price model proposed by Benth, Kallsen & Meyer-Brandis (Appl. Math. Finance 14(2):153-169, 2007).

A Malliavin Calculus Approach to Minimal Variance Hedging

In this paper, we investigate the following problem: How can a financial institution, which has sold an option to a client, optimally hedge the payoff of this option by investing into a stock and into the option itself? Optimality is measured in terms of minimal variance and the associated optimal hedging portfolio is derived by a stochastic maximum principle. Moreover, we deduce the time dynamics of the stochastic option price process by Malliavin calculus methods, particularly by an application of the Clark-Ocone formula. We finally apply our theoretical results to several examples.

Martingale decomposition of an L2 space with nonlinear stochastic integrals

AbstractThis paper generalizes the Kunita–Watanabe decomposition of an $L^2$ space. The generalization comes from using nonlinear stochastic integrals where the integrator is a family of continuous martingales bounded in $L^2$ . This result is also the solution of an optimization problem in $L^2$ . First, martingales are assumed to be stochastic integrals. Then, to get the general result, it is shown that the regularity of the family of martingales with respect to its spatial parameter is inherited by the integrands in the integral representation of the martingales. Finally, an example showing how the results of this paper, with the Clark–Ocone formula, can be applied to polynomial functions of Brownian integrals.

Complex Wiener-Itô Chaos Decomposition Revisited

In this article, some properties of complex Wiener-Ito multiple integrals and complex Ornstein-Uhlenbeck operators and semigroups are obtained. Those include Stroock’s formula, Hu-Meyer formula, Clark-Ocone formula, and the hypercontractivity of complex Ornstein-Uhlenbeck semigroups. As an application, several expansions of the fourth moments of complex Wiener-Ito multiple integrals are given.

Functional inequalities for marked point processes

In recent years, a number of functional inequalities have been derived for Poisson random measures, with a wide range of applications. In this paper, we prove that such inequalities can be extended to the setting of marked temporal point processes, under mild assumptions on their Papangelou conditional intensity. First, we derive a Poincaré inequality. Second, we prove two transportation cost inequalities. The first one refers to functionals of marked point processes with a Papangelou conditional intensity and is new even in the setting of Poisson random measures. The second one refers to the law of marked temporal point processes with a Papangelou conditional intensity, and extends a related inequality which is known to hold on a general Poisson space. Finally, we provide a variational representation of the Laplace transform of functionals of marked point processes with a Papangelou conditional intensity. The proofs make use of an extension of the Clark-Ocone formula to marked temporal point processes. Our results are shown to apply to classes of renewal, nonlinear Hawkes and Cox point processes.

An Analytic Market Condition for Mutual Fund Separation: Demand for the Non-Sharpe Ratio Maximizing Portfolio

This study finds a necessary and sufficient condition for mutual fund separation, in which investors have the same portfolio of risky assets regardless of their utility functions. Unlike previous studies, the market condition is obtained in analytic form, using the Clark–Ocone formula of Ocone and Karatzas (Stoch Stoch Rep 34(3–4):187–220, 1991). We also find that the condition for separation among arbitrary utility functions is equivalent to the condition for separation among utility functions with constant relative risk aversion (CRRA utility functions). The condition is that a conditional expectation of an infinitesimal change in the uncertainty of an instantaneous Sharpe ratio maximizing portfolio can be hedged by the Sharpe ratio maximizer itself. In a Markovian market, such an infinitesimal change is characterized as an infinitesimal change in state variables. A closer look at the Clark–Ocone formula offers an intuition of the condition: an investor invests in (1) the Sharpe ratio maximizer; and (2) another portfolio in such a way as to reduce the uncertainty produced by an infinitesimal change in the Sharpe ratio maximizer, depending on four components: the investor’s wealth level, marginal utility and risk tolerance, at the time of consumption, and the shadow price. This decomposition is also valid in non-Markovian markets.

Clark-Ocone Formula for Generalized Functionals of Discrete-Time Normal Noises

The Clark-Ocone formula in the theory of discrete-time chaotic calculus holds only for square integrable functionals of discrete-time normal noises. In this paper, we aim at extending this formula to generalized functionals of discrete-time normal noises. Let Z be a discrete-time normal noise that has the chaotic representation property. We first prove a result concerning the regularity of generalized functionals of Z. Then, we use the Fock transform to define some fundamental operators on generalized functionals of Z and apply the abovementioned regularity result to prove the continuity of these operators. Finally, we establish the Clark-Ocone formula for generalized functionals of Z and show its application results, which include the covariant identity result and the variant upper bound result for generalized functionals of Z.

A Generalization of the Clark-Ocone Formula

In this paper, we use a white noise approach to Malliavin calculus to prove the generalization of the Clark-Ocone formula , where E[F] denotes the generalized expectation, is the (generalized) Malliavin derivative, ◊ is the Wick product and W(t) is the 1-dimensional Gaussian white noise.

A Clark–Ocone formula for temporal point processes and applications

We provide a Clark–Ocone formula for square-integrable functionals of a general temporal point process satisfying only a mild moment condition, generalizing known results on the Poisson space. Some classical applications are given, namely a deviation bound and the construction of a hedging portfolio in a pure-jump market model. As a more modern application, we provide a bound on the total variation distance between two temporal point processes, improving in some sense a recent result in this direction.

A Discrete-Time Clark–Ocone Formula and its Application to an Error Analysis

In this paper, we will establish a discrete-time version of Clark(-Ocone-Haussmann) formula, which can be seen as an asymptotic expansion in a weak sense. The formula is applied to the estimation of the error caused by the martingale representation. In the way, we use another distribution theory with respect to Gaussian rather than Lebesgue measure, which can be seen as a discrete Malliavin calculus.

Hedging in bond markets by the Clark-Ocone formula

Hedging strategies in bond markets are computed by martingale representation and the choice of a suitable of numeraire, based on the Clark- Ocone formula in a model driven by the dynamics of bond prices. Applica- tions are given to the hedging of swaptions and other interest rate derivatives and we compare our approach to delta hedging when the underlying swap rate is modeled by a diffusion process.

The covariation for Banach space valued processes and applications

This article focuses on a new concept of quadratic variation for processes taking values in a Banach space $B$ and a corresponding covariation. This is more general than the classical one of Metivier and Pellaumail. Those notions are associated with some subspace $\chi$ of the dual of the projective tensor product of $B$ with itself. We also introduce the notion of a convolution type process, which is a natural generalization of the Ito process and the concept of $\bar \nu_0$-semimartingale, which is a natural extension of the classical notion of semimartingale. The framework is the stochastic calculus via regularization in Banach spaces. Two main applications are mentioned: one related to Clark-Ocone formula for finite quadratic variation processes; the second one concerns the probabilistic representation of a Hilbert valued partial differential equation of Kolmogorov type.

Weak approximations for Wiener functionals

In this paper we introduce a simple space-filtration discretization scheme on Wiener space which allows us to study weak decompositions and smooth explicit approximations for a large class of Wiener functionals. We show that any Wiener functional has an underlying robust semimartingale skeleton which under mild conditions converges to it. The discretization is given in terms of discrete-jumping filtrations which allow us to approximate nonsmooth processes by means of a stochastic derivative operator on the Wiener space. As a by-product, we provide a robust semimartingale approximation for weak Dirichlet-type processes. The underlying semimartingale skeleton is intrinsically constructed in such way that all the relevant structure is amenable to a robust numerical scheme. In order to illustrate the results, we provide an easily implementable approximation scheme for the classical Clark–Ocone formula in full generality. Unlike in previous works, our methodology does not assume an underlying Markovian structure and does not require Malliavin weights. We conclude by proposing a method that enables us to compute optimal stopping times for possibly non-Markovian systems arising, for example, from the fractional Brownian motion.

Clark-Ocone formula by the S-transform on the Poisson white noise space

Given ' a square-integrable Poisson white noise functionals we show that the Segal-Bargmann (S-) transform t 㜀! S'(Pt(g)) is absolutely continuous with d dt S '(P t(g)) = S @ � E(@t'jFt ) � (g) for almost all t 2 R, where Pt(g) = 1(1 , t) �g for g in the Schwartz space S on R, and @t means the Poisson white noise derivative. After integration with respect to t and applying the inverse S-transform, this identity recovers the Clark-Ocone formula for '.