Ito Calculus Research Articles

Exponential Dispersion accelerated degradation modelling and reliability assessment considering initial value and processes heterogeneity

In the production and operation, inherent variability and uncertainty necessitate addressing unit-to-unit heterogeneity in initial performance values and degradation processes. This article presents a bi-stochastic exponential dispersion process (BS-ED) designed to account for heterogeneity in both initial performance values and degradation processes. First, based on the ED process, the time and acceleration covariates are introduced to form a nonlinear accelerated ED process, and a random effect coefficient associated with the accelerated stress is incorporated to consider the heterogeneity of the process. Meanwhile, through the modelling of degradation time-shift, a degradation model considering the stochastic initial value of the product performance is developed. To effectively conduct the statistic inference of the BS-ED process, an improved stochastic EM algorithm is proposed, and the information matrix and Ito calculus are combined to estimate the confidence intervals. Finally, the stability of the method is verified by simulation and analyzed by two real cases.

Functional Solutions of Stochastic Differential Equations

We present an integration condition ensuring that a stochastic differential equation dXt=μ(t,Xt)dt+σ(t,Xt)dBt, where μ and σ are sufficiently regular, has a solution of the form Xt=Z(t,Bt). By generalizing the integration condition we obtain a class of stochastic differential equations that again have a functional solution, now of the form Xt=Z(t,Yt), with Yt an Ito process. These integration conditions, which seem to be new, provide an a priori test for the existence of functional solutions. Then path-independence holds for the trajectories of the process. By Green’s Theorem, it holds also when integrating along any piece-wise differentiable path in the plane. To determine Z at any point (t,x), we may start at the initial condition and follow a path that is first horizontal and then vertical. Then the value of Z can be determined by successively solving two ordinary differential equations. Due to a Lipschitz condition, this value is unique. The differential equations relate to an earlier path-dependent approach by H. Doss, which enables the expression of a stochastic integral in terms of a differential process.

Simulating variable‐order fractional Brownian motion and solving nonlinear stochastic differential equations

Stochastic differential equations (SDEs) are very useful in modeling many problems in biology, economic data, turbulence, and medicine. Fractional Brownian motion (fBm) and variable‐order fractional Brownian motion (vofBm) are suitable alternatives to standard Brownian motion (sBm) for describing and modeling many phenomena, since the increments of these processes are dependent of the past and for these increments have the property of long‐term dependence. Classical mathematical techniques such as Ito's calculus do not work for stochastic computations on fBm and vofBm due to they are not semi‐Martingale for . Therefore, solving these equations is much more difficult than solving SDEs with sBm. On the other hand, these equations do not have an analytical solution, so we have to use numerical methods to find their solution. In this paper, a computational approach based on hybrid of block‐pulse and parabolic functions (HBPFs) has been introduced for simulating vofBm and solving a modern class of SDEs. The mechanism of this approach is based on stochastic and fractional integration operational matrices, which transform the intended problem to a nonlinear system of algebraic equations. Thus, the complexity of solving the mentioned problem is reduced significantly. Also, convergence analysis of the expressed method has been theoretically examined. Finally, the accuracy and efficiency of the proposed algorithm have been experimentally investigated through some test problems and comparison of obtained results with results of previous papers. High accurate numerical results are obtained by using a small number of basic functions. Therefore, this method deals with smaller matrices and vectors, which is one of the most important advantage of our suggested method. Also, presenting an applicable procedure to construct vofBm is another innovation of this work. To gain this aim, at first, discretized sBm is generated via fundamental features of this process, and afterward, block‐pulse functions (BPFs) and HBPFs are utilized for simulating discretized vofBm. Finally, spline interpolation method has been employed to provide a continuous path of vofBm.

Revisiting Stochastic Realization Theory using Functional Itô Calculus

This paper considers the problem of constructing finite-dimensional state space realizations for stochastic processes that can be represented as the outputs of a certain type of a causal system driven by a continuous semimartingale input process. The main assumption is that the output process is infinitely differentiable, where the notion of differentiability comes from the functional Ito calculus introduced by Dupire as a causal (nonanticipative) counterpart to Malliavin's stochastic calculus of variations. The proposed approach builds on the ideas of Hijab, who had considered the case of processes driven by a Brownian motion, and makes contact with the realization theory of deterministic systems based on formal power series and Chen-Fliess functional expansions.

Using optimal control without loss the money in future

The purpose of writing this paper is to demonstrate a short study to learn how the person to maximize his money by using an optimal control without loss. The purpose of the control is to maximize the expected payoff in the future . We have in this paper very valuable result .
 If we have found an optimal decision that will bring some positive inpute in future . We know that to maximize our money with out losing is actually very necessary part in our life.
 
 Suppose that the state of a system at time t is described by ito process
 of the form
 
 Markov control is assume that does not depend on the starting point , the value we choose at time only dependes on of the system at this time.

Stochastic optical solitons of the perturbed nonlinear Schrödinger equation with Kerr law via Ito calculus

Stochastic optical solitons of the perturbed nonlinear Schrödinger equation with Kerr law via Ito calculus

Euler-Maruyama Approximation of Stochastic Dependent Poisson-Jump in Black-Scholes Asset Price Model

Abstract In this study, the Gaussian white noise and the differential Poisson of the Stochastic Differential Equation(SDE) with distributed jump are examined. Using Ito integral as a tool, a one step Euler-Maruyama (E-M) method is considered for the approximation of Stochastic Dependent Poisson Analysis (SDPA) in finance. The Deterministic Quadrature Rule (DQR) was used in the establishment of the method for easy examination of the Black-Scholes asset price model for stock investors; MATLAB package was used for simulation of the method. However the Mean Absolute Error (MAE) as well as Strong Order of Convergence (SOC) method was considered to ascertain its usability. The result clearly shows entry points and exit points of stock market. Consequently, the findings of this research is strongly recommended. Keywords: Euler-Maruyama method, Stochastic differential equation, Ito integral, Poisson distributed jump, Random variables, Deterministic model.

On a regime switching illiquid high volatile prediction model for cryptocurrencies

PurposeThe current paper proposes a prediction model for a cryptocurrency that encompasses three properties observed in the markets for cryptocurrencies—namely high volatility, illiquidity, and regime shifts. As far as the authors’ knowledge extends, this paper is the first attempt to introduce a stochastic differential equation (SDE) for pricing cryptocurrencies while explicitly integrating the mentioned three significant stylized facts.Design/methodology/approachCryptocurrencies are increasingly utilized by investors and financial institutions worldwide as an alternative means of exchange. To the authors’ best knowledge, there is no SDE in the literature that can be used for representing and evaluating the data-generating process for the price of a cryptocurrency.FindingsBy using Ito calculus, the authors provide a solution for the suggested SDE along with mathematical proof. Numerical simulations are performed and compared to the real data, which seems to capture the dynamics of the price path of two main cryptocurrencies in the real markets.Originality/valueThe stochastic differential model that is introduced and solved in this article is expected to be useful for the pricing of cryptocurrencies in situations of high volatility combined with structural changes and illiquidity. These attributes are apparent in the real markets for cryptocurrencies; therefore, accounting explicitly for these underlying characteristics is a necessary condition for accurate evaluation of cryptocurrencies.

Quantum mechanics from stochastic processes

We construct an explicit one-to-one correspondence between non-relativistic stochastic processes and solutions of the Schrodinger equation and between relativistic stochastic processes and solutions of the Klein-Gordon equation. The existence of this equivalence suggests that the Lorentzian path integral can be defined as an Ito integral, similar to the definition of the Euclidean path integral in terms of the Wiener integral. Moreover, the result implies a stochastic interpretation of quantum theories.

Stochastic dispersive Schrödinger–Hirota equation having parabolic law nonlinearity with multiplicative white noise via Ito calculus

Purpose:The aim of this study is to introduce the stochastic dispersive Schrödinger–Hirota equation with parabolic law nonlinearity (SHEPL) with multiplicative white noise via Ito calculus and investigate its stochastic optical soliton solutions. For this purpose, we used unified Riccati equation expansion (UREEM) and a sub-version of an auxiliary methods to construct analytical solutions. Methodology:Firstly, we implemented traveling wave transform into SHEPL with multiplicative white noise via Ito calculus and constructed imaginary and real parts of its nonlinear ordinary differential equation (NODE) form. Then, we presented solution algorithms of UREEM and a sub-version of an auxiliary methods and successfully implemented to obtained NODE. We created analytical solutions of SHEPL with using the appropriate solution sets which involve unknown parameters and necessary transformations. Then we presented graphical representations of these solution functions by investigating the noise effect on the soliton structure and presented the gained results. Findings:We proposed the stochastic SHEPL equation for the first time in this article. We obtained stochastic optical soliton solutions, reflected their graphical representations and also observed effect of noise factor on the obtained soliton structure. Originality:The stochastic model of SHEPL has not been studied and the results have not been reported.

Mean-square Approximation of Iterated Ito and Stratonovich Stochastic Integrals: Method of Generalized Multiple Fourier Series. Application to Numerical Integration of Ito Sdes and Semilinear Spdes (third Edition)

This is the third edition of the monograph (first edition 2020, second edition 2021) devoted to the problem of mean-square approximation of iterated Ito and Stratonovich stochastic integrals with respect to components of the multidimensional Wiener process. The mentioned problem is considered in the book as applied to the numerical integration of non-commutative Ito stochastic differential equations and semilinear stochastic partial differential equations with nonlinear non-commutative trace class noise. The book opens up a new direction in researching of iterated stochastic integrals. For the first time we use the generalized multiple Fourier series converging in the sense of norm in Hilbert space for the expansion of iterated Ito stochastic integrals of arbitrary multiplicity k with respect to components of the multidimensional Wiener process (Chapter 1). Sections 1.11-1.13 (Chapter 1) are new and generalize the results of Chapter 1 obtained earlier by the author and are also closely related to the multiple Wiener stochastic integral introduced by Ito in 1951. The convergence with probability 1 as well as the convergence in the sense of n-th (n=2, 3,...) moment for the expansion of iterated Ito stochastic integrals have been proved (Chapter 1). Moreover, the rate of both types of convergence has been established. The main difference between the third and second editions of the book is that the third edition includes original material (Chapter 2, Sections 2.10-2.19) on a new approach to the series expansion of iterated Stratonovich stochastic integrals of arbitrary multiplicity k with respect to components of the multidimensional Wiener process. The above approach allowed us to generalize some of the author's earlier results and also to make significant progress in solving the problem of series expansion of iterated Stratonovich stochastic integrals. In particular, for iterated Stratonovich stochastic integrals of the fifth and sixth multiplicity, series expansions based on multiple Fourier-Legendre series and multiple trigonometric Fourier series are obtained. In addition, expansions of iterated Stratonovich stochastic integrals of multiplicities 2 to 4 were generalized. These results (Chapter 2) adapt the results of Chapter 1 for iterated Stratonovich stochastic integrals. Two theorems on expansion of iterated Stratonovich stochastic integrals of arbitrary multiplicity k based on generalized iterated Fourier series with pointwise convergence are formulated and proved (Chapter 2). The results of Chapters 1 and 2 can be considered from the point of view of the Wong-Zakai approximation for the case of a multidimensional Wiener process and the Wiener process approximation based on its series expansion using Legendre polynomials and trigonometric functions. The integration order replacement technique for iterated Ito stochastic integrals has been introduced (Chapter 3). Exact expressions are obtained for the mean-square approximation error of iterated Ito stochastic integrals of arbitrary multiplicity k (Chapter 1) and iterated Stratonovich stochastic integrals of multiplicities 1 to 4 (Chapter 5). Furthermore, we provided a significant practical material (Chapter 5) devoted to the expansions and mean-square approximations of specific iterated Ito and Stratonovich stochastic integrals of multiplicities 1 to 6 from the unified Taylor-Ito and Taylor-Stratonovich expansions (Chapter 4). These approximations were obtained using Legendre polynomials and trigonometric functions. The methods constructed in the book have been compared with some existing methods (Chapter 6). The results of Chapter 1 were applied (Chapter 7) to the approximation of iterated stochastic integrals with respect to the finite-dimensional approximation of the Q-Wiener process (for integrals of multiplicity k) and with respect to the infinite-dimensional Q-Wiener process (for integrals of multiplicities 1 to 3).

Option Prices Expansions and Applications

Daniel Bloch presents a general pricing approximation technique for European call option prices in a jump-diffusion model with stochastic interest rates. The author considers the dynamics of the logarithm of the forward price, under the forward measure, in the class of multifactor Affine and Quadratic models without jumps, and devise the dynamics of its associated variance swap. Bloch expresses the expected future average volatility as a function of that variance swap, and uses classical Ito's calculus to expand prices around the Black formula. The author then adds jumps to the dynamics of the log-forward price and condition the expectation of the call price with respect to the number of jumps. Applying a change of measure, option prices decompose into a weighted sum of approximated multifactor Affine and Quadratic models. Bloch uses these prices decompositions to define an analytical formula that approximate the implied volatility surface in the class of Affine and Quadratic models with jumps. At last, the author compares a few prices approximations against Monte Carlo simulations for a range of maturities covering one year, and provide accuracy measures. Bloch then uses these prices as benchmark to measure the accuracy of the implied volatility surface expansion. The author obtains very low MAE and RMSE, both on the prices and IVS, demonstrating that the series expansions are very precise for short and long maturities. Applications include: fast prices and Greeks estimation for European options, initial values for numerical computation of IVS, and control variate for complex pricing models in Monte Carlo and Machine Learning.

Expansion of a filtration with a stochastic process: The information drift

When expanding a filtration with a stochastic process it is easily possible for semimartingale no longer to remain semimartingales in the enlarged filtration. Y. Kchia and P. Protter indicated a way to avoid this pitfall in 2015, but they were unable to give the semimartingale decomposition in the enlarged filtration except for special cases. We provide a way to compute such a decomposition, and moreover we provide a sufficient condition for Ito processes to remain Ito processes in the enlarged filtration. This has significance in applications to Mathematical Finance.

Chiral selectivity vs. noise in spontaneous mirror symmetry breaking.

Mirror symmetry breaking bifurcations, that occur in nonlinear chemical systems leading to final chiral states with very large enantiomeric excess, can be exploited as an efficient chiral signal selector for even the smallest chiral polarizations. This effect of the chiral polarization requires the system's capacity for overcoming thermal noise, which is manifested as fluctuating reaction rate constants. Therefore, we investigate the chiral selectivity across a range of tiny parity-violating energy differences (PVED) in the presence of inevitable non-equilibrium temperature fluctuations. We use a stochastic differential equation simulation methodology (Ito process) that serves as a valuable tool in open systems for identifying the thresholds at which the chiral force induces chiral selectivity in the presence of non-equilibrium temperature fluctuations. This approach enables us to include and analyze chiral selectivity in the presence of other types of fluctuations, such as perturbations in the rate of fluid flow into and out of the reactor and in the clamped input concentrations. These concepts may be of practical interest (i.e., spontaneous deracemizations) but are also useful for a better understanding of the general principles governing the emergence of biological homochirality.

Causal functional calculus

We construct a new topology on the space of stopped paths and introduce a calculus for causal functionals on generic domains of this space. We propose a generic approach to pathwise integration without any assumption on the variation index of a path and obtain functional change of variable formulae which extend the results of Föllmer [Séminaire de probabilités 15 (1981), 143–150] and Cont and Fournié [J. Funct. Anal. 259 (2010), no. 4, 1043–1072] to a larger class of functionals, including Föllmer's pathwise integrals. We show that a class of smooth functionals possess a pathwise analogue of the martingale property. For paths that possess finite quadratic variation, our approach extends the Föllmer–Ito calculus and removes previous restriction on the time partition sequence. We introduce a foliation structure on this path space and show that harmonic functionals may be represented as pathwise integrals of closed 1-forms.

Covariant nonequilibrium thermodynamics from Ito-Langevin dynamics

Using the recently developed covariant Ito-Langevin dynamics, we develop a nonequilibrium thermodynamic theory for small systems coupled to multiplicative noises. The theory is based on Ito calculus, and is fully covariant under time-independent nonlinear transformation of variables. Assuming instantaneous detailed balance, we derive expressions for various thermodynamic functions, including work, heat, entropy production, and free energy, both at ensemble level and at trajectory level, and prove the second law of thermodynamics for arbitrary nonequilibrium processes. We relate time-reversal asymmetry of path probability to entropy production, and derive its consequences such as fluctuation theorem and nonequilibrium work relation. For Langevin systems with additive noises, our theory is equivalent to the common theories of stochastic energetics and stochastic thermodynamics. Using concrete examples, we demonstrate that whenever kinetic coefficients or metric tensor depend on system variables, the common theories of stochastic thermodynamics and stochastic energetics should be replaced by our theory.

Dynamic Equilibrium Strategy of Power Battery Closed-Loop Supply Chain Based on Stochastic Differential Game

The industrialization of new energy vehicles has accelerated, and the recycling industry driven by a large number of retired power batteries has exploded rapidly. How to control and deal with decommissioned batteries and balance economic and environmental benefits has become an urgent problem to be solved. Considering the influence of uncertain interference factors, the dynamic equilibrium strategy of power battery closed-loop supply chain members composed of leading manufacturer, recycler, and cascade utilization enterprise was studied. (1) This paper describes the stochastic evolution process of cascade utilization effort level by using Ito process and constructs a stochastic differential game model of closed-loop supply chain dynamic system combined with cost-sharing coordination mechanism. (2) Based on the stochastic differential game theory, the optimal profit function of the participants is given, and the dynamic equilibrium strategy of each participant is obtained. (3) In order to grasp the statistical characteristics of cascade utilization effort level, the random evolution characteristics of cascade utilization effort level are revealed. (4) Combined with numerical example, the influence of relevant parameters on the dynamic system of power battery closed-loop supply chain is analyzed. The results found that with the increase of the proportion of manufacturer sharing the innovation cost of cascade utilization enterprise, the cascade utilization effort level will improve, the profits of cascade utilization enterprise and recycler will increase significantly, the profits of manufacturer will decrease slightly, and the overall profits of the supply chain will increase. The cost-sharing coordination mechanism is conducive to ensuring the steady development of the industry and effectively improving the utilization efficiency of industrial resources.

On the uniqueness of higher order Gubinelli derivatives and an analogue of the Doob – Meyer theorem for rough paths of the arbitrary positive Holder index

In this paper, we investigate the features of higher order Gubinelli derivatives of controlled rough paths having an arbitrary positive Holder index. There is used a notion of the (α, β)-rough map on the basis of which the sufficient conditions are given for the higher order Gubinelli derivatives uniqueness. Using the theorem on the uniqueness of higher order Gubinelli derivatives an analogue of the Doob – Meyer theorem for rough paths with an arbitrary positive Holder index is proved. In the final section of the paper, we prove that the law of the local iterated logarithm for fractional Brownian motion allows using all the main results of this paper for integration over the multidimensional fractional Brownian motions of the arbitrary Hurst index. The examples demonstrating the connection between the rough path integrals and the Ito and Stratonovich integrals are represented.

Closed-form pricing formulas for variance swaps in the Heston model with stochastic long-run mean of variance

The Heston model is a popular stochastic volatility model in mathematical finance and it has been extended or modified in several ways by researchers to overcome the shortcomings of the model in the context of pricing derivatives. However, the extended models usually do not lead to a closed-form formula for the derivative prices. This paper is focused on a stochastic extension of the constant long-run mean of variance in the Heston model for the pricing of variance swaps. The extension is given by a positive function perturbed by an amplitude-modulated Brownian motion or Ito integral. We obtain two closed-form formulas for the fair strike prices of a variance swap under two corresponding underlying models. The formulas are explicitly given by elementary functions without any integral terms involved. Further, the two models show better performance than the Heston model when the market implied volatility has a concave-down pattern as shown in an unstable market circumstance caused by the COVID-19 pandemic.

Diffusivity interfaces in lattice Monte Carlo simulations: Modeling inhomogeneous delivery and release systems.

Lattice Monte Carlo (LMC) simulations are widely used to investigate diffusion-controlled problems such as drug-release systems. The presence of an inhomogeneous diffusivity environment raises subtle questions about the interpretation of stochastic dynamics in the overdamped limit, an issue sometimes referred to as the "Ito-Stratonovich-isothermal dilemma." We propose a LMC formalism that includes the different stochastic interpretations in order to model the diffusion of particles in a space-dependent diffusivity landscape. Using as an example a simple inhomogeneous one-dimensional system with a diffusivity interface and different boundary conditions, we demonstrate that we can properly reproduce the steady state and dynamic properties of these systems and that these properties do depend on the choice of calculus. In particular, we argue that the version of the LMC algorithm that uses Ito calculus, which is commonly used to model drug delivery systems, should be replaced by the isothermal version for most applications. Our LMC methodology provides an efficient alternative to Langevin simulations for a wide class of space-dependent diffusion problems.