LSM Algorithm for Pricing American Option Under Heston–Hull–White’s Stochastic Volatility Model

In this paper, we present American option pricing under Heston–Hull–White’s stochastic volatility and stochastic interest rate model. To do this, we first discretize the stochastic processes with Euler discretization scheme. Then, we price American option by using least-squares Monte Carlo algorithm. We also compare the numerical results of our model with the Heston-CIR model. Finally, numerical results show the efficiency of the proposed algorithm for pricing American option under the Heston–Hull–White model.     

A hybrid predictive controller with an adaptive fast model†

A hybrid predictive controller is proposed for control of second-order, or higher-order systems which can be approximated to second order. The proposed controller uses hybrid computation and is suitable for on-line computer controlled problems. A fast time second-order adaptive model of the system is used in which the gain and one of the equivalent time constants of the system is calculated instantaneously. As an example the proposed controller is used for control of the output voltage of a dc. generating system under load variations. Responses are compared when the controller uses a system model with fixed parameters and when the model is made adaptive.     

A Durbin–Watson serial correlation test for ARX processes via excited adaptive tracking

We propose a new statistical test for the residual autocorrelation in ARX adaptive tracking. The introduction of a persistent excitation in the adaptive tracking control allows us to build a bilateral statistical test based on the well-known Durbin–Watson statistic. We establish the almost sure convergence and the asymptotic normality for the Durbin–Watson statistic leading to a powerful serial correlation test. Numerical experiments illustrate the good performances of our statistical test procedure.     

Psychophysiological assessment of an adaptive asynchronous human–machine system with the dual-task method

Human error is one of the leading causes of industrial accidents at modern manufacturing production sites. An adaptive asynchronous human–machine system (Async-HMS) was previously proposed as a new mechanism for preventing the reduction of cognitive level by controlling the actual operation period of machines that lead to monotonous behavior. The objective of this paper is to assess Async-HMS from the aspects of person’s current capacity for performing processing resources. A dual-task method consisting of a synchronization task with three periodic fluctuations and four levels of difficulties of mental arithmetic tasks was conducted with Async-HMS on a PC. The three periodic fluctuations are constant periodic fluctuation (Cnst), perceptible periodic fluctuation (Supraliminal), and imperceptible periodic fluctuation (Subliminal). The task performance and the psychophysiological state were assessed through the dual-task method. In an ANOVA analysis, the time shared fraction, which is an index of processing resources devoted to an arithmetic task, had no significant effect on the synchronization task. The mean blood pressure in Subliminal had a significant increase compared with that in Cnst and Supraliminal. These results indicate that subliminal fluctuation evokes sympathetic hyperactivity without affecting processing resources. Implementation of subliminal operation period into industrial machines performing periodic behavior might inhibit monotony and, therefore, prevent human error.     

Synchronization of delayed reaction–diffusion neural networks via an adaptive learning control approach

An adaptive learning control strategy is utilized to investigate the synchronization problem for delayed reaction–diffusion neural networks (RDNNs) with unknown time-varying coupling strengths. A novel adaptive synchronization approach is proposed, which is consisted of differential–difference type updating law and feedback control law. By constructing a Lyapunov–Krasovskii-like composite energy functional (CEF), based on the LaSalle invariant principle of functional differential equations, a sufficient condition for the adaptive synchronization of such a system is obtained. Finally, a numerical example is given to show the effectiveness of the proposed synchronization method.     

The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

In this study, an adaptive fuzzy time series model for forecasting Taiwan’s tourism demand is proposed to further enhance the predicted accuracy. We first transfer fuzzy time series data to the fuzzy logic group, assign weights to each period, and then use the proposed adaptive fuzzy time series model for forecasting in which an enrollment forecasting values is applied to obtain the smallest forecasting error. Finally, an illustrated example for forecasting Taiwan’s tourism demand is used to verify the effectiveness of proposed model and confirmed the potential benefits of the proposed approach with a very small forecasting error MAPE and RMSE.     

Optimal control of viscous Burgers equation via an adaptive nonmonotone Barzilai–Borwein gradient method

An adaptive nonmonotone spectral gradient method for the solution of distributed optimal control problem (OCP) for the viscous Burgers equation is presented in a black-box framework. Regarding the implicit function theorem, the OCP is transformed into an unconstrained nonlinear optimization problem (UNOP). For solving UNOP, an adaptive nonmonotone Barzilai–Borwein gradient method is proposed in which to make a globalization strategy, first an adaptive nonmonotone strategy which properly controls the degree of nonmonotonicity is presented and then is incorporated into an inexact line search approach to construct a more relaxed line search procedure. Also an adjoint technique is used to effectively evaluate the gradient. The low memory requirement and the guaranteed convergence property make the proposed method quite useful for large-scale OCPs. The efficiency of the presented method is supported by numerical experiments.     

Control of an ideal activated sludge process in wastewater treatment via an ODE–PDE model

The activated sludge process (ASP), found in most wastewater treatment plants, consists basically of a biological reactor followed by a sedimentation tank, which has one inlet and two outlets. The purpose of the ASP is to reduce organic material and dissolved nutrients (substrate) in the incoming wastewater by means of activated sludge (microorganisms). The major part of the discharged flow through the bottom outlet of the sedimentation tank is recirculated to the reactor, so that the biomass is reused. Only two material components are considered; the soluble substrate and the particulate sludge. The biological reactions are modelled by two nonlinear ordinary differential equations and the continuous sedimentation process by two hyperbolic partial differential equations (PDEs), which have coefficients that are discontinuous functions in space due to the inlet and outlets. In contrast to previously published modelling-control aspects of the ASP, the theory for such PDEs is utilized. It is proved that the most desired steady-state solutions can be parameterized by a natural control variable; the ratio of the recirculating volumetric flow to the input flow. This knowledge is a key ingredient in a two-variable regulator, with which the effluent dissolved nutrients concentration and the concentration profile in the sedimentation tank are controlled. Theoretical results are supported by simulations.     

Pricing weather derivatives with partial differential equations of the Ornstein–Uhlenbeck process

This paper means to price weather derivatives through solving the Partial Differential Equation (PDE) of the Ornstein–Uhlenbeck process. Since the PDE is convection dominated, a finite difference scheme with adaptively adjusted one-sided difference is proposed to discretize the PDE without causing spurious oscillations. We compare the finite difference scheme with both the Monte Carlo simulations and Alaton’s approximate formulas. It is shown by extensive numerical experiments that the PDE based approach is accurate, efficient and practical for weather derivative pricing.In addition, we point out that the PDE approach developed for discretely sampled temperature is essentially equivalent to the Semi-Lagrangian time stepping based method. A corresponding Semi-Lagrangian method is also proposed to price weather derivatives of continuously sampled temperature.     

Fixed-time synchronization in multilayer networks with delay Cohen–Grossberg neural subnets via adaptive quantitative control

In this paper, fixed-time synchronization of nonlinear stochastic coupling multilayer neural networks is studied. The neural subnets in the multilayer networks are delay Cohen–Grossberg neural networks (DCGNNs). To overcome uncertain factors, we designed an adaptive delay-dependent controller in synchronization. To describe constraints of communication and other related problems in networks, which are due to limitations for bit rates and bandwidths in communication channels, an adaptive fixed-time control strategy is purposed by introducing quantization signal input. A theoretical framework about fixed-time synchronization in multilayer delay Cohen–Grossberg neural networks (MDCGNNs) is established. We find that fixed settling time is related to the scale of MDCGNNs, characteristics of the designed controller parameters, and level of quantization. Finally, the effective of the theoretical framework is validated in an example.     

Pricing and balancing of the sea–cargo service chain with empty equipment repositioning

To sustain a business, firms have to reposition empty containers from a surplus port to a port with a shortage and incur repositioning costs if the realized demands are unbalanced in the sea–cargo service chain. In this paper, we study a sea cargo service chain with one carrier and two forwarders providing transportation service between two ports, and there are potential demands for transportation services in both directions. We built a mathematical model to study how the carrier and forwarders determine pricing and empty equipment repositioning (hereafter EER) decisions. We find that whether or not the carrier and forwarders use pricing policies to balance the cargo demands depends on the potential demand imbalance volume between two ports. We also investigate the EER sharing strategy on whether to share the EER cost or undertake it solely. It is found that there exists a threshold, and when the EER cost is beyond the threshold value, the carrier assumes all of the repositioning costs; otherwise, the forwarder assumes all of the repositioning costs. Lastly, we study a subsidy contract between both forwarders and expand to study an EER sharing mode between the carrier and both forwarders.     

Play segmentation for the play–break based sports video using a local adaptive model

This paper proposes a new play segmentation algorithm using a local adaptive model for each sports game, in which the play start shots (PSS) that represent the start of each play segment are detected by comparing all of keyframes with the PSS model. The PSS model is calculated on the fly using generic clustering algorithm and a repetitive characteristic of the PSS. The end of each play segment (the play end shot (PES)) is determined by detecting close up shots using the field color extracted from the play start shots since the camera will focus on the players or the audience with close up view. Experimental results with 28 baseball videos show that good performance can be obtained with the proposed algorithm compared to other algorithms.

Option pricing formulas for uncertain financial market based on the exponential Ornstein–Uhlenbeck model

Uncertain finance is an application of uncertainty theory in the field of finance. This paper investigates the uncertain financial market based on the exponential Ornstein–Uhlenbeck model. European option pricing formulas and American option pricing formulas are derived via the \(\alpha \)-path method. Finally, some mathematical properties of the uncertain option pricing formulas are discussed.     

A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things

The Journal of Supercomputing - Accurate cellular traffic prediction becomes more and more critical for efficient network resource management in the Internet of Things (IoT). However, high-accuracy...     

Extending the technology acceptance model with task–technology fit constructs

During the past decade, two significant models of information technology (IT) utilization behavior have emerged in the MIS literature. These two models, the technology acceptance model (TAM) and the task–technology fit model (TTF), provide a much needed theoretical basis for exploring the factors that explain software utilization and its link with user performance. These models offer different, though overlapping perspectives on utilization behavior. TAM focuses on attitudes toward using a particular IT which users develop based on perceived usefulness and ease of use of the IT. TTF focuses on the match between user task needs and the available functionality of the IT. While each of these models offers significant explanatory power, a model that integrates constructs from both may offer a significant improvement over either model alone. We discuss the theoretical foundation of both these models and present a theoretical rationale for an integrated model. The result is an extension of TAM to include TTF constructs. We test our integrated IT utilization model using path analysis. Our integrated model provides more explanatory power than either model alone. Research using the integrated model should lead to a better understanding of choices about using IT.     

An hp finite element adaptive scheme to solve the Laplace model for fluid–solid vibrations

In this paper we introduce an hp finite element method to solve a two-dimensional fluid–structure spectral problem. This problem arises from the computation of the vibration modes of a bundle of parallel tubes immersed in an incompressible fluid. We prove the convergence of the method and a priori error estimates for the eigenfunctions and the eigenvalues. We define an a posteriori error estimator of the residual type which can be computed locally from the approximate eigenpair. We show its reliability and efficiency by proving that the estimator is equivalent to the energy norm of the error up to higher order terms, the equivalence constant of the efficiency estimate being suboptimal in that it depends on the polynomial degree. We present an hp adaptive algorithm and several numerical tests which show the performance of the scheme, including some numerical evidence of exponential convergence.     

The active learning hypothesis of the job–demand–control model: an experimental examination

The active learning hypothesis of the job–demand–control model [Karasek, R. A. 1979. “Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign.” Administration Science Quarterly 24: 285–307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed–accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.     

Stabilization of the Euler–Bernoulli equation via boundary connection with heat equation

In this paper, we are concerned with the stabilization of a coupled system of Euler–Bernoulli beam or plate with heat equation, where the heat equation (or vice versa the beam equation) is considered as the controller of the whole system. The dissipative damping is produced in the heat equation via the boundary connections only. The one-dimensional problem is thoroughly studied by Riesz basis approach: The closed-loop system is showed to be a Riesz spectral system and the spectrum-determined growth condition holds. As the consequences, the boundary connections with dissipation only in heat equation can stabilize exponentially the whole system, and the solution of the system has the Gevrey regularity. The exponential stability is proved for a two dimensional system with additional dissipation in the boundary of the plate part. The study gives rise to a different design in control of distributed parameter systems through weak connections with subsystems where the controls are imposed.     

A closed-form approximation for the fractional Black–Scholes model with transaction costs

In this paper, we investigate option valuation problems under the fractional Black–Scholes model. The aim is to propose a pricing formula for the European option with transaction costs, where the costs structure contains fixed costs, a cost propositional to the volume traded, and a cost proportional to the value traded. Precisely, we provide an approximate solution of the nonlinear Hoggard–Whalley–Wilmott equation. The comparison results reveal that our approximate solutions are close to the numerical computations. Moreover, the comparison results demonstrate that the price of the European option decreases as the Hurst exponent increases.     

Global sensitivity analysis of an energy–economy model of the residential building sector

In this paper, we discuss the results of a sensitivity analysis of Res-IRF, an energy–economy model of the demand for space heating in French dwellings. Res-IRF has been developed for the purpose of increasing behavioral detail in the modeling of energy demand. The different drivers of energy demand, namely the extensive margin of energy efficiency investment, the intensive one and building occupants, behavior are disaggregated and determined endogenously. The model also represents the established barriers to the diffusion of energy efficiency: heterogeneity of consumer preferences, landlord–tenant split incentives and slow diffusion of information. The relevance of these modeling assumptions is assessed through the Morris method of sensitivity analysis, which allows for the exploration of uncertainty over the whole input space. We find that the Res-IRF model is most sensitive to energy prices. It is also found to be quite sensitive to the factors parameterizing the different drivers of energy demand. In contrast, inputs mimicking barriers to energy efficiency have been found to have little influence. These conclusions build confidence in the accuracy of the model and highlight occupants' behavior as a priority area for future empirical research.