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Modeling and Innovative Research Frameworks

Abstract

Recent empirical studies show that an underlying asset price process may have the property of long memory. In this paper, it is introduced the bifractional Brownian motion to capture the underlying asset of European options. Moreover, a bifractional Black-Scholes partial differential equation formulation for valuing European options based on Delta hedging strategy is proposed. Using the final condition and the method of variable substitution, the pricing formulas for the European options are derived. Furthermore, applying to risk-neutral principle, we obtain the pricing formulas for the compound options. Finally, the numerical experiments show that the parameter HK has a significant impact on the option value.

Abstract

Assessment of brand competitiveness which influences consumer trends and company’s sales performance contributes to business development. This paper elaborates the theories and the main investigations of brand competitiveness, and formulates a comprehensive hierarchical structure integrating multi-attribute indexes, i.e., social, technical, managerial, environmental and cultural criteria. An integrated multi-criterion decision-making (MCDM) model combining with analytic hierarchy process (AHP), grey relational analysis (GRA) and VIKOR is presented to determine the weights of influence criteria and to evaluate brand competitiveness. The utilized integrated methodology has been proved to be valid and practical by the empirical application on three enterprises. The results provide an accurate and effective tool for MCDM problem and also a new guideline for the enterprise development.

Abstract

Online media have brought tremendous changes to civic life, public opinions, and government administration. Compared with traditional media, online media not only allow individuals to browse news and express their views more freely, but also accelerate the transmission of opinions and expand influence. As public opinions may arouse societal unrest, it is worth detecting the primary topics and uncovering the evolution trends of public opinions for societal administration. Various algorithms are developed to deal with the huge volume of unstructured online media data. In this study, dynamic topic model is employed to explore topic content evolution and prevalence evolution using the original posts published from 2013 to 2017 on the Tianya Zatan Board of Tianya Club, which is one of the most popular BBS in China. Based on semantic similarities, topics are grouped into three themes: Family life, societal affairs, and government administration. The evolution of topic prevalence and content are affected by emergent incidents. Topics on family life become popular, while themes “societal affairs” and “government administration” with bigger standard deviations are more likely to be influenced by emergent hot events. Content evolution represented by monthly pairwise distance matrix is very easy to find change points of topic content.

Abstract

Spatio-temporal model and event analysis were integrated in this paper, with 156 prefecture level cities’ housing transaction data and 167 items policies proposed by 10 central cities between January, 2010 and December, 2018 as samples. This paper studied the regional and cross-regional spillover effects of central cities’ urban housing regulation policies to the peripheral cities in the scope of urban agglomerations, as well as the policy-driven interactions of different regional real estate markets. The results indicated that: China’s regional housing market has obvious characteristics of policy orientation, of which the regulation measures on some central cities can affect the residential market and produce certain spillover interference on the market fluctuations of peripheral cities in time and space dimension. When geographical factor was considered, the 10 central cities had different degree of policy spillover effects caused by distinct policy types in their respective urban agglomerations. When ignoring spatial factors, restrictive policies in Beijing, Shanghai, Zhengzhou, Xi’an, Wuhan and Shenzhen had significant cross-regional spillover effects and drove the surrounding housing markets to have geared interactions, which to a certain extent revealed the flowing way of population and wealth in China’s regional economy during the past dozen years.

Abstract

In this paper we consider a discrete-time Geo/G/1 queue with delayed Min(N, D)-policy. Using renewal process theory, total probability decomposition technique and z-transform, we study the transient and equilibrium properties of the queue length from an arbitrary initial state, and obtain both the recursive expressions of the transient state queue length distribution and the steady state queue length distribution at arbitrary time epoch n +. Furthermore, we derive the important relations between equilibrium queue length distributions at different time epochs n , n and n +. Finally, we give some numerical examples about capacity decision in queueing systems to demonstrate the application of the analytical results reported in this paper.

Abstract

Carpooling as a transportation demand management (TDM) tool is currently being prevalent in major Chinese cities and producing much diminishment in the frequency of solo-driving trips. Meanwhile, much disputes relating to carpooling is arising. To better understand the acceptance and the influence factors of carpooling, this paper investigates travelers’ willingness to provide and accept carpooling services. Firstly, a questionnaire survey was conducted. Secondly, we proposed a theoretical model, both car-owners and non-car-owners were sampled as respondents, and a multi-variable regression method was employed to analyze the survey data. Finally, we found that the higher acceptance, the more positive reactions to carpooling. The results indicate that it is necessary to improve the public’s acceptance of carpooling, because lower acceptance will lead to more negative reactions towards the carpooling, which may weaken its effectiveness.

Abstract

Freezing of gait (FOG) consistently reoccurs in the later phases of a patient suffering from Parkinson’s disease (PD). Although it is treated with pharmacological treatment, the impact of the medication fades with increasing duration of the disease and thus diminishing the mobility of a patient. This chapter aims at developing a neural network-based classification model that helps to detect FOG episodes in a patient at early stages so that lethal mishaps can be avoided. In this application example, we build user-independent FOG recognition system that would work along in conjunction with nonpharmacological medications. The structured system of developing a neural network-based classification model can be organized into three different stages. The process starts with extraction of suitable features from the dataset. In the subsequent stage, patients are additionally grouped into two clusters depending on the FOG episodes. In the final stage, two neural network models are developed using feedforward network on the two clusters that were formed. The accuracy of the model is computed using sensitivity and specificity.