An application of the one-factor HullWhite model in an IoT financial scenario

Cuomo, Salvatore; Somma, Vittorio Di; Sica, Federica

doi:10.1016/j.scs.2017.12.005

Cited by 15 publications

(6 citation statements)

References 8 publications

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“…Therefore, it might improve the efficiency and scope of financial services through the application of technology in the banking domain. Along with the sustainable development of Fintech, there are many technological breakthroughs in this field, such as big data [11], cloud computing [12], Internet of Things [13], and data analytic techniques [14]. Du et al divided the main issues of security and privacy of Fintech into four dimensions-data-oriented, facility and equipment, applications, and service models [15].…”

Section: Fintechmentioning

confidence: 99%

Adoption Intention of Fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model

et al. 2019

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Along with the development of Fintech, many scholars have studied how information technology is applied to financial services with a focus on extended methods for application. Few scholars have studied the influence mechanism behind the adoption of Fintech services. This paper proposes an improved technology acceptance model (TAM) that incorporates user innovativeness, government support, brand image, and perceived risk as determinants of trust to investigate how users adopt Fintech services. We designed a questionnaire, sent it to active customers of the Hefei Science and Technology Rural Commercial Bank, and obtained 387 eligible responses. We analyzed the data with a structural equation model (SEM) to test the hypotheses, including the relationships of all latent variables. The results reveal that users’ trust in Fintech services has a very significant influence on users’ attitudes for adoption. In addition, perceived ease of use and perceived risk does not affect users’ attitudes toward the adoption regarding Fintech services. This study contributes to the literature of the adoption of Fintech services by providing a more comprehensive view of the determinants of users’ attitudes by combining trust of Fintech services with TAM.

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Section: Fintechmentioning

confidence: 99%

Adoption Intention of Fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model

et al. 2019

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“…Using a Suppes-Bayes causal network, [20] carry out a series of simulations of financial stress test scenarios with the aim at discovering complex relations among the financial factors that affect the value of a portfolio. Within this research trend, [21] investigate the use of internal and external data obtained from stock markets to create financial scenarios to offer trading opportunities to investors. Financial scenarios that use a joint distribution of losses for assessing the stability of the banking sector in the EU, based on the Basel II capital rules and micro-simulations of portfolio structures, has been researched in [22].…”

Section: Previous Workmentioning

confidence: 99%

“…These variables are defined by the EIRR. According to Definition 3, the input variables can be expressed as partial linguistic rates in accordance with the logistic function using the independent effects expressed in (21). According to the normalization of the input variables in the interval [−1, 1], the independent effects takes a form based on (6).…”

Section: Neural Stochastic Model With Partial Activation Logistic Funmentioning

confidence: 99%

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

González-Ruiz

Peña

Grisales

et al. 2019

Applied Soft Computing

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In general, the development of economic infrastructure systems requires a behavioural comprehensive analysis of different financial variables or rates to establish its long-term success with regards to the Equity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizations have developed economic scenarios supported by computational techniques and models to identify the evolution of these financial rates. However, these models and techniques have shown a series of limitations with regard to the financial management process and its impact on EIRR over time. To address these limitations in an inclusive way, researchers have developed different approaches and methodologies focused on the development of financial models using stochastic simulation methods and computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where the input consists in financial rates that are characterized as linguistic rates through a series of adaptive logistic functions. The stochastic process that explains the behaviour of the financial rates over time and their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financing scenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it was possible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM was validated using three different models: (1) a financial management tool; (2) a general FCM without pre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where the causalities between the concepts or rates were obtained as a result of an independent effects analysis applying a cross modelling between variables and by using a statistical multi-linear model (statistical significance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporation of an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. This allowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financing period. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in different knowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by the traditional computational models used to design these financial scenarios.

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“…Things become the leading producer and receiver of data since the number of things, which are connected together via IoT, are much larger than the number of humans . IoT framework provides multiple benefits for us by gathering data . The gathered data can be processed by mathematical models to provide useful information for businesses, organizations, and enterprises .…”

Section: Introductionmentioning

confidence: 99%

“…4 IoT framework provides multiple benefits for us by gathering data. 5 The gathered data can be processed by mathematical models to provide useful information for businesses, organizations, and enterprises. 6 For example, an IoT framework supporting financial services by designing an algorithm for the estimation of the volatility was discussed in the work of Cuomo et al 7 However, security outcome is one of the most complex and important issues that arise within the IoT infrastructure technologies, which should be taken into a comprehensive consideration.With the emergence of new wireless communication systems and increasing number of users, there is a corresponding increase in the demand for spectrum use.…”

mentioning

confidence: 99%

A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

Emami

Zarrabi

Jabbarpour

et al. 2018

Concurrency and Computation

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Summary Recently, the growth of Internet of Things (IoT) and its remarkable impacts on human well‐being life style are deniable. On the connectivity side, IoT is highly related to Wireless Sensor Network (WSN) concept. The key elements include the data, which is machine‐produced, specifically by sensors, and the data communication through connectivity technologies. On the security side, primary user emulation attack (PUE) is one of the well‐defined attacks in cognitive radio (CR)–based WSN. Here, we investigate a smart primary user emulation attacker that has the most destructive effect on the spectrum sensing unit of cognitive radio users. To deal with this attack, a soft cooperative spectrum sensing using an energy detector is proposed. In the proposed method, the values of sensing information of each secondary user are sent to a fusion center. Once the values are received, they will be combined with some appropriate coefficients in order to minimize spectrum sensing probability of error for a given probability of false alarm. The coefficients are the variables of a constrained optimization problem. Based on simulation results, our method has a lower error probability in spectrum sensing in comparison to hard combination schemes (eg, OR rule) and soft combination schemes (eg, CSINR method).

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An application of the one-factor HullWhite model in an IoT financial scenario

Cited by 15 publications

References 8 publications

Adoption Intention of Fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model

Adoption Intention of Fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

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