Stelios Bekiros - Academia.edu (original) (raw)
Papers by Stelios Bekiros
Robust adaptive control of fractional-order memristive neural networks
Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
Mathematics
Due to the vital role of financial systems in today’s sophisticated world, applying intelligent c... more Due to the vital role of financial systems in today’s sophisticated world, applying intelligent controllers through management strategies is of crucial importance. We propose to formulate the control problem of the macroeconomic system as an optimization problem and find optimal actions using a reinforcement learning algorithm. Using the Q-learning algorithm, the best optimal action for the system is obtained, and the behavior of the system is controlled. We illustrate that it is possible to control the nonlinear dynamics of the macroeconomic systems using restricted actuation. The highly effective performance of the proposed controller for uncertain systems is demonstrated. The simulation results evidently confirm that the proposed controller satisfies the expected performance. In addition, the numerical simulations clearly confirm that even when we confined the control actions, the proposed controller effectively finds optimal actions for the nonlinear macroeconomic system.
Fractals
Mathematical modeling can be utilized to find out how the coronavirus spreads within a population... more Mathematical modeling can be utilized to find out how the coronavirus spreads within a population. Hence, considering models that can precisely describe natural phenomena is of crucial necessity. Besides, although one of the most significant benefits of mathematical modeling is designing optimal policies for battling the disease, there are a few studies that employ this beneficial aspect. To this end, this study aims to design optimal management policies for the novel coronavirus disease 2019 (COVID-19). This is pioneering research that designs optimal policies based on multi-objective evolutionary algorithms for control of the fractional-order model of the COVID-19 outbreak. Firstly, a fractional-order model of the disease dynamic is presented. The impacts of the fractional derivative's value on the modeling and forecasting of the disease spread are considered. After that, a multi-objective optimization problem is proposed by considering the rate of communication, the transition
Cognitive Computation
Background Like common stocks, Bitcoin price fluctuations are non-stationary and highly noisy. Du... more Background Like common stocks, Bitcoin price fluctuations are non-stationary and highly noisy. Due to attractiveness of Bitcoin in terms of returns and risk, Bitcoin price prediction is attracting a growing attention from both investors and researchers. Indeed, with the development of machine learning and especially deep learning, forecasting Bitcoin is receiving a particular interest. Methods We implement and apply deep forward neural network (DFFNN) for the analysis and forecasting Bitcoin highfrequency price data. Importantly, we seek to investigate the effect of standard numerical training algorithms on the accuracy obtained by DFFNN; namely, the conjugate gradient with Powell-Beale restarts, the resilient algorithm, and Levenberg-Marquardt algorithm. The DFFNN was applied to a big dataset composed of 65,535 samples. Results In terms of root mean of squared errors (RMSEs), the simulation results show that the DFFNN trained with the Levenberg-Marquardt algorithm outperforms DFFNN trained with Powell-Beale restarts algorithm and DFFNN trained with resilient algorithm. In addition, the resilient algorithm is fast which suggests that it could be promising in online training and trading. Conclusions The DFFNN trained with Levenberg-Marquardt algorithm is effective and easy to implement for Bitcoin highfrequency price data forecasting.
Deep learning systems for automatic diagnosis of infant cry signals
Chaos, Solitons & Fractals
This paper incorporates anticipated and unexpected shocks to bank capital into a DSGE model with ... more This paper incorporates anticipated and unexpected shocks to bank capital into a DSGE model with a banking sector. We apply this model to study Basel III countercyclical capital requirements and their implications for banking stability and household welfare. We introduce three different countercyclical capital rules. The first countercyclical capital rule responds to credit to output ratio. The second countercyclical rule reacts to deviations of credit to its steady state, and the third rule reacts to credit growth. The second rule proves to be the most effective tool in dampening credit supply, housing demand, household debt and output fluctuations as well as in enhancing the banking stability by ensuring that banks have higher bank capital and capital to asset ratio. After conducting a welfare analysis we find that the second rule outranks the other ones followed by the first rule, the baseline and the third rule respectively in terms of welfare accumulation.
This paper explores a real-world fundamental theme under a data science perspective. It specifica... more This paper explores a real-world fundamental theme under a data science perspective. It specifically discusses whether fraud or manipulation can be observed in and from municipality income tax size distributions, through their aggregation from citizen fiscal reports. The study case pertains to official data obtained from the Italian Ministry of Economics and Finance over the period 2007–2011. All Italian (20) regions are considered. The considered data science approach concretizes in the adoption of the Benford first digit law as quantitative tool. Marked disparities are found, for several regions, leading to unexpected “conclusions”. The most eye browsing regions are not the expected ones according to classical imagination about Italy financial shadow matters. © 2017 Elsevier Ltd. All rights reserved.
We assess the effectiveness of the forward guidance undertaken by European Central Bank using a s... more We assess the effectiveness of the forward guidance undertaken by European Central Bank using a standard medium-scale DSGE model a la Smets and Wouters (2007). Exploiting data on expectations from surveys, we show that incorporating expectations should be crucial in performance evaluation of models for the forward guidance. We conduct an exhaustive empirical exercise to compare the pseudo out-of-sample predictive performance of the estimated DSGE model with a Bayesian VAR and a DSGE-VAR models. DSGE model with expectations outperforms others for inflation; while for output and short term-interest rate the DSGE-VAR with expectations reports the best prediction.
Although policymakers and practitioners are particularly interested in DSGE models, these are typ... more Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be applied directly to the data and often yield weak prediction re- sults. Very recently, hybrid DSGE model
Annals of Operations Research
It is well documented that the biopharmaceutical sector has exhibited weak financial returns, con... more It is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in the biotech sector and ten asset classes using daily cross-country data from 1995 to 2019. We capture investors' heterogeneous investment horizons by decomposing time series according to frequencies. Using a maximal overlap discrete wavelet transform (MODWT) and a dynamic conditional correlation (DCC)-Student-t copula, diversification potentials are revealed, helping investors to reap the benefits of investing in biotech. Our findings indicate that the underlying assets exhibit nonlinear asymmetric behavior that strengthens during periods of turmoil.
Entropy
The Selkov system, which is typically employed to model glycolysis phenomena, unveils some rich d... more The Selkov system, which is typically employed to model glycolysis phenomena, unveils some rich dynamics and some other complex formations in biochemical reactions. In the present work, the synchronization problem of the glycolysis reaction-diffusion model is handled and examined. In addition, a novel convenient control law is designed in a linear form and, on the other hand, the stability of the associated error system is demonstrated through utilizing a suitable Lyapunov function. To illustrate the applicability of the proposed schemes, several numerical simulations are performed in one- and two-spatial dimensions.
Entropy
Although most of the early research studies on fractional-order systems were based on the Caputo ... more Although most of the early research studies on fractional-order systems were based on the Caputo or Riemann–Liouville fractional-order derivatives, it has recently been proven that these methods have some drawbacks. For instance, kernels of these methods have a singularity that occurs at the endpoint of an interval of definition. Thus, to overcome this issue, several new definitions of fractional derivatives have been introduced. The Caputo–Fabrizio fractional order is one of these nonsingular definitions. This paper is concerned with the analyses and design of an optimal control strategy for a Caputo–Fabrizio fractional-order model of the HIV/AIDS epidemic. The Caputo–Fabrizio fractional-order model of HIV/AIDS is considered to prevent the singularity problem, which is a real concern in the modeling of real-world systems and phenomena. Firstly, in order to find out how the population of each compartment can be controlled, sensitivity analyses were conducted. Based on the sensitivit...
The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets
Chaos, Solitons & Fractals
Abstract We examine long memory (self-similarity) in digital currencies and international stock e... more Abstract We examine long memory (self-similarity) in digital currencies and international stock exchanges prior and during COVID-19 pandemic. Specifically, ARFIMA and FIGARCH models are respectively employed to evaluate long memory parameter in returns and volatility. The dataset contains 45 cryptocurrency markets and 16 international equity markets. The t-test and F-test are performed to estimated long memory parameters. The empirical findings follow. First, the level of persistence in return series of both markets has increased during the COVID-19 pandemic. Second, during COVID-19 pandemic, variability level in persistence in return series has increased in both digital currencies and stock markets. Third, return series in both markets exhibited comparable level of persistence prior and during the COVID-19 pandemic. Fourth, return series in volatility series of cryptocurrency exhibited high degree of persistence compared to international stock markets during the COVID-19 pandemic. Therefore, it is concluded that COVID-19 pandemic significantly affected long memory in return and volatility of cryptocurrency and international stock markets. In addition, our results suggest that the hybrid long memory model represented by the integration of ARFIMA-FIGARCH is significantly suitable to describe returns and volatility of cryptocurrencies and stocks and to reveal differences before and during COVID-19 pandemic periods.
International Review of Financial Analysis
We analyze money market dynamics under a long-run equilibrium framework where commonly-monitored ... more We analyze money market dynamics under a long-run equilibrium framework where commonly-monitored spreads serve as error correction terms, derived from a structural model incorporating autocorrelated risk premia, interest rate smoothing and monetary policy feedback. Using a dataset of monthly observations of the spot next and four-, thirteen-, twenty six-and fifty two-week Treasury Bills rates for the United States, Germany and United Kingdom from January 1999 to April 2016, we investigate the power of the expectations hypothesis theory of interest rates taking into account long-run deviations from equilibrium and inherent nonlinearities. We reveal short-run dynamic adjustments for the term structure of the USA, Germany and the UK, which are subject to regime switches. When forecastability is tested during May 2016-October 2017, the MSIH-VECM outperforms systematically the VECM. This is the first attempt to explore the possibility of parameter instability as a crucial factor in deriving the rejection of the restricted version of the cointegration space. Moreover, we investigate the dynamic out-of-sample forecasts of the term structure to assess the effectiveness of nonlinear MS-VECM modeling in capturing the after-effects of the global crisis. Overall, our results suggest that regime shifts in the mean and variance of the term structure may be intertwined with changes in fundamentals, that play a role in driving interest rate regimes, in particular business cycle and inflation fluctuations.
Experimental Validation of Disturbance Observer-based Adaptive Terminal Sliding Mode Control Subject to Control Input Limitations for SISO and MIMO Systems
European Journal of Control
A novel fuzzy mixed H2/H∞ optimal controller for hyperchaotic financial systems
Chaos, Solitons & Fractals
Abstract Due to the importance of consumed control energy in financial systems, employing an opti... more Abstract Due to the importance of consumed control energy in financial systems, employing an optimal controller for these systems could be beneficial. Also, the presence of disturbances is undeniable in most of these systems. Hence, applying optimal mixed H 2 / H ∞ control, which posses the positive features of both H ∞ and H 2 control, could bring up luminous results for these systems. However, in the literature, no attempt has been made to design this type of controller for chaotic financial systems. This issue motivated the current study. In this study, we present a type-2 fuzzy-based optimal mixed H 2 / H ∞ control for a hyperchaotic financial system. In this approach, H ∞ attenuates the effect of uncertainties and through H 2 the consumed control energy is minimized. Also, since the proposed controller is equipped with a type-2 fuzzy observer, it can easily handle uncertainties and unknown functions. Via Lyapunov theorem, it is confirmed that all signals in the system are bounded. In the numerical simulation, firstly, a hyperchaotic financial system with coexisting attractors is investigated. The proposed controller is then applied to the financial system, and the proposed technique's performance is assessed. Numerical simulations clearly confirm the theoretical claims about the effectiveness of the proposed methodology.
Symmetry
Control of supply chains with chaotic dynamics is an important, yet daunting challenge because of... more Control of supply chains with chaotic dynamics is an important, yet daunting challenge because of the limitations and constraints there are in the amplitude of control efforts. In real-world systems, applying control techniques that need a large amplitude signal is impractical. In the literature, there is no study that considers the control of supply chain systems subject to control input limitations. To this end, in the current study, a new control scheme is proposed to tackle this issue. In the designed control input, limitations in control inputs, as well as robustness against uncertainties, are taken into account. The proposed scheme is equipped with a fixed time disturbance observer to eliminate the destructive effects of uncertainties and disturbances. Additionally, the super-twisting sliding mode technique guarantees the fixed-time convergence of the closed-loop system. After that, a symmetric supply chain system is presented, and its chaotic attractors are demonstrated. Fina...
On chaos and projective synchronization of a fractional difference map with no equilibria using a fuzzy-based state feedback control
Physica A: Statistical Mechanics and its Applications
Abstract In this paper, by considering the Caputo-like delta difference definition, a fractional ... more Abstract In this paper, by considering the Caputo-like delta difference definition, a fractional difference order map with chaotic dynamics and with no equilibria is proposed. The complex dynamical behaviors associated with fractional difference order maps are analyzed employing the phase portraits, bifurcations diagrams, and Lyapunov exponents. The complexity of the sequence generated by the chaotic difference map is studied using the permutation entropy approach. Afterwards, projective synchronization of the systems is investigated. Fuzzy logic engines as intelligent schemes are strong tools for control of various systems. However, studies that apply fuzzy logic engines for control of fractional-order discrete-time systems are rare. Hence, in the current study, by taking advantages of fuzzy systems, a new controller is proposed for the fractional-order discrete-time map. The fuzzy logic engine is implemented in order to enhance the performance and agility of the proposed control technique. The stability of the closed-loop systems and asymptotic convergence of the projective synchronization error based on the proposed control scheme are proven. Finally, numerical simulations which clearly confirm that the offered control technique is able to push the states of the fractional-order discrete-time system to the desired value in a short period of time are presented.
Internet Finance
Start-Ups and SMEs
New developments in the Information and Communications Technology industry have substantially inc... more New developments in the Information and Communications Technology industry have substantially increased the importance of the internet over the last decade. As a result, the finance sector has developed its technological capability to be able to compete in an online marketplace with other financial services providers and to be able to serve their customer. This chapter examines the use of technology in the financial industry and the various factors associated with it, as well as introducing the reader to the main types of project initiators-contributor business relations in online crowdfunding.
Tracking Control and Stabilization of a Fractional Financial Risk System Using Novel Active Finite-Time Fault-Tolerant Controls
Fractals
This paper introduces a fractional-order financial risk system for the first time. Employing well... more This paper introduces a fractional-order financial risk system for the first time. Employing well-known tools and analyses such as bifurcations diagrams and spectral entropy, the dynamical behaviors of the system associated with fractional derivative are investigated. The impacts of the fractional derivative on the system’s behavior and its dynamical feature are shown. Then, tracking control and stabilization of the systems are studied. As it is obvious, the existence of faults and failures in the process of control of financial and economic systems is undeniable — this issue necessitates applying proper control techniques for the systems. So as to achieve appropriate results in the control of fractional financial risk system, two finite-time fault-tolerant controllers are proposed, namely, finite-time active fault-tolerant control and finite-time passive fault-tolerant control. Not only do these techniques force the system to reach desired values in finite time, but also, the propo...
Robust adaptive control of fractional-order memristive neural networks
Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
Mathematics
Due to the vital role of financial systems in today’s sophisticated world, applying intelligent c... more Due to the vital role of financial systems in today’s sophisticated world, applying intelligent controllers through management strategies is of crucial importance. We propose to formulate the control problem of the macroeconomic system as an optimization problem and find optimal actions using a reinforcement learning algorithm. Using the Q-learning algorithm, the best optimal action for the system is obtained, and the behavior of the system is controlled. We illustrate that it is possible to control the nonlinear dynamics of the macroeconomic systems using restricted actuation. The highly effective performance of the proposed controller for uncertain systems is demonstrated. The simulation results evidently confirm that the proposed controller satisfies the expected performance. In addition, the numerical simulations clearly confirm that even when we confined the control actions, the proposed controller effectively finds optimal actions for the nonlinear macroeconomic system.
Fractals
Mathematical modeling can be utilized to find out how the coronavirus spreads within a population... more Mathematical modeling can be utilized to find out how the coronavirus spreads within a population. Hence, considering models that can precisely describe natural phenomena is of crucial necessity. Besides, although one of the most significant benefits of mathematical modeling is designing optimal policies for battling the disease, there are a few studies that employ this beneficial aspect. To this end, this study aims to design optimal management policies for the novel coronavirus disease 2019 (COVID-19). This is pioneering research that designs optimal policies based on multi-objective evolutionary algorithms for control of the fractional-order model of the COVID-19 outbreak. Firstly, a fractional-order model of the disease dynamic is presented. The impacts of the fractional derivative's value on the modeling and forecasting of the disease spread are considered. After that, a multi-objective optimization problem is proposed by considering the rate of communication, the transition
Cognitive Computation
Background Like common stocks, Bitcoin price fluctuations are non-stationary and highly noisy. Du... more Background Like common stocks, Bitcoin price fluctuations are non-stationary and highly noisy. Due to attractiveness of Bitcoin in terms of returns and risk, Bitcoin price prediction is attracting a growing attention from both investors and researchers. Indeed, with the development of machine learning and especially deep learning, forecasting Bitcoin is receiving a particular interest. Methods We implement and apply deep forward neural network (DFFNN) for the analysis and forecasting Bitcoin highfrequency price data. Importantly, we seek to investigate the effect of standard numerical training algorithms on the accuracy obtained by DFFNN; namely, the conjugate gradient with Powell-Beale restarts, the resilient algorithm, and Levenberg-Marquardt algorithm. The DFFNN was applied to a big dataset composed of 65,535 samples. Results In terms of root mean of squared errors (RMSEs), the simulation results show that the DFFNN trained with the Levenberg-Marquardt algorithm outperforms DFFNN trained with Powell-Beale restarts algorithm and DFFNN trained with resilient algorithm. In addition, the resilient algorithm is fast which suggests that it could be promising in online training and trading. Conclusions The DFFNN trained with Levenberg-Marquardt algorithm is effective and easy to implement for Bitcoin highfrequency price data forecasting.
Deep learning systems for automatic diagnosis of infant cry signals
Chaos, Solitons & Fractals
This paper incorporates anticipated and unexpected shocks to bank capital into a DSGE model with ... more This paper incorporates anticipated and unexpected shocks to bank capital into a DSGE model with a banking sector. We apply this model to study Basel III countercyclical capital requirements and their implications for banking stability and household welfare. We introduce three different countercyclical capital rules. The first countercyclical capital rule responds to credit to output ratio. The second countercyclical rule reacts to deviations of credit to its steady state, and the third rule reacts to credit growth. The second rule proves to be the most effective tool in dampening credit supply, housing demand, household debt and output fluctuations as well as in enhancing the banking stability by ensuring that banks have higher bank capital and capital to asset ratio. After conducting a welfare analysis we find that the second rule outranks the other ones followed by the first rule, the baseline and the third rule respectively in terms of welfare accumulation.
This paper explores a real-world fundamental theme under a data science perspective. It specifica... more This paper explores a real-world fundamental theme under a data science perspective. It specifically discusses whether fraud or manipulation can be observed in and from municipality income tax size distributions, through their aggregation from citizen fiscal reports. The study case pertains to official data obtained from the Italian Ministry of Economics and Finance over the period 2007–2011. All Italian (20) regions are considered. The considered data science approach concretizes in the adoption of the Benford first digit law as quantitative tool. Marked disparities are found, for several regions, leading to unexpected “conclusions”. The most eye browsing regions are not the expected ones according to classical imagination about Italy financial shadow matters. © 2017 Elsevier Ltd. All rights reserved.
We assess the effectiveness of the forward guidance undertaken by European Central Bank using a s... more We assess the effectiveness of the forward guidance undertaken by European Central Bank using a standard medium-scale DSGE model a la Smets and Wouters (2007). Exploiting data on expectations from surveys, we show that incorporating expectations should be crucial in performance evaluation of models for the forward guidance. We conduct an exhaustive empirical exercise to compare the pseudo out-of-sample predictive performance of the estimated DSGE model with a Bayesian VAR and a DSGE-VAR models. DSGE model with expectations outperforms others for inflation; while for output and short term-interest rate the DSGE-VAR with expectations reports the best prediction.
Although policymakers and practitioners are particularly interested in DSGE models, these are typ... more Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be applied directly to the data and often yield weak prediction re- sults. Very recently, hybrid DSGE model
Annals of Operations Research
It is well documented that the biopharmaceutical sector has exhibited weak financial returns, con... more It is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in the biotech sector and ten asset classes using daily cross-country data from 1995 to 2019. We capture investors' heterogeneous investment horizons by decomposing time series according to frequencies. Using a maximal overlap discrete wavelet transform (MODWT) and a dynamic conditional correlation (DCC)-Student-t copula, diversification potentials are revealed, helping investors to reap the benefits of investing in biotech. Our findings indicate that the underlying assets exhibit nonlinear asymmetric behavior that strengthens during periods of turmoil.
Entropy
The Selkov system, which is typically employed to model glycolysis phenomena, unveils some rich d... more The Selkov system, which is typically employed to model glycolysis phenomena, unveils some rich dynamics and some other complex formations in biochemical reactions. In the present work, the synchronization problem of the glycolysis reaction-diffusion model is handled and examined. In addition, a novel convenient control law is designed in a linear form and, on the other hand, the stability of the associated error system is demonstrated through utilizing a suitable Lyapunov function. To illustrate the applicability of the proposed schemes, several numerical simulations are performed in one- and two-spatial dimensions.
Entropy
Although most of the early research studies on fractional-order systems were based on the Caputo ... more Although most of the early research studies on fractional-order systems were based on the Caputo or Riemann–Liouville fractional-order derivatives, it has recently been proven that these methods have some drawbacks. For instance, kernels of these methods have a singularity that occurs at the endpoint of an interval of definition. Thus, to overcome this issue, several new definitions of fractional derivatives have been introduced. The Caputo–Fabrizio fractional order is one of these nonsingular definitions. This paper is concerned with the analyses and design of an optimal control strategy for a Caputo–Fabrizio fractional-order model of the HIV/AIDS epidemic. The Caputo–Fabrizio fractional-order model of HIV/AIDS is considered to prevent the singularity problem, which is a real concern in the modeling of real-world systems and phenomena. Firstly, in order to find out how the population of each compartment can be controlled, sensitivity analyses were conducted. Based on the sensitivit...
The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets
Chaos, Solitons & Fractals
Abstract We examine long memory (self-similarity) in digital currencies and international stock e... more Abstract We examine long memory (self-similarity) in digital currencies and international stock exchanges prior and during COVID-19 pandemic. Specifically, ARFIMA and FIGARCH models are respectively employed to evaluate long memory parameter in returns and volatility. The dataset contains 45 cryptocurrency markets and 16 international equity markets. The t-test and F-test are performed to estimated long memory parameters. The empirical findings follow. First, the level of persistence in return series of both markets has increased during the COVID-19 pandemic. Second, during COVID-19 pandemic, variability level in persistence in return series has increased in both digital currencies and stock markets. Third, return series in both markets exhibited comparable level of persistence prior and during the COVID-19 pandemic. Fourth, return series in volatility series of cryptocurrency exhibited high degree of persistence compared to international stock markets during the COVID-19 pandemic. Therefore, it is concluded that COVID-19 pandemic significantly affected long memory in return and volatility of cryptocurrency and international stock markets. In addition, our results suggest that the hybrid long memory model represented by the integration of ARFIMA-FIGARCH is significantly suitable to describe returns and volatility of cryptocurrencies and stocks and to reveal differences before and during COVID-19 pandemic periods.
International Review of Financial Analysis
We analyze money market dynamics under a long-run equilibrium framework where commonly-monitored ... more We analyze money market dynamics under a long-run equilibrium framework where commonly-monitored spreads serve as error correction terms, derived from a structural model incorporating autocorrelated risk premia, interest rate smoothing and monetary policy feedback. Using a dataset of monthly observations of the spot next and four-, thirteen-, twenty six-and fifty two-week Treasury Bills rates for the United States, Germany and United Kingdom from January 1999 to April 2016, we investigate the power of the expectations hypothesis theory of interest rates taking into account long-run deviations from equilibrium and inherent nonlinearities. We reveal short-run dynamic adjustments for the term structure of the USA, Germany and the UK, which are subject to regime switches. When forecastability is tested during May 2016-October 2017, the MSIH-VECM outperforms systematically the VECM. This is the first attempt to explore the possibility of parameter instability as a crucial factor in deriving the rejection of the restricted version of the cointegration space. Moreover, we investigate the dynamic out-of-sample forecasts of the term structure to assess the effectiveness of nonlinear MS-VECM modeling in capturing the after-effects of the global crisis. Overall, our results suggest that regime shifts in the mean and variance of the term structure may be intertwined with changes in fundamentals, that play a role in driving interest rate regimes, in particular business cycle and inflation fluctuations.
Experimental Validation of Disturbance Observer-based Adaptive Terminal Sliding Mode Control Subject to Control Input Limitations for SISO and MIMO Systems
European Journal of Control
A novel fuzzy mixed H2/H∞ optimal controller for hyperchaotic financial systems
Chaos, Solitons & Fractals
Abstract Due to the importance of consumed control energy in financial systems, employing an opti... more Abstract Due to the importance of consumed control energy in financial systems, employing an optimal controller for these systems could be beneficial. Also, the presence of disturbances is undeniable in most of these systems. Hence, applying optimal mixed H 2 / H ∞ control, which posses the positive features of both H ∞ and H 2 control, could bring up luminous results for these systems. However, in the literature, no attempt has been made to design this type of controller for chaotic financial systems. This issue motivated the current study. In this study, we present a type-2 fuzzy-based optimal mixed H 2 / H ∞ control for a hyperchaotic financial system. In this approach, H ∞ attenuates the effect of uncertainties and through H 2 the consumed control energy is minimized. Also, since the proposed controller is equipped with a type-2 fuzzy observer, it can easily handle uncertainties and unknown functions. Via Lyapunov theorem, it is confirmed that all signals in the system are bounded. In the numerical simulation, firstly, a hyperchaotic financial system with coexisting attractors is investigated. The proposed controller is then applied to the financial system, and the proposed technique's performance is assessed. Numerical simulations clearly confirm the theoretical claims about the effectiveness of the proposed methodology.
Symmetry
Control of supply chains with chaotic dynamics is an important, yet daunting challenge because of... more Control of supply chains with chaotic dynamics is an important, yet daunting challenge because of the limitations and constraints there are in the amplitude of control efforts. In real-world systems, applying control techniques that need a large amplitude signal is impractical. In the literature, there is no study that considers the control of supply chain systems subject to control input limitations. To this end, in the current study, a new control scheme is proposed to tackle this issue. In the designed control input, limitations in control inputs, as well as robustness against uncertainties, are taken into account. The proposed scheme is equipped with a fixed time disturbance observer to eliminate the destructive effects of uncertainties and disturbances. Additionally, the super-twisting sliding mode technique guarantees the fixed-time convergence of the closed-loop system. After that, a symmetric supply chain system is presented, and its chaotic attractors are demonstrated. Fina...
On chaos and projective synchronization of a fractional difference map with no equilibria using a fuzzy-based state feedback control
Physica A: Statistical Mechanics and its Applications
Abstract In this paper, by considering the Caputo-like delta difference definition, a fractional ... more Abstract In this paper, by considering the Caputo-like delta difference definition, a fractional difference order map with chaotic dynamics and with no equilibria is proposed. The complex dynamical behaviors associated with fractional difference order maps are analyzed employing the phase portraits, bifurcations diagrams, and Lyapunov exponents. The complexity of the sequence generated by the chaotic difference map is studied using the permutation entropy approach. Afterwards, projective synchronization of the systems is investigated. Fuzzy logic engines as intelligent schemes are strong tools for control of various systems. However, studies that apply fuzzy logic engines for control of fractional-order discrete-time systems are rare. Hence, in the current study, by taking advantages of fuzzy systems, a new controller is proposed for the fractional-order discrete-time map. The fuzzy logic engine is implemented in order to enhance the performance and agility of the proposed control technique. The stability of the closed-loop systems and asymptotic convergence of the projective synchronization error based on the proposed control scheme are proven. Finally, numerical simulations which clearly confirm that the offered control technique is able to push the states of the fractional-order discrete-time system to the desired value in a short period of time are presented.
Internet Finance
Start-Ups and SMEs
New developments in the Information and Communications Technology industry have substantially inc... more New developments in the Information and Communications Technology industry have substantially increased the importance of the internet over the last decade. As a result, the finance sector has developed its technological capability to be able to compete in an online marketplace with other financial services providers and to be able to serve their customer. This chapter examines the use of technology in the financial industry and the various factors associated with it, as well as introducing the reader to the main types of project initiators-contributor business relations in online crowdfunding.
Tracking Control and Stabilization of a Fractional Financial Risk System Using Novel Active Finite-Time Fault-Tolerant Controls
Fractals
This paper introduces a fractional-order financial risk system for the first time. Employing well... more This paper introduces a fractional-order financial risk system for the first time. Employing well-known tools and analyses such as bifurcations diagrams and spectral entropy, the dynamical behaviors of the system associated with fractional derivative are investigated. The impacts of the fractional derivative on the system’s behavior and its dynamical feature are shown. Then, tracking control and stabilization of the systems are studied. As it is obvious, the existence of faults and failures in the process of control of financial and economic systems is undeniable — this issue necessitates applying proper control techniques for the systems. So as to achieve appropriate results in the control of fractional financial risk system, two finite-time fault-tolerant controllers are proposed, namely, finite-time active fault-tolerant control and finite-time passive fault-tolerant control. Not only do these techniques force the system to reach desired values in finite time, but also, the propo...