1
|
Bekiros S, Jahanshahi H, Munoz-Pacheco JM. A new buffering theory of social support and psychological stress. PLoS One 2022; 17:e0275364. [PMID: 36223401 PMCID: PMC9555651 DOI: 10.1371/journal.pone.0275364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 09/15/2022] [Indexed: 11/06/2022] Open
Abstract
A dynamical model linking stress, social support, and health has been recently proposed and numerically analyzed from a classical point of view of integer-order calculus. Although interesting observations have been obtained in this way, the present work conducts a fractional-order analysis of that model. Under a periodic forcing of an environmental stress variable, the perceived stress has been analyzed through bifurcation diagrams and two well-known metrics of entropy and complexity, such as spectral entropy and C0 complexity. The results obtained by numerical simulations have shown novel insights into how stress evolves with frequency and amplitude of the perturbation, as well as with initial conditions for the system variables. More precisely, it has been observed that stress can alternate between chaos, periodic oscillations, and stable behaviors as the fractional order varies. Moreover, the perturbation frequency has revealed a narrow interval for the chaotic oscillations, while its amplitude may present different values indicating a low sensitivity regarding chaos generation. Also, the perceived stress has been noted to be highly sensitive to initial conditions for the symptoms of stress-related ill-health and for the social support received from family and friends. This work opens new directions of research whereby fractional calculus might offer more insight into psychology, life sciences, mental disorders, and stress-free well-being.
Collapse
Affiliation(s)
- Stelios Bekiros
- LSE Health Centre & Department of Health Policy, London School of Economics and Political Science (LSE), London, United Kingdom
- Faculty of Economics &Management (FEMA), University of Malta, Msida, Malta
- * E-mail: , ,
| | - Hadi Jahanshahi
- Department of Mechanical Engineering, University of Manitoba, Winnipeg, Canada
| | - Jesus M. Munoz-Pacheco
- Faculty of Electronics Sciences, Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
| |
Collapse
|
2
|
Bekiros S, Soradi-Zeid S, Mou J, Yousefpour A, Zambrano-Serrano E, Jahanshahi H. Laguerre Wavelet Approach for a Two-Dimensional Time-Space Fractional Schrödinger Equation. Entropy (Basel) 2022; 24:1105. [PMID: 36010769 PMCID: PMC9407114 DOI: 10.3390/e24081105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/27/2022] [Accepted: 08/07/2022] [Indexed: 06/15/2023]
Abstract
This article is devoted to the determination of numerical solutions for the two-dimensional time-spacefractional Schrödinger equation. To do this, the unknown parameters are obtained using the Laguerre wavelet approach. We discretize the problem by using this technique. Then, we solve the discretized nonlinear problem by means of a collocation method. The method was proven to give very accurate results. The given numerical examples support this claim.
Collapse
Affiliation(s)
- Stelios Bekiros
- FEMA, University of Malta, MSD 2080 Msida, Malta
- LSE Health, Department of Health Policy, London School of Economics and Political Science, London WC2A 2AE, UK
- IPAG Business School (IPAG), 184, bd Saint-Germain, 75006 Paris, France
| | - Samaneh Soradi-Zeid
- Faculty of Industry and Mining (Khash), University of Sistan and Baluchestan, Zahedan 9816745845, Iran
| | - Jun Mou
- School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, China
| | - Amin Yousefpour
- Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 94720, USA
| | - Ernesto Zambrano-Serrano
- Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, Av. Universidad S/N, Cd. Universitaria, San Nicolás de los Garza C.P. 66455, NL, Mexico
| | - Hadi Jahanshahi
- Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada or
| |
Collapse
|
3
|
Lahmiri S, Bekiros S. The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets. Chaos Solitons Fractals 2021; 151:111221. [PMID: 36568907 PMCID: PMC9759418 DOI: 10.1016/j.chaos.2021.111221] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 06/23/2021] [Indexed: 05/26/2023]
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.
Collapse
Affiliation(s)
- Salim Lahmiri
- Chaire innovation et économie numérique, ESCA École de Management, Casablanca, Morocco
| | - Stelios Bekiros
- Department of Banking and Finance, FEMA, University of Malta, Malta
- Department of Economics, European University Institute, Florence, Italy
| |
Collapse
|
4
|
Beigi A, Yousefpour A, Yasami A, Gómez-Aguilar JF, Bekiros S, Jahanshahi H. Application of reinforcement learning for effective vaccination strategies of coronavirus disease 2019 (COVID-19). Eur Phys J Plus 2021; 136:609. [PMID: 34094796 PMCID: PMC8166378 DOI: 10.1140/epjp/s13360-021-01620-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 05/26/2021] [Indexed: 05/08/2023]
Abstract
Since December 2019, the new coronavirus has raged in China and subsequently all over the world. From the first days, researchers have tried to discover vaccines to combat the epidemic. Several vaccines are now available as a result of the contributions of those researchers. As a matter of fact, the available vaccines should be used in effective and efficient manners to put the pandemic to an end. Hence, a major problem now is how to efficiently distribute these available vaccines among various components of the population. Using mathematical modeling and reinforcement learning control approaches, the present article aims to address this issue. To this end, a deterministic Susceptible-Exposed-Infectious-Recovered-type model with additional vaccine components is proposed. The proposed mathematical model can be used to simulate the consequences of vaccination policies. Then, the suppression of the outbreak is taken to account. The main objective is to reduce the effects of Covid-19 and its domino effects which stem from its spreading and progression. Therefore, to reach optimal policies, reinforcement learning optimal control is implemented, and four different optimal strategies are extracted. Demonstrating the efficacy of the proposed methods, finally, numerical simulations are presented.
Collapse
Affiliation(s)
- Alireza Beigi
- School of Mechanical Engineering, College of Engineering, University of Tehran, 14399‒57131 Tehran, Iran
| | - Amin Yousefpour
- School of Mechanical Engineering, College of Engineering, University of Tehran, 14399‒57131 Tehran, Iran
| | - Amirreza Yasami
- School of Mechanical Engineering, College of Engineering, University of Tehran, 14399‒57131 Tehran, Iran
| | - J. F. Gómez-Aguilar
- CONACyT-Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, C.P. 62490 Cuernavaca, Morelos Mexico
| | - Stelios Bekiros
- Department of Banking and Finance, FEMA, , University of Malta, Msida, MSD 2080 Malta
- Department of Economics, European University Institute, Via delle Fontanelle, 18, 50014 Florence, Italy
| | - Hadi Jahanshahi
- Department of Mechanical Engineering, University of Manitoba, Winnipeg, R3T 5V6 Canada
| |
Collapse
|
5
|
Wang H, Jahanshahi H, Wang MK, Bekiros S, Liu J, Aly AA. A Caputo-Fabrizio Fractional-Order Model of HIV/AIDS with a Treatment Compartment: Sensitivity Analysis and Optimal Control Strategies. Entropy (Basel) 2021; 23:e23050610. [PMID: 34069228 PMCID: PMC8156190 DOI: 10.3390/e23050610] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/30/2021] [Accepted: 05/12/2021] [Indexed: 11/26/2022]
Abstract
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 sensitivity analyses, the most effective agents in disease transmission and prevalence were selected as control inputs. In this way, a modified Caputo–Fabrizio fractional-order model of the HIV/AIDS epidemic is proposed. By changing the contact rate of susceptible and infectious people, the atraumatic restorative treatment rate of the treated compartment individuals, and the sexual habits of susceptible people, optimal control was designed. Lastly, simulation results that demonstrate the appropriate performance of the Caputo–Fabrizio fractional-order model and proposed control scheme are illustrated.
Collapse
Affiliation(s)
- Hua Wang
- School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha 410114, China;
| | - Hadi Jahanshahi
- Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada;
| | - Miao-Kun Wang
- Department of Mathematics, Huzhou University, Huzhou 313000, China
- Correspondence: (M.-K.W.); (S.B.)
| | - Stelios Bekiros
- Department of Banking and Finance, FEMA, University of Malta, MSD 2080 Msida, Malta
- European University Institute, Department of Economics, Via delle Fontanelle, 18, I-50014 Florence, Italy
- Correspondence: (M.-K.W.); (S.B.)
| | - Jinping Liu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China;
| | - Ayman A. Aly
- Department of Mechanical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| |
Collapse
|
6
|
|
7
|
Jahanshahi H, Munoz-Pacheco JM, Bekiros S, Alotaibi ND. A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19. Chaos Solitons Fractals 2021; 143:110632. [PMID: 33519121 PMCID: PMC7832492 DOI: 10.1016/j.chaos.2020.110632] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/23/2020] [Accepted: 12/25/2020] [Indexed: 05/04/2023]
Abstract
COVID-19 is a novel coronavirus affecting all the world since December last year. Up to date, the spread of the outbreak continues to complicate our lives, and therefore, several research efforts from many scientific areas are proposed. Among them, mathematical models are an excellent way to understand and predict the epidemic outbreaks evolution to some extent. Due to the COVID-19 may be modeled as a non-Markovian process that follows power-law scaling features, we present a fractional-order SIRD (Susceptible-Infected-Recovered-Dead) model based on the Caputo derivative for incorporating the memory effects (long and short) in the outbreak progress. Additionally, we analyze the experimental time series of 23 countries using fractal formalism. Like previous works, we identify that the COVID-19 evolution shows various power-law exponents (no just a single one) and share some universality among geographical regions. Hence, we incorporate numerous memory indexes in the proposed model, i.e., distinct fractional-orders defined by a time-dependent function that permits us to set specific memory contributions during the evolution. This allows controlling the memory effects of more early states, e.g., before and after a quarantine decree, which could be less relevant than the contribution of more recent ones on the current state of the SIRD system. We also prove our model with Italy's real data from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.
Collapse
Affiliation(s)
- Hadi Jahanshahi
- Department of Mechanical Engineering, University of Manitoba, Winnipeg R3T 5V6, Canada
| | - Jesus M Munoz-Pacheco
- Faculty of Electronics Sciences, Benemerita Universidad Autonoma de Puebla, 72570 Mexico
| | - Stelios Bekiros
- European University Institute, Department of Economics, Via delle Fontanelle, 18, Florence, I-50014, Italy
- Rimini Centre for Economic Analysis (RCEA), LH3079, Wilfrid Laurier University, 75 University Ave W., ON Waterloo, N2L3C5, Canada
| | - Naif D Alotaibi
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| |
Collapse
|
8
|
Chen SB, Soradi-Zeid S, Jahanshahi H, Alcaraz R, Gómez-Aguilar JF, Bekiros S, Chu YM. Optimal Control of Time-Delay Fractional Equations via a Joint Application of Radial Basis Functions and Collocation Method. Entropy (Basel) 2020; 22:e22111213. [PMID: 33286981 PMCID: PMC7711967 DOI: 10.3390/e22111213] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 11/16/2022]
Abstract
A novel approach to solve optimal control problems dealing simultaneously with fractional differential equations and time delay is proposed in this work. More precisely, a set of global radial basis functions are firstly used to approximate the states and control variables in the problem. Then, a collocation method is applied to convert the time-delay fractional optimal control problem to a nonlinear programming one. By solving the resulting challenge, the unknown coefficients of the original one will be finally obtained. In this way, the proposed strategy introduces a very tunable framework for direct trajectory optimization, according to the discretization procedure and the range of arbitrary nodes. The algorithm's performance has been analyzed for several non-trivial examples, and the obtained results have shown that this scheme is more accurate, robust, and efficient than most previous methods.
Collapse
Affiliation(s)
- Shu-Bo Chen
- School of Science, Hunan City University, Yiyang 413000, China;
| | - Samaneh Soradi-Zeid
- Faculty of Industry and Mining (khash), University of Sistan and Baluchestan, Zahedan 98155-987, Iran;
| | - Hadi Jahanshahi
- Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada;
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha (UCLM), 16071 Cuenca, Spain
- Correspondence: (R.A.); (Y.-M.C.)
| | - José Francisco Gómez-Aguilar
- CONACyT-Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, Cuernavaca C.P. 62490, Morelos, Mexico;
| | - Stelios Bekiros
- Department of Economics, European University Institute, Via delle Fontanelle, 18, I-50014 Florence, Italy;
- Rimini Centre for Economic Analysis (RCEA), LH3079, Wilfrid Laurier University, 75 University Ave W., Waterloo, ON N2L3C5, Canada
| | - Yu-Ming Chu
- Department of Mathematics, Huzhou University, Huzhou 313000, China
- Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science & Technology, Changsha 410114, China
- Correspondence: (R.A.); (Y.-M.C.)
| |
Collapse
|
9
|
Lahmiri S, Bekiros S. Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic. Chaos Solitons Fractals 2020; 139:110084. [PMID: 32834621 PMCID: PMC7347498 DOI: 10.1016/j.chaos.2020.110084] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 07/02/2020] [Indexed: 05/22/2023]
Abstract
The COVID-19 pandemic has seriously affected world economies. In this regard, it is expected that information level and sharing between equity, digital currency, and energy markets has been altered due to the pandemic outbreak. Specifically, the resulting twisted risk among markets is presumed to rise during the abnormal state of world economy. The purpose of the current study is twofold. First, by using Renyi entropy, we analyze the multiscale entropy function in the return time series of Bitcoin, S&P500, WTI, Brent, Gas, Gold, Silver, and investor fear index represented by VIX. Second, by estimating mutual information, we analyze the information sharing between these markets. The analyses are conducted before and during the COVID-19 pandemic. The empirical results from Renyi entropy indicate that for all market indices, randomness and disorder are more concentrated in less probable events. The empirical results from mutual information showed that the information sharing network between markets has changed during the COVID-19 pandemic. From a managerial perspective, we conclude that during the pandemic (i) portfolios composed of Bitcoin and Silver, Bitcoin and WTI, Bitcoin and Gold, Bitcoin and Brent, or Bitcoin and S&P500 could be risky, (ii) diversification opportunities exist by investing in portfolios composed of Gas and Silver, Gold and Silver, Gold and Gas, Brent and Silver, Brent and Gold, or Bitcoin and Gas, and that (iii) the VIX exhibited the lowest level of information disorder at all scales before and during the pandemic. Thus, it seems that the pandemic has not influenced the expectations of investors. Our results provide an insight of the response of stocks, cryptocurrencies, energy, precious metal markets, to expectations of investors in the aftermath of the COVID-19 pandemic in terms of information ordering and sharing.
Collapse
Affiliation(s)
- Salim Lahmiri
- Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, Canada
| | - Stelios Bekiros
- Department of Economics, European University Institute, Florence, Italy
- Rimini Centre for Economic Analysis, Wilfrid Laurier University, Waterloo, Canada
| |
Collapse
|
10
|
Lahmiri S, Bekiros S. The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets. Chaos Solitons Fractals 2020; 138:109936. [PMID: 32501379 PMCID: PMC7254002 DOI: 10.1016/j.chaos.2020.109936] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 05/25/2020] [Indexed: 05/20/2023]
Abstract
We explore the evolution of the informational efficiency in 45 cryptocurrency markets and 16 international stock markets before and during COVID-19 pandemic. The measures of Largest Lyapunov Exponent (LLE) based on the Rosenstein's method and Approximate Entropy (ApEn), which are robust to small samples, are applied to price time series in order to estimate degrees of stability and irregularity in cryptocurrency and international stock markets. The amount of regularity infers on the unpredictability of fluctuations. The t-test and F-test are performed on estimated LLE and ApEn. In total, 36 statistical tests are performed to check for differences between time periods (pre- versus during COVID-19 pandemic samples) on the one hand, as well as check for differences between markets (cryptocurrencies versus stocks), on the other hand. During the COVID-19 pandemic period it was found that (a) the level of stability in cryptocurrency markets has significantly diminished while the irregularity level significantly augmented, (b) the level of stability in international equity markets has not changed but gained more irregularity, (c) cryptocurrencies became more volatile, (d) the variability in stability and irregularity in equities has not been affected, (e) cryptocurrency and stock markets exhibit a similar degree of stability in price dynamics, whilst finally (f) cryptocurrency exhibit a low level of regularity compared to international equity markets. We find that cryptos showed more instability and more irregularity during the COVID-19 pandemic compared to international stock markets. Thus, from an informational efficiency perspective, investing in digital assets during big crises as the COVID-19 pandemic, could be considered riskier as opposed to equities.
Collapse
Affiliation(s)
- Salim Lahmiri
- Department of Supply Chain & Business Technology Management, John Molson School of Business, Concordia University, Montreal, Canada
| | - Stelios Bekiros
- Department of Economics, European University Institute, Florence, Italy
- Rimini Centre for Economic Analysis, Wilfrid Laurier University, Waterloo, Canada
| |
Collapse
|
11
|
Lahmiri S, Bekiros S. Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic. Entropy (Basel) 2020; 22:e22080833. [PMID: 33286604 PMCID: PMC7517433 DOI: 10.3390/e22080833] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/23/2020] [Accepted: 07/27/2020] [Indexed: 12/23/2022]
Abstract
The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.
Collapse
Affiliation(s)
- Salim Lahmiri
- Department of Supply Chain & Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC H3H 0A1, Canada;
| | - Stelios Bekiros
- Department of Economics, European University Institute, 50014 Florence, Italy
- Rimini Centre for Economic Analysis, Wilfrid Laurier University, 75 University Ave W., Waterloo, ON N2L 3C5, Canada
- Correspondence:
| |
Collapse
|
12
|
Bekiros S, Kouloumpou D. SBDiEM: A new mathematical model of infectious disease dynamics. Chaos Solitons Fractals 2020; 136:109828. [PMID: 32327901 PMCID: PMC7177179 DOI: 10.1016/j.chaos.2020.109828] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 04/16/2020] [Indexed: 05/18/2023]
Abstract
A worldwide multi-scale interplay among a plethora of factors, ranging from micro-pathogens and individual or population interactions to macro-scale environmental, socio-economic and demographic conditions, entails the development of highly sophisticated mathematical models for robust representation of the contagious disease dynamics that would lead to the improvement of current outbreak control strategies and vaccination and prevention policies. Due to the complexity of the underlying interactions, both deterministic and stochastic epidemiological models are built upon incomplete information regarding the infectious network. Hence, rigorous mathematical epidemiology models can be utilized to combat epidemic outbreaks. We introduce a new spatiotemporal approach (SBDiEM) for modeling, forecasting and nowcasting infectious dynamics, particularly in light of recent efforts to establish a global surveillance network for combating pandemics with the use of artificial intelligence. This model can be adjusted to describe past outbreaks as well as COVID-19. Our novel methodology may have important implications for national health systems, international stakeholders and policy makers.
Collapse
Affiliation(s)
- Stelios Bekiros
- European University Institute, Via delle Fontanelle, 18, Florence I-50014, Italy
- RCEA, LH3079, Wilfrid Laurier University, 75 University Ave W., Waterloo, ON N2L3C5, Canada
- Corresponding author at: Department of Economics, Via delle Fontanelle, 18, I-50014 Florence, Italy.
| | - Dimitra Kouloumpou
- Hellenic Naval Academy, Section of Mathematics, Mathematical Modeling and Applications Laboratory, Piraeus 18539, Greece
| |
Collapse
|
13
|
Yousefpour A, Jahanshahi H, Bekiros S. Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak. Chaos Solitons Fractals 2020; 136:109883. [PMID: 32427205 PMCID: PMC7229919 DOI: 10.1016/j.chaos.2020.109883] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 05/08/2020] [Indexed: 05/03/2023]
Abstract
Understanding the early transmission dynamics of diseases and estimating the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying the proposed optimal policies, governments could find useful and practical ways for control of the disease.
Collapse
Affiliation(s)
- Amin Yousefpour
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, 11155-4563, Iran
| | - Hadi Jahanshahi
- Department of Mechanical Engineering, University of Manitoba, Winnipeg R3T 5V6, Canada
| | - Stelios Bekiros
- European University Institute, Department of Economics, Via delle Fontanelle, 18, I-50014, Florence, Italy
- Rimini Centre for Economic Analysis (RCEA), LH3079, Wilfrid Laurier University, 75 University Ave W., ON, N2L3C5, Waterloo, Canada
| |
Collapse
|
14
|
Lahmiri S, Bekiros S. The Informational Dynamics of Mean‒Variance Relationships in Fertilizer Markets: An Entropic Investigation. Entropy (Basel) 2018; 20:e20090677. [PMID: 33265766 PMCID: PMC7513201 DOI: 10.3390/e20090677] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 09/03/2018] [Accepted: 09/04/2018] [Indexed: 11/16/2022]
Abstract
The risk‒return trade-off is a fundamental relationship that has received a large amount of attention in financial and economic analysis. Indeed, it has important implications for understanding linear dynamics in price returns and active quantitative portfolio optimization. The main contributions of this work include, firstly, examining such a relationship in five major fertilizer markets through different time periods: a period of low variability in returns and a period of high variability such as that during which the recent global financial crisis occurred. Secondly, we explore how entropy in those markets varies during the investigated time periods. This requires us to assess their inherent informational dynamics. The empirical results show that higher volatility is associated with a larger return in diammonium phosphate, potassium chloride, triple super phosphate, and urea market, but not rock phosphate. In addition, the magnitude of this relationship is low during a period of high variability. It is concluded that key statistical patterns of return and the relationship between return and volatility are affected during high variability periods. Our findings indicate that entropy in return and volatility series of each fertilizer market increase significantly during time periods of high variability.
Collapse
Affiliation(s)
- Salim Lahmiri
- Department of Quantitative Methods, ESCA School of Management, Casablanca 20000, Morocco
| | - Stelios Bekiros
- Department of Accounting & Finance, Athens University of Economics & Business, 76 Patission Str., GR10434 Athens, Greece
- Department of Economics, European University Institute (EUI), 50121 Florence, Italy
- Correspondence: ; Tel./Fax: +30-21-08203486
| |
Collapse
|