
Logistic sigmoids due to their flexibility seem to be natural candidates for modelling macrofinancial leverage behavior. The sigmoidal leverage transition towards its stationary value, which was driven by the yield spreads, could have replicated the dynamics of macrofinancial assets, debt and capital. The leverage transition, in its turn, has been a major factor in better balancing macrofinancial liabilities and assets. The sigmoidal leverage trajectories including their inflections and different phases were identified by a nonlinear transition function providing information necessary for steering the process towards its stable state. Solving the stationary Kolmogorov-Fokker-Plank logistic equation revealed that random leverage realizations might follow the gamma distribution. Parameters of its stationary probability density function, as well as the expected and the modal leverage, were dependent on the process variance and the yield spreads. Thus, the stochastic leverage behaviour reproduced a sequence of stylized phases similar to the observed in the US Treasuries market meltdown in 2020. In particular, larger yield spreads and smaller modal leverage signalled a "defensive" market response to sudden volatility increases. In addition, it was shown that the logistic leverage modelling could be helpful in the analysis of debt and money dynamics including some consequences of "minting a one trillion dollars coin".
Citation: Alexander D. Smirnov. Sigmoidal dynamics of macro-financial leverage[J]. Quantitative Finance and Economics, 2023, 7(1): 147-164. doi: 10.3934/QFE.2023008
[1] | Naime Altay, Ebru Kılıcarslan Toruner, Ebru Akgun-CITAK . Determine the BMI levels, self-concept and healthy life behaviours of children during a school based obesity training programme. AIMS Public Health, 2020, 7(3): 535-547. doi: 10.3934/publichealth.2020043 |
[2] | Marie K. Fialkowski, Ashley Yamanaka, Lynne R. Wilkens, Kathryn L. Braun, Jean Butel, Reynolette Ettienne, Katalina McGlone, Shelley Remengesau, Julianne M. Power, Emihner Johnson, Daisy Gilmatam, Travis Fleming, Mark Acosta, Tayna Belyeu-Camacho, Moria Shomour, Cecilia Sigrah, Claudio Nigg, Rachel Novotny . Recruitment Strategies and Lessons Learned from the Children’s Healthy Living Program Prevalence Survey. AIMS Public Health, 2016, 3(1): 140-157. doi: 10.3934/publichealth.2016.1.140 |
[3] | Elizabeth Dean, Margot Skinner, Homer Peng-Ming Yu, Alice YM Jones, Rik Gosselink, Anne Söderlund . Why COVID-19 strengthens the case to scale up assault on non-communicable diseases: role of health professionals including physical therapists in mitigating pandemic waves. AIMS Public Health, 2021, 8(2): 369-375. doi: 10.3934/publichealth.2021028 |
[4] | Helen Mary Haines, Opie Cynthia, David Pierce, Lisa Bourke . Notwithstanding High Prevalence of Overweight and Obesity, Smoking Remains the Most Important Factor in Poor Self-rated Health and Hospital Use in an Australian Regional Community. AIMS Public Health, 2017, 4(4): 402-417. doi: 10.3934/publichealth.2017.4.402 |
[5] | Sameer Badri Al-Mhanna, Alexios Batrakoulis, Abdulrahman M. Sheikh, Abdulaziz A. Aldayel, Abdulwali Sabo, Mahaneem Mohamed, Hafeez Abiola Afolabi, Abdirizak Yusuf Ahmed, Sahra Isse Mohamed, Mehmet Gülü, Wan Syaheedah Wan Ghazali . Impact of COVID-19 lockdown on physical activity behavior among students in Somalia. AIMS Public Health, 2024, 11(2): 459-476. doi: 10.3934/publichealth.2024023 |
[6] | Tyler C. Smith MS PhD, Besa Smith MPH PhD . Understanding the Early Signs of Chronic Disease by Investigating the Overlap of Mental Health Needs and Adolescent Obesity. AIMS Public Health, 2015, 2(3): 487-500. doi: 10.3934/publichealth.2015.3.487 |
[7] | MaríaVictorinaAguilarVilas, GabrielaRubalcava, AntonioBecerra, MaríaCarmenMartínezPara . Nutritional Status and Obesity Prevalence in People with Gender Dysphoria. AIMS Public Health, 2014, 1(3): 137-146. doi: 10.3934/publichealth.2014.3.137 |
[8] | Martin Burtscher, Grégoire P Millet, Jeannette Klimont, Johannes Burtscher . Differences in the prevalence of physical activity and cardiovascular risk factors between people living at low (<1,001 m) compared to moderate (1,001–2,000 m) altitude. AIMS Public Health, 2021, 8(4): 624-635. doi: 10.3934/publichealth.2021050 |
[9] | Richard Bailey, Claude Scheuer . The COVID-19 pandemic as a fortuitous disruptor in physical education: the case of active homework. AIMS Public Health, 2022, 9(2): 423-439. doi: 10.3934/publichealth.2022029 |
[10] | Karl Peltzer, Supa Pengpid . The Association of Dietary Behaviors and Physical Activity Levels with General and Central Obesity among ASEAN University Students. AIMS Public Health, 2017, 4(3): 301-313. doi: 10.3934/publichealth.2017.3.301 |
Logistic sigmoids due to their flexibility seem to be natural candidates for modelling macrofinancial leverage behavior. The sigmoidal leverage transition towards its stationary value, which was driven by the yield spreads, could have replicated the dynamics of macrofinancial assets, debt and capital. The leverage transition, in its turn, has been a major factor in better balancing macrofinancial liabilities and assets. The sigmoidal leverage trajectories including their inflections and different phases were identified by a nonlinear transition function providing information necessary for steering the process towards its stable state. Solving the stationary Kolmogorov-Fokker-Plank logistic equation revealed that random leverage realizations might follow the gamma distribution. Parameters of its stationary probability density function, as well as the expected and the modal leverage, were dependent on the process variance and the yield spreads. Thus, the stochastic leverage behaviour reproduced a sequence of stylized phases similar to the observed in the US Treasuries market meltdown in 2020. In particular, larger yield spreads and smaller modal leverage signalled a "defensive" market response to sudden volatility increases. In addition, it was shown that the logistic leverage modelling could be helpful in the analysis of debt and money dynamics including some consequences of "minting a one trillion dollars coin".
Flies are complete metamorphosis insects that contain various species, including Muscidae (houseflies), Calliphoridae (blowflflies) Drosophilae (fruitflies) and Scrcophagidae (fleshflies), etc. The life history of flies can be divided into egg, larva, pre-pupa, pupa and adult stages. Although the life span of flies is only about one month, they are very fertile and multiply rapidly in a short period [1]. The feeding habits of flies are very complex. They can feed on a variety of substances, such as human food, animal waste, kitchen scraps and other refuses. It is known to us that flies transmit various pathogens from filth to humans and cause many diseases [2,3,4]. On the other hand, flies are also beneficial to medical research, ecosystem food chain and pollen dispersal. Considering medical research, for example, fruit fly Drosophila is of great significance in studying the pathogenesis and therapy of human diseases. The nervous system of Drosophila is much simpler than that of human beings, but it also exhibits complex behavioral characteristics similar to humans [5,6]. Therefore, studying fly population dynamics is of crucial importance to both nature and human society.
The study of biological population growth model promotes the development of human society to a great extent. It has important applications in population control, social resource allocation, ecological environment improvement, species protection and human life and health [7,8,9]. To understand the population dynamics of the Australian sheep blowfly, Gurney et al. [10] constructed the autonomous delay differential equation
x′(t)=−δx(t)+Px(t−τ)e−γx(t−τ) |
based on experimental data [11,12]. In this model, x is the density of mature blowflies, δ is the daily mortality rate of adult blowflies, P is the maximum daily spawning rate of female blowflies, τ is the time required for a blowfly to mature from an egg to an adult, 1/γ is the blowfly population size at which the production function f(u)=ue−γu reaches the maximum value. Subsequently, this model and its modified extensions were continually used to describe rich fly dynamics.
Environmental changes play an important role in biological systems. The influence of a periodically changing environment on the system is different from that of a constant environment, and it can better facilitate system evolution. Moreover, delay is one of the important factors which can change the dynamical properties and result in more rich and complex dynamics in biological systems [13,14]. Many researchers have assumed periodic coefficients and time delays in the system to combine with the periodic changes of the environment [15,16,17,18]. For related literature, we refer to [19,20]. However, considering the fact that adult flies number is a discrete value that varies daily and the situations where population numbers are small and individual effects are important or dominate, a discrete model would indeed be more realistic to describe the population evolution in discrete time-steps [21,22,23].
Interactions between different species are extremely important for maintaining ecological balance. Such interactions are typically direct or indirect between multiple species, including positive interactions and negative interactions. Among them, the positive interactions can be divided into three categories according to the degree of action: commensalism, protocooperation and mutualism [24,25]. In the paper [9], a delay differential Nicholson-type system concerning the mutualism effects with constant coefficients was proposed. The existence, global stability and instability of positive equilibrium were obtained. Based on this system, Zhou [26] and Amster [27] considered periodic Nicholson-type system combined with nonlinear harvesting terms. The main research theme is the existence of positive periodic solutions. Recently, Ossandóna et al. [28] presented a Nicholson-type system with nonlinear density-dependent mortality to describe the dynamics of multiple species, the uniqueness and local exponential stability of the periodic solution are established. However, relatively few studies on discrete dynamical systems have explored the mutualism of flies. In this paper, we consider the mutualism relationship between two fly species and establish a two-dimensional discrete Nicholson system with multiple time-varying delays
{Δx1(k)=−a1(k)x1(k)+b1(k)x2(k)+∑nj=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k)), Δx2(k)=−a2(k)x2(k)+b2(k)x1(k)+∑nj=1c2j(k)x2(k−τ2j(k))e−γ2j(k)x2(k−τ2j(k)). | (1.1) |
We assume that ai:Z→(0,1), bi:Z→(0,∞), cij:Z→(0,∞), τij:Z→Z+ and γij:Z→(0,∞) are ω-periodic discrete functions for 1≤i≤2 and 1≤j≤n. The period ω is a positive integer. Moreover, the interaction rate of second fly specie on first fly species and that of first fly specie on second fly species are represented by b1 and b2, respectively.
Because τij (1≤i≤2) have ω-periodicity, we can find the maximum values
¯τi=max1≤j≤n{max1≤k≤ωτij(k)}∈Z+ |
of {τi1(k)}, {τi2(k)}, …, {τin(k)} for i=1,2. Note that 0<ai(k)<1 for k∈Z. Then, the solution x(⋅,ϕ)=(x1(⋅,ϕ1),x2(⋅,ϕ2))T of system (1.1) that satisfies the initial condition
xi(s)=ϕi(s)>0fors∈[−¯τi,0]∩Z | (1.2) |
is a positive solution. The purpose of this paper is to present sufficient conditions for the existence of positive ω-periodic solution of (1.1).
We discuss the parametric delay difference system
{Δx1(k)=−λa1(k)x1(k)+λb1(k)x2(k)+λ∑nj=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k)), Δx2(k)=−λa2(k)x2(k)+λb2(k)x1(k)+λ∑nj=1c2j(k)x2(k−τ2j(k))e−γ2j(k)x2(k−τ2j(k)) | (2.1) |
for each parameter λ∈(0,1). Let a_i=min1≤k≤ωai(k) and ¯bi=max1≤k≤ωbi(k) for i=1,2. Then, an estimation of upper and lower bounds of positive ω-periodic solution of (2.1) can be conducted.
Proposition 2.1. Suppose that
a_1a_2−¯b1¯b2>0 | (2.2) |
and there exists a constant γ>1 such that
n∑j=1cij(k)>γai(k)fork=1,2,…,ωand1≤i≤2. | (2.3) |
Then, every positive ω-periodic solution x=(x1,x2)T of (2.1) is bounded. Specifically,
A1<x1(k)≤B1andA2<x2(k)≤B2fork=1,2,…,ω, |
where
A1≤min{lnγ¯γ1,γB1e−¯γ1B1}andB1=a_2(a_1a_2−¯b1¯b2)e(n∑j=1¯c1jγ_1j+¯b1a_2n∑j=1¯c2jγ_2j), |
A2≤min{lnγ¯γ2,γB2e−¯γ2B2}andB2=a_1(a_1a_2−¯b1¯b2)e(n∑j=1¯c2jγ_2j+¯b2a_1n∑j=1¯c1jγ_1j), |
in which γ_1j=min1≤k≤ωγ1j(k), γ_2j=min1≤k≤ωγ2j(k), ¯c1j=max1≤k≤ωc1j(k), ¯c2j=max1≤k≤ωc2j(k), ¯γ1=max1≤j≤n{max1≤k≤ωγ1j(k)} and ¯γ2=max1≤j≤n{max1≤k≤ωγ2j(k)}.
Remark 1. Note that Ai and Bi are the lower bound and upper bound of xi, respectively. We can verify the fact that Ai<Bi for i=1,2. From the definitions of A1 and A2, we see that
A1≤γB1e−¯γ1B1≤γe¯γ1andA2≤γB2e−¯γ2B2≤γe¯γ2. |
Hence, we obtain
B1>a_2(a_1a_2−¯b1¯b2)en∑j=1¯c1jγ_1j=1/(1−¯b1¯b2a_1a_2)×1a_1en∑j=1¯c1jγ_1j>∑nj=1¯c1ja_11e¯γ1>γe¯γ1≥A1. |
Similarly, it follows that
B2>a_1(a_1a_2−¯b1¯b2)en∑j=1¯c2jγ_2j>γe¯γ2≥A2. |
Proof. Let x=(x1,x2)T be arbitrary positive ω-periodic solution of (2.1) under the initial condition (1.2). For i=1,2, we define
¯xi=max1≤k≤ωxi(k)andx_i=min1≤k≤ωxi(k). |
Then x_i≤xi(k)≤¯xi for k∈Z+. We can rewrite system (2.1) into
{x1(k+1)=(1−λa1(k))x1(k)+λb1(k)x2(k)+λ∑nj=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k)), x2(k+1)=(1−λa2(k))x2(k)+λb2(k)x1(k)+λ∑nj=1c2j(k)x2(k−τ2j(k))e−γ2j(k)x2(k−τ2j(k)). | (2.4) |
Taking the maximum on both sides of the first equation of (2.4) in one period, we have
¯x1=max1≤k≤ω{x1(k+1)}≤max1≤k≤ω{(1−λa1(k))x1(k)}+λmax1≤k≤ω{b1(k)x2(k)}+λmax1≤k≤ω{n∑j=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k))}≤max1≤k≤ω{(1−λa1(k))}max1≤k≤ω{x1(k)}+λmax1≤k≤ω{b1(k)}max1≤k≤ω{x2(k)}+λmax1≤k≤ω{n∑j=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k))}≤(1−λa_1)¯x1+λ¯b1¯x2+λmax1≤k≤ω{n∑j=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k))}. |
Similarly, we obtain
¯x2≤(1−λa_2)¯x2+λ¯b2¯x1+λmax1≤k≤ω{n∑j=1c2j(k)x2(k−τ2j(k))e−γ2j(k)x2(k−τ2j(k))}. |
Hence, it leads to
¯x1≤¯b1a_1¯x2+1a_1max1≤k≤ω{n∑j=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k))}≤¯b1a_1¯x2+1a_1en∑j=1¯c1jγ_1j, | (2.5) |
and
¯x2≤¯b2a_2¯x1+1a_2max1≤k≤ω{n∑j=1c2j(k)x2(k−τ2j(k))e−γ2j(k)x2(k−τ2j(k))}≤¯b1a_2¯x1+1a_2en∑j=1¯c2jγ_2j. | (2.6) |
By (2.5) and (2.6), basic computations show that
¯x1≤1/(1−¯b1¯b2a_1a_2)×(1a_1en∑j=1¯c1jr_1j+¯b1a_1a_2en∑j=1¯c2jγ_2j)=a_2(a_1a_2−¯b1¯b2)e(n∑j=1¯c1jγ_1j+¯b1a_2n∑j=1¯c2jγ_2j)=B1, |
¯x2≤1/(1−¯b1¯b2a_1a_2)×(1a_2en∑j=1¯c2jr_2j+¯b2a_1a_2en∑j=1¯c1jγ_1j)=a_1(a_1a_2−¯b1¯b2)e(n∑j=1¯c2jγ_2j+¯b2a_1n∑j=1¯c1jγ_1j)=B2. |
Note that 1−λai(k)>0 for all k∈Z and i=1,2. Multiplying both sides of the two equation of (2.1) by ∏kr=01/(1−λa1(r)) and ∏kr=01/(1−λa2(r)) respectively, we have
x1(k+1)k∏r=011−λa1(r)−x1(k)k−1∏r=011−λa1(r)−λb1(k)x2(k)k∏r=011−λa1(r)=λn∑j=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k))k∏r=011−λa1(r), | (2.7) |
and
x2(k+1)k∏r=011−λa2(r)−x2(k)k−1∏r=011−λa2(r)−λb2(k)x1(k)k∏r=011−λa2(r)=λn∑j=1c2j(k)x2(k−τ2j(k))e−γ2j(k)x2(k−τ2j(k))k∏r=011−λa2(r). | (2.8) |
Choosing natural numbers k1 and k2 such that
¯τ1≤k1≤¯τ1+ω−1andx1(k1)=x_1, |
¯τ2≤k2≤¯τ2+ω−1andx2(k2)=x_2. |
Summing both sides of (2.7) and (2.8) over k ranging from k1 to k1+ω−1 and k2 to k2+ω−1 respectively, by using xi(ki+ω)=xi(ki)=x_i, we obtain
x_1k1−1∏r=011−λa1(r)(k1+ω−1∏r=k111−λa1(r)−1) =λk1+ω−1∑s=k1((b1(s)x2(s)+n∑j=1c1j(s)x1(s−τ1j(s))e−γ1j(s)x1(s−τ1j(s)))s∏r=011−λa1(r)), |
and
x_2k2−1∏r=011−λa2(r)(k2+ω−1∏r=k211−λa2(r)−1) =λk2+ω−1∑s=k2((b2(s)x1(s)+n∑j=1c2j(s)x2(s−τ2j(s))e−γ2j(s)x2(s−τ2j(s)))s∏r=011−λa2(r)). |
Note that ai (i=1,2) is positive ω-periodic. It follws that
ki+ω−1∏r=ki(1−λai(r))=ω−1∏r=0(1−λai(r)). | (2.9) |
Hence, we obtain
x_1=λ∏k1+ω−1r=0(1−λa1(r))1−∏ω−1r=0(1−λa1(r))(k1+ω−1∑s=k1(b1(s)x2(s)+n∑j=1c1j(s)x1(s−τ1j(s))e−γ1j(s)x1(s−τ1j(s)))s∏r=011−λa1(r))=λ1−∏ω−1r=0(1−λa1(r))k1+ω−1∑s=k1((b1(s)x2(s)+n∑j=1c1j(s)x1(s−τ1j(s))e−γ1j(s)x1(s−τ1j(s)))k1+ω−1∏r=s+1(1−λa1(r))), | (2.10) |
and
x_2=λ1−∏ω−1r=0(1−λa2(r))k2+ω−1∑s=k2((b2(s)x1(s)+n∑j=1c2j(s)x1(s−τ2j(s))e−γ2j(s)x1(s−τ2j(s)))k1+ω−1∏r=s+1(1−λa2(r))). | (2.11) |
Recall that ¯γi=max1≤j≤n{max1≤k≤ω−1γij(k)} for i=1,2. We define f1(u)=ue−¯γ1u and f2(u)=ue−¯γ2u for u≥0. Since x_i≤xi(k)≤¯xi for all k∈Z+, it turns out that
xi(s−τij(s))e−γij(s)xi(s−τij(s))≥min{fi(x_i),fi(¯xi)}fors≥¯τijfori=1,2. |
Note that k1≥¯τ1. By using (2.3) and (2.10), we have
x_1≥λmin{f1(x_1),f1(¯x1)}1−∏ω−1r=0(1−λa1(r))k1+ω−1∑s=k1(n∑j=1c1j(s)k1+ω−1∏r=s+1(1−λa1(r)))>λmin{f1(x_1),f1(¯x1)}1−∏ω−1r=0(1−λa1(r))k1+ω−1∑s=k1(γa1(s)k1+ω−1∏r=s+1(1−λa1(r)))=γmin{f1(x_1),f1(¯x1)}1−∏ω−1r=0(1−λa1(r))k1+ω−1∑s=k1(λa1(s)k1+ω−1∏r=s+1(1−λa1(r)))=γmin{f1(x_1),f1(¯x1)}1−∏ω−1r=0(1−λa1(r))k1+ω−1∑s=k1((1−(1−λa1(s)))k1+ω−1∏r=s+1(1−λa1(r)))=γmin{f1(x_1),f1(¯x1)}1−∏ω−1r=0(1−λa1(r))k1+ω−1∑s=k1(k1+ω−1∏r=s+1(1−λa1(r))−k1+ω−1∏r=s(1−λa1(r)))=γmin{f1(x_1),f1(¯x1)}1−∏ω−1r=0(1−λa1(r))(k1+ω−1∏r=k1+ω(1−λa1(r))−k1+ω−1∏r=k1(1−λa1(r))). |
Calculating by the same way, from (2.3) and (2.11), we obtain
x_2=γmin{f2(x_2),f2(¯x2)}1−∏ω−1r=0(1−λa2(r))(k2+ω−1∏r=k2+ω(1−λa2(r))−k2+ω−1∏r=k2(1−λa2(r))). |
Then, it follows from (2.9) that
x_i>γmin{fi(x_i),fi(¯xi)}fori=1,2. | (2.12) |
It is natural to divide the argument into two cases: (ⅰ) fi(x_i)≤fi(¯xi); (ⅱ) fi(x_i)>fi(¯xi).
Case (ⅰ): It follows from (2.12) that x_i>γfi(x_i). Specifically, we have
x_1>γf1(x_1)=γx_1e¯γ1x_1andx_2>γf2(x_2)=γx_2e¯γ2x_2, |
which imply that x_1>lnγ/¯γ1 and x_2>lnγ/¯γ2.
Case (ⅱ): Function fi is unimodal and takes the only peak value at 1/¯γi. Also, fi monotonically increases on [0,1/¯γi] and monotonically decreases on [1/¯γi,∞). If ¯xi≤1/1/¯γi, then we see that fi(x_i)≤fi(¯xi)≤fi(1/¯γi), which is a contradiction. Hence, it follows that ¯xi>1/¯γi. Note that ¯xi≤Bi. From (2.12), we obtain
x_1>γf1(¯x1)≥γf1(B1)=γB1e−¯γ1B1 |
and
x_2>γf2(¯x2)≥γf2(B2)=γB2e−¯γ2B2. |
Thus, we estimate
x_1>min{lnγ¯γ1,γB1e−¯γ1B11}≥A1 |
and
x_2>min{lnγ¯γ2,γB2e−¯γ2B22}≥A2. |
Now, it can be concluded that each positive ω-periodic solution x=(x1,x2)T of (2.1) satisfies
A1<x_1≤x1(k)≤¯x1≤B1 |
and
A2<x_2≤x2(k)≤¯x1≤B2 |
for k∈Z+. The proof is complete.
Suppose that X is a Banach space and L:Dom L⊂X→X is a linear operator. The operator L is called a Fredholm operator of index zero if
(i) dim Ker L=codim Im L<+∞,
(ii) Im L is closed in X.
If L is a Fredholm operator of index zero and P, Q:X→X are continuous projectors satisfying
Im P=Ker LandKer Q=Im L=Im (I−Q), |
where I is the identity operator from X to X, then the restriction LP:Dom L∩Ker P→Im L is invertible and has the inverse KP:Im L→Dom L∩Ker P.
Let N:X→X be a continuous operator and Ω an open bounded subset of X. The operator N is L-compact on ¯Ω if
(i) QN(¯Ω) is bounded,
(ii) KP(I−Q)N:¯Ω→X is compact.
We present the continuation theorem of coincidence degree theory (for example, see [29,30]) as follows:
Lemma 2.2. Let L:Dom L⊂X→X be a Fredholm operator of index zero and let N:X→X be L-compact on ¯Ω. Suppose that
(i) every solution x of Lx=λNx satisfies x∉∂Ω for λ∈(0,1);
(ii) QNx≠0 for x∈∂Ω∩Ker L and
deg{QN,Ω∩Ker L,0}≠0. |
Then, Lx=Nx has at least one solution in X∩¯Ω.
Theorem 3.1. Suppose that (2.2) and (2.3) hold. If
∑ωk=1∑nj=1(cij(k)∑ωk=1(ai(k)−bi(k))>1fori=1,2, | (3.1) |
then system (1.1) has at least one positive ω-periodic solution x∗.
Proof. Let X be a set of ω-periodic functions x=(x1,x2)T defined on Z+ and denote the maximum norm ||x||=max{max1≤k≤ω|x1(k)|,max1≤k≤ω|x2(k)|} for any x∈X. Then, X is a Banach space. Moreover, we define
Lx=((Lx)1(k)(Lx)2(k))=(x1(k+1)−x1(k)x2(k+1)−x2(k)), |
and
Nx=((Nx)1(k)(Nx)2(k))=(− a1(k)x1(k)+b1(k)x2(k)+∑nj=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k))− a2(k)x2(k)+b2(k)x1(k)+∑nj=1c2j(k)x2(k−τ2j(k))e−γ2j(k)x2(k−τ2j(k))). |
It is not difficult to show that L is a linear operator from X to X and N is a continuous operator from X to X.
From the definition of L, we see that
Ker L={x∈X:(x1(k),x2(k))T≡(c1,c2)T∈R2}and Im L={x∈X:ω∑k=1x1(k)=ω∑k=1x2(k)=0}. |
It turns out that dim Ker L=2=codim Im L<+∞ and Im L is closed in X. Thus, L is a Fredholm operator of index zero.
We define P:X→X by
Px=((Px)1(Px)2)=(1ωω∑k=1x1(k)1ωω∑k=1x2(k)) |
and let Q=P. Then, P and Q are two continuous projectors such that Im P=Ker L and Ker Q=Im L=Im (I−Q).
It can be shown that the restriction LP:Dom L∩Ker P→Im L has the inverse KP:Im L→Dom L∩Ker P given by
KPx=((KPx)1(KPx)2)=(k−1∑s=0x1(s)−1ωω−1∑s=0s∑r=0x1(r)k−1∑s=0x2(s)−1ωω−1∑s=0s∑r=0x2(r)) |
for x=(x1,x2)T∈Im L. In fact, for i=1,2, since
(KPx)i(k+ω)−(KPx)i(k)=k+ω−1∑s=0xi(s)−1ωω−1∑s=0s∑r=0xi(r)−k−1∑s=0xi(s)+1ωω−1∑s=0s∑r=0xi(r)=k+ω−1∑s=kxi(s)=ω−1∑s=0xi(s)=0 |
for all k∈Z+, we see that KPx∈Dom L. Moreover, it follows that
(PKPx)i=1ωω∑k=1KPxi(k)=1ωω∑k=1(k−1∑s=0xi(s)−1ωω−1∑s=0s∑r=0xi(r))=1ω(ω∑k=1k−1∑s=0xi(s)−ωωω−1∑s=0s∑r=0xi(r))=1ω(ω∑k=1k−1∑s=0xi(s)−ω∑k=1k−1∑r=0xi(r))=0. |
Hence, KPx∈Ker P.
For any x∈Im L, one has
(LPKPx)i=(KPx)i(k+1)−(KPx)i(k)=k∑s=0xi(s)−1ωω−1∑s=0s∑r=0xi(r)−k−1∑s=0xi(s)+1ωω−1∑s=0s∑r=0xi(r)=xi(k)=(Ix)i. |
Furthermore, for any x∈Dom L∩Ker P, one has
(KPLPx)i=KP(xi(k+1)−xi(k))=k−1∑s=0(xi(s+1)−xi(s))−1ωω−1∑s=0s∑r=0(xi(r+1)−xi(r))=xi(k)−xi(0)−1ωω−1∑s=0(xi(s+1)−xi(0))=xi(k)−1ωω∑s=1xi(s). |
Since x∈Ker P=Ker Q=Im L, we see that ∑ωs=1xi(s)=0. Hence, (KPLPx)i=xi(k)=(Ix)i. We therefore conclude that KP=L−1P.
We define
Ω={x=(x1,x2)T∈X:A1<x1(k)<B1+1,A2<x2(k)<B2+1} |
and prove that the operator N defined above is L-compact on ¯Ω. We first check that QN(¯Ω) is bounded.
Since x1(k)<B1+1 and x2(k)<B2+1 for k∈Z+, we obtain
(QNx)1=1ωω∑k=1(− a1(k)x1(k)+b1(k)x2(k)+n∑j=1c1j(k)x1(k−τ1j(k))e−γ1j(k)x1(k−τ1j(k)))<1ωω∑k=1(¯b1(B2+1)+1en∑j=1¯c1jγ_1j)=(¯b1(B2+1)+1en∑j=1¯c1jγ_1j), |
and
(QNx)2=1ωω∑k=1(− a2(k)x2(k)+b2(k)x1(k)+n∑j=1c2j(k)x2(k−τ2j(k))e−γ2j(k)x2(k−τ2j(k)))<1ωω∑k=1(¯b2(B1+1)+1en∑j=1¯c2jγ_2j)=(¯b2(B1+1)+1en∑j=1¯c2jγ_2j) |
for x∈¯Ω. Hence, the operator QN is bounded on ¯Ω.
We next show that KP(I−Q)N:¯Ω→X is compact. From the definitions of N, QN and Kp, we obtain
(Kp(I−Q)Nx)1=k−1∑s=0(− a1(s)x1(s)+b1(s)x2(s))+k−1∑s=0(n∑j=1c1j(s)x1(s−τ1j(s))e−γ1j(s)x1(s−τ1j(s)))−(kω−ω+12ω)ω∑s=1(− a1(s)x1(s)+b1(s)x2(s))−(kω−ω+12ω)ω∑s=1(n∑j=1c1j(s)x1(s−τ1j(s))e−γ1j(s)x1(s−τ1j(s)))−1ωω−1∑s=0s∑r=0(− a1(r)x1(r)+b1(r)x2(r))−1ωω−1∑s=0s∑r=0(n∑j=1c1j(r)x1(r−τ1j(r))e−γ1j(r)x1(r−τ1j(r))). |
Meanwhile, we have
(Kp(I−Q)Nx)2=k−1∑s=0(− a2(s)x2(s)+b2(s)x1(s))+k−1∑s=0(n∑j=1c2j(s)x2(s−τ2j(s))e−γ2j(s)x2(s−τ2j(s)))−(kω−ω+12ω)ω∑s=1(− a2(s)x2(s)+b2(s)x1(s))−(kω−ω+12ω)ω∑s=1(n∑j=1c2j(s)x2(s−τ2j(s))e−γ2j(s)x2(s−τ2j(s)))−1ωω−1∑s=0s∑r=0(− a2(r)x2(r)+b2(r)x1(r))−1ωω−1∑s=0s∑r=0(n∑j=1c2j(r)x2(r−τ2j(r))e−γ2j(r)x2(r−τ2j(r))) |
for x∈X. For any bounded subset E⊂¯Ω⊂X, it is a subspace of a finite dimensional Banach space X. Hence, E is closed, and therefore E is compact. By a straightforward calculation, it can be proven that KP(I−Q)N(E) is relatively compact.
An arbitrary ω-periodic solution of (2.1) corresponds one-to-one to a solution of Lx=λNx with parameter λ∈(0,1). Proposition 2.1 displays that each positive solution x=(x1,x2)T of Lx=λNx satisfies that A1<x1≤B1 and A2<x2≤B2. It is obvious that if y=(y1,y2)T∈∂Ω, then y is never a solution of Lx=λNx. Hence, the condition (i) of Lemma 2.2 holds. If x=(x1,x2)T∈∂Ω∩Ker L, then there are four cases to be considered: (1) x=(A1,x2)T, (2) x=(B1+1,x2)T, (3) x=(x1,A2)T, (4) x=(x1,B2+1)T.
Case (1): It follows from x1≡A1 that
(QNx)1=1ωω∑k=1(−A1a1(k)+b1(k)x2(k)+n∑j=1cij(k)A1e−γ1j(k)A1)≥A1ωω∑k=1(−a1(k)+1eA1¯γ1n∑j=1cij(k))>A1ωω∑k=1(−a1(k)+γeA1¯γ1a1(k))=A1ω(γeA1¯γ1−1)ω∑k=1a1(k). |
Since A1≤lnγ/¯γ1, we see that eA1¯γ1≤γ. Hence, (QNx)1>0.
Case (2): Because of x1≡B1+1, we have
(QNx)1=1ωω∑k=1(−(B1+1)a1(k)+b1(k)x2(k)+n∑j=1cij(k)(B1+1)e−γ1j(k)(B1+1))≤1ωω∑k=1(−a_1(B1+1)+¯b1B2+n∑j=1¯c1jeγ_1j)=−a_1(B1+1)+¯b1B2+1en∑j=1¯c1jγ_1j=−a_1−a_1a_2(a_1a_2−¯b1¯b2)e(n∑j=1¯c1jγ_1j+¯b1a_2n∑j=1¯c2jγ_2j)+a_1¯b1(a_1a_2−¯b1¯b2)e(n∑j=1¯c2jγ_2j+¯b2a_1n∑j=1¯c1jγ_1j)+1en∑j=1¯c1jγ_1j=−a_1<0. |
Similarly, we can show that (QNx)2>0 in Case (3) and (QNx)2<0 in Case (4). We therefore conclude that QNx=((QNx)1,(QNx)2)T≠0 for each x∈∂Ω∩Ker L.
Define a continuous operator H:Ω∩Ker L×[0,1]→X by
H(x,μ)=(H1(x,μ)H2(x,μ))=(−μ(Ix1−A1+B12)+(1−μ)(QNx)1−μ(Ix2−A2+B22)+(1−μ)(QNx)2). |
Recall that the elements of ∂Ω∩Ker L are vectors satisfying x=(A1,x2)T, y=(B1+1,y2)T, z=(z1,A2)T and w=(w1,B2+1)T. For x=(A1,x2)T, we can check that
H1(x,μ)=−μ(A1−A1+B12)+(1−μ)(QNx)1=−μ(A1−B12)+(1−μ)(QNx)1>0. |
Moreover,
H1(y,μ)=−μ(B1+1−A1+B12)+(1−μ)(QNy)1=−μ(A1−B1+22)+(1−μ)(QNy)1<0 |
for y=(B1+1,y2)T. Hence, H(x,μ)≠0 and H(y,μ)≠0. By similar computations, we have H(z,μ)≠0 and H(w,μ)≠0. Therefore, we see that H(x,μ)≠0 for (x,μ)∈∂Ω∩Ker L×[0,1]. Thus, H is a homotopic mapping. Using the homotopy invariance, we have
deg{QN,Ω∩Ker L,0}=deg{(−Ix1+A1+B12−Ix2+A2+B22),Ω∩Ker L,0}=1≠0. |
Hence, the condition (ⅱ) of Lemma 2.2 holds. Therefore, the equation Lx=Nx has at least one solution located in X∩¯Ω. Thus, from Lemma 2.2, we obtain that there is a positive ω-periodic solution of system (1.1). The proof is now complete.
Consider the delay difference system
{Δx1(k)=−a1(k)x1(k)+b1(k)x2(k)+c11(k)x1(k−1)e−γ11(k)x1(k−1)+c12(k)x1(k−1)e−γ12(k)x1(k−1),Δx2(k)=−a2(k)x2(k)+b2(k)x1(k)+c21(k)x2(k−4)e−γ21(k)x2(k−4)+c22(k)x2(k−4)e−γ22(k)x2(k−4). |
Here, we assume that
a1(k)={1/2ifk=1,2/5ifk=2,1/4ifk=3,1/5ifk=4,a2(k)={3/4ifk=1,3/5ifk=2,1/2ifk=3,5/6ifk=4, |
b1(k)={1/5ifk=1,1/4ifk=2,1/7ifk=3,1/6ifk=4,b2(k)={1/20ifk=1,1/12ifk=2,1/24ifk=3,1/18ifk=4, |
c11(k)={1/2ifk=1,3/4ifk=2,1/3ifk=3,2/3ifk=4,c12(k)={5/6ifk=1,4/5ifk=2,2/5ifk=3,1/6ifk=4,c21(k)={7/8ifk=1,4/5ifk=2,2/3ifk=3,6/7ifk=4,c22(k)={1/4ifk=1,1/2ifk=2,1/10ifk=3,20/21ifk=4, |
γ11(k)={3ifk=1,1ifk=2,1.5ifk=3,2ifk=4,γ12(k)={10ifk=1,4ifk=2,3ifk=3,5ifk=4,γ21(k)={5ifk=1,2ifk=2,1ifk=3,2.5ifk=4,γ22(k)={2ifk=1,1.5ifk=2,8ifk=3,3ifk=4. |
In addition, ai(k)=ai(k+4), bi(k)=bi(k+4), cij(k)=cij(k+4) and γij(k)=γij(k+4) for k∈Z, i=1,2 and j=1,2. Theorem 3.1 shows that the system has at least one positive 4-periodic solution.
It is clear that ω=4, ai, bi, cij, γij and τij (1≤i≤2,1≤j≤2) are ω-periodic discrete functions satisfying 0<ai(k)<1, 0<bi(k)<1, cij(k)>0 and γij(k)>0 for k∈Z+. Since a_1=1/5, a_1=1/2, ¯b1=1/4 and ¯b2=1/12, we see that
a_1a_1−¯b1¯b2=15×12−14×112=19240>0. |
Hence, condition (2.2) is satisfied. Let γ=11/10>1. Then, we can easily check condition (2.3)
(c11(k)+c12(k))>γa1(k)and(c21(k)+c22(k))>γa2(k) |
for k=1,2,3,4. Moreover, it can be calculated that
4∑k=1(c11(k)+c12(k))4∑k=1(a1(k)−b1(k))=1869248>1and4∑k=1(c21(k)+c22(k))4∑k=1(a2(k)−b2(k))=221106181>1. |
Namely, condition (3.1) holds. Therefore, from Theorem 3.1, it turns out that the system has at least one positive 4-periodic solution.
A discrete Nicholson system that describles the dynamics of two fly species is studied in this paper. The system considers the mutualism effect between fly species. Continuation theorem of coincidence degree theory is used effectively to seek sufficient conditions for the existence of a positive periodic solution. It is easy to check whether these sufficient conditions hold or not by using coefficients. The positive periodic solution indicates a cycle change in the adult fly populations. From the obtained result, we found that mutualistic interactions between species plays an important role in adult flies populations. But the increase in the flies populations resulting from maximum cumulative mutualism effect only should be less than the death of the flies populations because there is the natural generation of flies populations. Moreover, to avoid species extinction and maintain the coexistence of two fly species in a mutually beneficial environment, we see that (ⅰ) the adult fly population produced by maximum daily spawning should exceed a constant multiple of dead fly population for each fly species, and the multiple is greater than constant 1 and (ⅱ) the total population growth must be maintained more than the population loss for each fly species. In fact, the third sufficient condition (3.1) of Theorem 3.1 can be rewritten into the form
ω∑k=1(n∑j=1(c1j(k)+b1(k))>ω∑k=1a1(k)andω∑k=1(n∑j=1(c2j(k)+b2(k))>ω∑k=1a2(k). |
The left side of each inequality represents the production of one fly species in a period under the mutualism influence of another, and the right side represents the death of that species in a period. Hence, statement (ⅱ).
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
The paper is supported by College Students Innovations Special Project funded by Northeast Forestry University of China (Grant No. 202210225156) and Fundamental Research Funds for the Central Universities of China (Grant No. 41422003).
The authors declare that there is no conflicts of interest.
[1] | Adrian T, Shin HS (2005) Liquidity and leverage. Available from: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr328.pdf. |
[2] |
Adrian T, Kiff J, Hyun SS (2018) Liquidity, Leverage and Regulation 10 Years after the Global Financial Crisis. Annual Rev Financ Econ 10: 1–24. https://doi.org/10.1146/annurev.financial-110217-023113 doi: 10.1146/annurev-financial-110217-023113
![]() |
[3] | Anderson D (2014) Leveraging: A Political, Economic and Societal Framework, Springer: Berlin, Heidelberg. |
[4] | Bank for International Settlements (2021) CBDC: an Opportunity for the Monetary System. Available from: https://www.bis.org/publ/arpdf/ar2021e3.pdf. |
[5] | Black F (1976) Studies of Stock Price Volatility Changes. Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economic Statistics Section, 177–181. |
[6] |
Blumberg A (1968) Logistic growth rate functions. J Theor Biol 21: 42–44 doi: 10.1016/0022-5193(68)90058-1
![]() |
[7] | Christie A (1982) The Stochastic Behavior of Common Stock Variances: Value, Leverage and Interest Rate Effects. J Financ Econ 3: 407–432. |
[8] |
Dennis B, Desharnais RJ, Cushing M, et al. (2003) Can Noise Induce Chaos? OIKOS 102: 329–339. https://doi.org/10.1034/j.1600-0706.2003.12387.x doi: 10.1034/j.1600-0706.2003.12387.x
![]() |
[9] | Financial Times (2020) US Treasuries: the lessons from March's market meltdown. Available from: https://www.ft.com/content/ea6f3104-eeec-466a-a082-76ae78d430fd. |
[10] | Gardiner CW (1997) Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences, 2nd Edition, Berlin/Heidelberg: Springer. |
[11] | Geanakoplos J (1999) Promises, Promises, In: Brian Arthur, W., Durlauf, S., Lane, D., The Economy as an Evolving Complex System Ⅱ, Reading, MA: Addison-Wesley. |
[12] | Grey R (2020–2021) Administering Money: Coinage, Debt Crises, and the Future of Fiscal Policy. Kentucky Law J 109: 230–298. Available from: https://heinonline.org/HOL/LandingPage?handle = hein.journals/kentlj109 & div = 12 & id = & page = . |
[13] | Hasanholzic J, Lo A (2011) Black's leverage effect is not due to leverage. Available from: https://ssrn.com/abstract = 1762363. |
[14] | Holmstrom B (2015) Understanding of the Role of Debt in the Financial System. Available from: https://www.bis.org/publ/work479.htm. |
[15] | Institute of International Finance (2021) Global Debt Monitor, April. Available from: https://www.iif.com/publications/global-debt-monitor. |
[16] |
Kozlowski J, Veldkampf L, Venkateswaran V (2015) The tail that wags the economy: belief-driven business cycle and persistent stagnation. Natl Bur Econ Res. https://doi.org/10.3386/w21719 doi: 10.3386/w21719
![]() |
[17] |
Mao X, Marion G, Renshaw E (2001) Environmental Brownian noise suppresses explosions in population dynamics. Stoch Proc Appl 97: 95–110. https://doi.org/10.1016/S0304-4149(01)00126-0 doi: 10.1016/S0304-4149(01)00126-0
![]() |
[18] |
Petroni C, De Martino S, De Siena S (2020) Logistic and logistic models in population dynamics: general analysis and exact results. J Phys A: Math Theor 53: 445005. https://doi.org/10.1088/1751-8121/abb277 doi: 10.1088/1751-8121/abb277
![]() |
[19] | Pasquali S (2001) The Stochastic Logistic Equation: Stationary Solutions and their Stability, Rendisconti del Seminario Matematico della Universita di Padova. 106: 165–183. |
[20] |
Richards F (1959) A flexible growth function for empirical use. J Exp Bot 10: 290–300. doi: 10.1093/jxb/10.2.290
![]() |
[21] | Rodriguez G (2012) Generalized Linear Models, Lecture Notes. Available from: https://data.princeton.edu > wws509 > notes. |
[22] | Schrimp A, Hyun SS, Sushko V (2020) Leverage and margin spirals in fixed income markets during the Covid–19 crisis. BIS Bulletin. Available from: https://www.bis.org/publ/bisbull02.pdf. |
[23] |
Skiadas C (2010) Exact Solutions of Stochastic Differential Equations: Gompertz, Generalized Logistic and Revised Exponential. Methodol Comput Appl 12: 261–27. https://doi.org/10.1007/s11009-009-9145-3 doi: 10.1007/s11009-009-9145-3
![]() |
[24] |
Smirnov AD (2018) Stochastic Logistic Model of the Global Financial Leverage. Be J Theor Econ 18: 20160009. https://doi.org/10.1515/bejte-2016-0009 doi: 10.1515/bejte-2016-0009
![]() |
[25] | Sornette D (2006) Critical Phenomena in Natural Sciences: Chaos, Fractals, Self-Organization and Disorder, Springer: Berlin/Heidelberg. |
[26] |
Tabatabai M, Eby W, Buzsac Z (2012) Oscillobolastic model, a new model for oscillatory dynamics. J Biomed Inf 45: 401–407. https://doi.org/10.1016/j.jbi.2011.11.016 doi: 10.1016/j.jbi.2011.11.016
![]() |
[27] | Tsoularis R (2001) Analysis of logistic growth models. Res Lett Inf Math Sci 2: 23–46. |
[28] | Walck C (1996) Handbook on Statistical Distributions for Experimentalists, University of Stockholm. |
1. | Jennifer L. Lemacks, Tammy Greer, Sermin Aras, Shantoni Holbrook, June Gipson, Multiphase optimization strategy to establish optimal delivery of nutrition-related services in healthcare settings: A step towards clinical trial, 2024, 146, 15517144, 107683, 10.1016/j.cct.2024.107683 |