Research article

Analytic approximations for European-style Asian spread options

  • Spread option is a exotic option, which allows investors to simultaneously take positions in two correlated underlying assets and profit from their price difference over some spread. This option provides stable investment opportunities for practitioners in unpredictable and complex financial markets. However, investors of the spread option may face problems caused by manipulating the two underlying assets' prices near the expiry, compared to plain vanilla options. To overcome such disadvantages, we propose Asian-spread options, which are linked to the price difference between two average prices of two underlying assets over the life of the option, and exhibit the original properties of standard spread options. In this paper, using distribution-approximating and moment-matching approaches, lower bounds of prices for the European spread option on the geometric average Asian option and arithmetic average Asian option are obtained in the classical Black-Scholes model. We verified the pricing accuracy of the proposed Asian-spread options by comparing our solutions with those obtained by Monte Carlo simulations. Finally, we analyzed the influence of stock price, maturity date, and some model parameters on option price and delta value through numerical examples. Numerical results showed that the lower bounds had a very high precision.

    Citation: Boling Chen, Guohe Deng. Analytic approximations for European-style Asian spread options[J]. AIMS Mathematics, 2024, 9(5): 11696-11717. doi: 10.3934/math.2024573

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  • Spread option is a exotic option, which allows investors to simultaneously take positions in two correlated underlying assets and profit from their price difference over some spread. This option provides stable investment opportunities for practitioners in unpredictable and complex financial markets. However, investors of the spread option may face problems caused by manipulating the two underlying assets' prices near the expiry, compared to plain vanilla options. To overcome such disadvantages, we propose Asian-spread options, which are linked to the price difference between two average prices of two underlying assets over the life of the option, and exhibit the original properties of standard spread options. In this paper, using distribution-approximating and moment-matching approaches, lower bounds of prices for the European spread option on the geometric average Asian option and arithmetic average Asian option are obtained in the classical Black-Scholes model. We verified the pricing accuracy of the proposed Asian-spread options by comparing our solutions with those obtained by Monte Carlo simulations. Finally, we analyzed the influence of stock price, maturity date, and some model parameters on option price and delta value through numerical examples. Numerical results showed that the lower bounds had a very high precision.



    We can identify two types of graph invariants which are currently being studied in chemical graph theory. Namely,

    XΣ(G)=XΣ,FV(G)=uV(G)FV(du)orXΣ(G)=XΣ,FE(G)=uvE(G)FE(du,dv) (1.1)

    and

    XΠ(G)=XΠ,FV(G)=uV(G)FV(du)orXΠ(G)=XΠ,FE(G)=uvE(G)FE(du,dv). (1.2)

    Here, uv denotes the edge of the graph G=(V(G),E(G)) connecting the vertices u and v, du is the degree of the vertex u, and FV(x) and FE(x,y) are appropriately defined functions (see e.g., [1]). Since XΣ(G) and XΠ(G) are usually known as topological indices in the literature, here we will refer to XΠ(G) as multiplicative topological indices (MTIs) to make a clear difference between both types of indices.

    Among the vast amount of topological indices of the form XΣ(G), the first and second Zagreb indices [2] stand out, and they are defined as

    M1(G)=uV(G)d2u=uvE(G)(du+dv) (1.3)

    and

    M2(G)=uvE(G)dudv, (1.4)

    respectively. Also, the Randić connectivity index [3]

    R(G)=uvE(G)1dudv, (1.5)

    the harmonic index [4]

    H(G)=uvE(G)2du+dv, (1.6)

    the sum-connectivity index [5]

    χ(G)=uvE(G)1du+dv, (1.7)

    and the inverse degree index [4]

    ID(G)=uV(G)1du=uvE(G)(1d2u+1d2v). (1.8)

    The topological indices mentioned above and many others of the form XΣ(G) have been widely studied during recent decades.

    More recently, MTIs have attracted a lot of attention, see e.g., [6,7,8,9,10,11,12]. Indeed, several MTIs have already been deeply analyzed in the literature (see e.g., the work of V. R. Kulli). Among the most relevant ones, the Narumi-Katayama index [13] should be mentioned:

    NK(G)=uV(G)du. (1.9)

    Additionally, multiplicative versions of the Zagreb indices [14]

    Π1(G)=uV(G)d2u, (1.10)
    Π2(G)=uvE(G)dudv (1.11)

    and

    Π1(G)=uvE(G)(du+dv). (1.12)

    In addition to the MTIs of Eqs (1.9)–(1.12), in this work we also consider multiplicative versions of the indices in Eqs (1.5)–(1.8): the multiplicative Randić connectivity index

    RΠ(G)=uvE(G)1dudv, (1.13)

    the multiplicative harmonic index

    HΠ(G)=uvE(G)2du+dv, (1.14)

    the multiplicative sum-connectivity index

    χΠ(G)=uvE(G)1du+dv, (1.15)

    and the multiplicative inverse degree index

    IDΠ(G)=uvE(G)(1d2u+1d2v). (1.16)

    Therefore, motivated by their potential applications, in this work we perform an analytical as well as computational study of MTIs. Specifically, the purpose of this work is threefold:

    First, we follow an analytical viewpoint to find several new inequalities that relate two MTIs indices between them as well as to their additive versions in Section 2. Note that the search for inequalities between different indices is a classical topic in the study of topological indices. See e.g., [6,7,8,9,10,11,12] for previous results relating MTIs.

    Second, we want to establish the statistical analysis of MTIs as a generic tool for the study of average properties of random networks in Section 3. For some previous works dealing with the statistical analysis of (non-multiplicative) topological indices, see, e.g., [15,16,17,18,19].

    Finally, in this paper we perform for the first time (to our knowledge) a scaling study of MTIs on random networks which let us state a scaling law that relates different random graph models in Section 4.

    There are many works studying particular MTIs; see, e.g., [20,21] and the references therein. However, in this section we obtain several analytical inequalities involving the general MTIs XΠ,FV(G) and XΠ,FE(G).

    The following equalities are direct:

    XΠ,FV(G)=uV(G)FV(du)=euV(G)logFV(du)=eXΣ,logFV(G),XΠ,FE(G)=uvE(G)FE(du,dv)=euvE(G)logFE(du,dv)=eXΣ,logFE(G).

    Since the geometric mean is at most the arithmetic mean (by Jensen's inequality), we have the following inequalities.

    Proposition 2.1. Let G be a graph with n vertices and m edges. Then,

    XΠ,FV(G)1/n1nXΣ,FV(G),XΠ,FE(G)1/m1mXΣ,FE(G).

    This general result has as a particular consequence the following known inequality, see [9, Theorem 2.1].

    Corollary 2.1. Let G be a graph with n vertices and m edges. Then,

    Π1(G)(2mn)2n.

    Proof. If we take FV(du)=du, then

    XΠ,FV(G)=Π1(G)1/2,XΣ,FV(G)=2m,

    and Proposition 2.1 gives

    Π1(G)1/(2n)2mn,

    which implies the desired inequality.

    Proposition 2.1 has several consequences:

    Proposition 2.2. Let G be a graph with n vertices and m edges.

    (1) If FE(du,dv)=h(du)+h(dv) and FV(du)=duh(du) for some function h, then

    XΠ,FV(G)1/n1nXΣ,FE(G),XΠ,FE(G)1/m1mXΣ,FV(G).

    (2) If FE(du,dv)=h(du)h(dv) and FV(du)=h(du)du for some function h, then

    XΠ,FE(G)1/n1nXΣ,FV(G),XΠ,FV(G)1/m1mXΣ,FE(G).

    Proof. Let G be a graph with n vertices and m edges.

    If FE(du,dv)=h(du)+h(dv) and FV(du)=duh(du) for some function h, then

    XΣ,FE(G)=uvE(G)FE(du,dv)=uvE(G)(h(du)+h(dv))=uV(G)duh(du)=XΣ,FV(G).

    Consequently, Proposition 2.1 implies

    XΠ,FV(G)1/n1nXΣ,FV(G)=1nXΣ,FE(G),XΠ,FE(G)1/m1mXΣ,FE(G)=1mXΣ,FV(G).

    If FE(du,dv)=h(du)h(dv) and FV(du)=h(du)du for some function h, then

    XΠ,FE(G)=uvE(G)FE(du,dv)=uvE(G)h(du)h(dv)=uV(G)h(du)du=XΠ,FV(G).

    Hence, Proposition 2.1 gives

    XΠ,FE(G)1/n=XΠ,FV(G)1/n1nXΣ,FV(G),XΠ,FV(G)1/m=XΠ,FE(G)1/m1mXΣ,FE(G).

    Theorem 2.1. Let G be a graph with m edges. Assume that F1(du,dv)=h(du)+h(dv) and F2(du,dv)=h(du)h(dv) for some function h. Then,

    XΠ,F2(G)(12mXΣ,F1(G))2m.

    Proof. Since the (weighted) geometric mean is at most the (weighted) arithmetic mean (by Jensen's inequality), the following inequality holds for every xi,wi>0:

    (ni=1xwii)1/ni=1wi1ni=1wini=1xiwi.

    If we take xi=h(du) and wi=du, then we get

    (uV(G)h(du)du)1/uE(G)du1uV(G)duuV(G)duh(du),(uvE(G)h(du)h(dv))1/(2m)12muvE(G)(h(du)+h(dv)),XΠ,F2(G)(12mXΣ,F1(G))2m.

    This general result has as a particular consequence the following known inequality, see [9, Theorem 3.1].

    Corollary 2.2. Let G be a graph with m edges. Then,

    Π2(G)(12mM1(G))2m.

    Proof. If we take h(x)=x, then F1(du,dv)=du+dv, F2(du,dv)=dudv, XΠ,F2(G)=Π2(G), XΣ,F1(G)=M1(G), and Theorem 2.1 gives the desired inequality.

    In [22] appears the following Jensen-type inequality.

    Theorem 2.2. Let μ be a probability measure on the space X and ab be real constants. If f:X[a,b] is a measurable function and φ is a convex function on [a,b], then f and φf are μ-integrable functions, and

    φ(a+bXfdμ)φ(a)+φ(b)Xφfdμ.

    Theorem 2.2 allows one to find the following converse of Proposition 2.1, if we consider the normalized counting measure as μ.

    Theorem 2.3. Let G be a graph with n vertices and m edges, aVbV, aEbE be real constants, and FV, FE be functions satisfying

    aVFV(du)bVuV(G),aEFE(du,dv)bEuvE(G).

    Then,

    1nXΣ,FV(G)eaV+ebVeaV+bVXΠ,FV(G)1/n,1mXΣ,FE(G)eaE+ebEeaE+bEXΠ,FE(G)1/m.

    Proof. Since φ(x)=ex is a convex function on R, Theorem 2.2 gives

    eaV+bV1nuV(G)logFV(du)eaV+ebV1nuV(G)elogFV(du),eaV+bVelog(ΠuV(G)FV(du))1/neaV+ebV1nuV(G)FV(du),eaV+bVXΠ,FV(G)1/neaV+ebV1nXΣ,FV(G).

    In a similar way,

    eaE+bE1muvE(G)logFE(du,dv)eaE+ebE1muvE(G)elogFE(du,dv),eaE+bEelog(ΠuvE(G)FE(du,dv))1/meaE+ebE1muvE(G)FE(du,dv),eaE+bEXΠ,FE(G)1/meaE+ebE1mXΣ,FE(G).

    Theorem 2.3 provides the following new lower bounds of Π1 and Π1.

    Corollary 2.3. Let G be a graph with n vertices and m edges, minimum degree δ, and maximum degree Δ. Then,

    Π1(G)(eδ2+eΔ2eδ2Δ2nM1(G))n,Π1(G)(e2δ+e2Δe2δ2ΔmM1(G))m.

    Proof. Since δ2d2uΔ2 and 2δdu+dv2Δ, Theorem 2.3 implies

    1nM1(G)eδ2+eΔ2eδ2+Δ2Π1(G)1/n,Π1(G)(eδ2+eΔ2eδ2Δ2nM1(G))n,1mM1(G)e2δ+e2Δe2δ+2ΔΠ1(G)1/m,Π1(G)(e2δ+e2Δe2δ2ΔmM1(G))m.

    The following Kober's inequalities in [23] (see also [24, Lemma 1]) are useful.

    Lemma 2.1. If aj>0 for 1jk, then

    kj=1aj+k(k1)(kj=1aj)1/k(kj=1aj)2(k1)kj=1aj+k(kj=1aj)1/k.

    Lemma 2.1 has the following consequence.

    Theorem 2.4. Let G be a graph with n vertices and m edges. Then,

    XΣ,F2V(G)+n(n1)XΠ,FV(G)2/nXΣ,FV(G)2(n1)XΣ,F2V(G)+nXΠ,FV(G)2/n,XΣ,F2E(G)+m(m1)XΠ,FE(G)2/mXΣ,FE(G)2(m1)XΣ,F2E(G)+mXΠ,FE(G)2/m.

    Proof. Lemma 2.1 gives

    uV(G)FV(du)2+n(n1)(uV(G)FV(du)2)1/n(uV(G)FV(du))2,XΣ,F2V(G)+n(n1)XΠ,FV(G)2/nXΣ,FV(G)2,

    and

    (uV(G)FV(du))2(n1)uV(G)FV(du)2+n(uV(G)FV(du)2)1/n,XΣ,FV(G)2(n1)XΣ,F2V(G)+nXΠ,FV(G)2/n.

    In a similar way, replacing FV(du) and n with FE(du,dv) and m, respectively, we obtain

    uvE(G)FE(du,dv)2+m(m1)(uvE(G)FE(du,dv)2)1/m(uvE(G)FE(du,dv))2,XΣ,F2E(G)+m(m1)XΠ,FE(G)2/mXΣ,FE(G)2,

    and

    (uvE(G)FE(du,dv))2(m1)uvE(G)FE(du,dv)2+m(uvE(G)FE(du,dv)2)1/m,XΣ,FE(G)2(m1)XΣ,F2E(G)+mXΠ,FE(G)2/m.

    Let us recall the following known Petrović inequality [25].

    Theorem 2.5. Let φ be a convex function on [0,a], and w1,,wn0. If t1,,tn[0,a] satisfy nk=1tkwk(0,a], and

    nk=1tkwktj,j=1,,n,

    then

    nk=1φ(tk)wkφ(nk=1tkwk)+(nk=1wk1)φ(0).

    Theorem 2.5 has the following consequence.

    Proposition 2.3. Let φ be a convex function on [0,a]. If t1,,tn[0,a] satisfy nk=1tk(0,a], then

    nk=1φ(tk)φ(nk=1tk)+(n1)φ(0).

    Proposition 2.3 allows for the proof of the following inequalities.

    Theorem 2.6. Let G be a graph with n vertices and m edges. Then,

    XΣ,FV(G)XΠ,FV(G)+n1,XΣ,FE(G)XΠ,FE(G)+m1.

    Proof. Let us consider the convex function φ(x)=ex. Proposition 2.3 gives

    XΣ,FV(G)=uV(G)FV(du)=uV(G)elogFV(du)euV(G)logFV(du)+n1=elogΠuV(G)FV(du)+n1=ΠuV(G)FV(du)+n1=XΠ,FV(G)+n1.

    In a similar way,

    XΣ,FE(G)=uvE(G)FE(du,dv)=uvE(G)elogFE(du,dv)euvE(G)logFE(du,dv)+m1=elogΠuvE(G)FE(du,dv)+m1=ΠuvE(G)FE(du,dv)+m1=XΠ,FE(G)+m1.

    Theorem 2.7. Let G be a graph. Then,

    XΠ,FV(G)XΣ,logFV(G)+1,XΠ,FE(G)XΣ,logFE(G)+1.

    Proof. The equality exx+1 holds for every xR. This inequality gives

    XΠ,FV(G)=elogXΠ,FV(G)logXΠ,FV(G)+1=log(ΠuE(G)FV(du))+1=uE(G)logFV(du)+1=XΣ,logFV(G)+1.

    The same argument gives the second inequality.

    In [26], the Gutman-Milovanović index is defined as

    Mα,β(G)=uvE(G)(dudv)α(du+dv)β,

    which is a natural generalization of Zagreb indices.

    Next, we present an inequality relating some multiplicative indices and the Gutman-Milovanović index.

    Theorem 2.8. Let G be a graph with m edges, and α,βR. Then,

    Mα,β(G)mΠ2(G)α/mΠ1(G)β/m.

    The equality is attained for any α,β if G is regular.

    Proof. Since the geometric mean is at most the arithmetic mean, we get

    1mMα,β(G)=1muvE(G)(dudv)α(du+dv)β(uvE(G)(dudv)α(du+dv)β)1/m=Π2(G)α/mΠ1(G)β/m.

    Thus,

    Mα,β(G)mΠ2(G)α/mΠ1(G)β/m.

    If the graph is δ-regular, then Mα,β(G)=2βδ2α+βm, Π2(G)α/m=(δ2m)α/m=δ2α, Π1(G)β/m=((2δ)m)β/m=2βδβ, and the equality holds.

    We have some direct formulas for some multiplicative indices, e.g.,

    Π1(G)=NK(G)2,Π2(G)=RΠ(G)2,Π1(G)=2mHΠ(G)1,Π1(G)=SΠ(G)2,HΠ(G)=2mSΠ(G)2,

    for every graph G with m edges.

    Let us show some inequalities relating different multiplicative indices.

    Theorem 2.9. Let G be a graph with m edges, maximum degree Δ and minimum degree δ. Then,

    2mΔmΠ2(G)Π1(G)2mδmΠ2(G),(2ΔδΔ+δ)mRΠ(G)HΠ(G)RΠ(G),(2δ2)mΠ2(G)2IDΠ(G)(2Δ2)mΠ2(G)2,2mΠ2(G)1IDΠ(G)(Δ2+δ2Δδ)mΠ2(G)1.

    Furthermore, the equality in each inequality is attained for every regular graph.

    Proof. First of all, note that if

    cFE(x,y)αFE(x,y)βC

    for every δx,yΔ, then we have for every uvE(G),

    cFE(du,dv)βFE(du,dv)αCFE(du,dv)β,cm(uvE(G)FE(du,dv))β(uvE(G)FE(du,dv))αCm(uvE(G)FE(du,dv))β,cmXΠ,FE(G)βXΠ,FE(G)αCmXΠ,FE(G)β,

    for every graph G with m edges, maximum degree Δ, and minimum degree δ.

    Since

    2Δ1x+1y=x+yxy2δ,

    we have

    2mΔmΠ2(G)Π1(G)2mδmΠ2(G).

    Since

    2ΔδΔ+δ2xyx+y=2x+y1xy1,

    we have

    (2ΔδΔ+δ)mRΠ(G)HΠ(G)RΠ(G).

    Since

    2δ2x2+y2=1x2+1y2(xy)22Δ2,

    we have

    (2δ2)mΠ2(G)2IDΠ(G)(2Δ2)mΠ2(G)2.

    Let us consider the function

    A(x,y)=1x2+1y21xy=xy+yx=f(t),

    where

    t=xy,f(t)=t+1t.

    Since

    f(t)=11t2=t21t2

    f is decreasing on (0,1] and increasing on [1,). Since f(1)=2, we conclude

    2A(x,y)Δ2+δ2Δδ.

    Hence,

    2mΠ2(G)1IDΠ(G)(Δ2+δ2Δδ)mΠ2(G)1.

    Let G be a δ-regular graph. Hence,

    Π2(G)=δ2m,Π1(G)=(2δ)m,RΠ(G)=δm,HΠ(G)=δm,IDΠ(G)=2mδ2m,2ΔδΔ+δ=1,Δ2+δ2Δδ=2.

    Therefore, the equality in each inequality is attained for every regular graph.

    Recall that a biregular graph is a bipartite graph for which any vertex in one side of the given bipartition has degree Δ and any vertex in the other side of the bipartition has degree δ. We say that a graph is (Δ,δ)-biregular if we want to write explicitly the maximum and minimum degrees.

    Theorem 2.10. If G is a graph with m edges, minimum degree δ, and maximum degree Δ, then

    23mΠ1(G)2IDΠ(G)(Δ+δ)2m(Δ2+δ2)mΠ1(G)2.

    The equality in the lower bound is attained if and only if each connected component of G is a regular graph. The equality in the upper bound is attained if and only if G is a regular or biregular graph.

    Proof. Define

    A(x,y):=x2+y2(x+y)2=(x+y)2(x2+y2),

    for x,y[δ,Δ]. Note that, in order to find the bounds of A, it suffices to consider the case yx. We have

    Ay(x,y)=2(x+y)(x2+y2)2(x+y)2y3=2x2y3(x+y)(y3+x2yx3x2y)=2x2y3(x+y)(y3x3)>0,

    for every y(x,Δ]. Hence, A(x,x)A(x,y)A(x,Δ). Note that A(x,x)=8.

    Define

    B(x)=A(x,Δ)=(x+Δ)2(x2+Δ2),

    for x[δ,Δ]. We get

    B(x)=2Δ2x3(x+Δ)(x3Δ3)<0,

    for every x[δ,Δ). Therefore,

    A(x,Δ)=B(x)B(δ)=(Δ+δ)2(Δ2+δ2),

    for x[δ,Δ]. Hence,

    8A(x,y)(Δ+δ)2(Δ2+δ2),

    and so,

    23mΠ1(G)2IDΠ(G)(Δ+δ)2m(Δ2+δ2)mΠ1(G)2.

    The previous argument gives that the equality in the lower bound is attained if and only if du=dv for every uvE(G), and this happens if and only if each connected component of G is a regular graph.

    Also, the equality in the upper bound is attained if and only if {du,dv}={Δ,δ} for every uvE(G), and this happens if and only if G is a regular (if Δ=δ) or (Δ,δ)-biregular (if Δδ) graph.

    The statistical properties of topological indices in random networks have been intensively studied, see, e.g., [27,28]. Within this statistical approach, it has been recently shown that the average values of indices of the type XΣ(G), normalized to the order of the network n, scale with the average degree d, see, e.g., [15,16,17,18]. That is, XΣ(G)/n is a function of d only. Moreover, it was also found that XΣ(G), for indices like R(G) and H(G), is highly correlated with the Shannon entropy of the eigenvectors of the adjacency matrix of random networks [19]. This is a notable result because it puts forward the application of topological indices beyond mathematical chemistry. Specifically, given the equivalence of the Hamiltonian of a tight-binding network (in the proper setup) and the corresponding network adjacency matrix, either R(G) or H(G) could be used to determine the eigenvector localization and delocalization regimes which in turn determine the insulator and metallic regimes of quantum transport.

    Below, for the first time to our knowledge, we apply the MTIs of Eqs (1.9)–(1.16) on three models of random networks: Erdös-Rényi (ER) networks, random geometric (RG) graphs, and bipartite random (BR) networks.

    ER networks [29,30,31] GER(n,p) are formed by n vertices connected independently with probability p[0,1]. While RG graphs [32,33] GRG(n,r) consist of n vertices uniformly and independently distributed on the unit square, where two vertices are connected by an edge if their Euclidean distance is less than or equal to the connection radius r[0,2]. In addition, we examine BR networks GBR(n1,n2,p) composed of two disjoint sets, set 1 and set 2, with n1 and n2 vertices each such that there are no adjacent vertices within the same set, being n=n1+n2 the total number of vertices in the bipartite network. The vertices of the two sets are connected randomly with probability p[0,1].

    Before computing MTIs on ER random networks, we note that in the dense limit, i.e., when d1, we can approximate dudvd in Eqs (1.9)–(1.16), with

    d=(n1)p. (3.1)

    Thus, for example, when np1, we can approximate NKΠ(GER) as

    NK(GER)=uV(G)duuV(G)ddn,

    which leads us to

    lnNK(GER)nlnd

    or

    lnNK(GER)nlnd. (3.2)

    A similar approximation gives

    lnΠ1(GER)n2lnd. (3.3)

    Also, for Π2(GER), we have that

    Π2(GER)=uvE(G)dudvuvE(G)dd=uvE(G)d2dnd

    where we have used E(GER)∣=nd/2. Therefore,

    lnΠ2(GER)ndlnd

    or

    lnΠ2(GER)ndlnd. (3.4)

    Following this procedure, for the rest of the MTIs of Eqs (1.12)–(1.16) we get:

    lnΠ1(GER)n12dln(2d), (3.5)
    lnRΠ(GER)n12dlnd, (3.6)
    lnHΠ(GER)n12dlnd, (3.7)
    lnχΠ(GER)nln24d14dlnd, (3.8)

    and

    lnIDΠ(GER)nln22ddlnd. (3.9)

    From the approximate expressions above we note that the logarithm of the MTIs on ER random networks, normalized to the network size n, does not depend on n. That is, in the dense limit, we expect lnXΠ(GER)/n to depend on d only; here, XΠ represents all the MTIs of Eqs (1.9)–(1.16).

    With Eqs (3.2)–(3.9) as a guide, in what follows we compute the average values of the logarithm of the MTIs listed in Eqs (1.9)–(1.16). All averages are computed over ensembles of 107/n ER networks characterized by the parameter pair (n,p).

    In Figure 1 we present the average logarithm of the eight MTIs of Eqs (1.9)–(1.16) as a function of the probability p of ER networks of sizes n={125,250,500,1000}. Since lnXΠ(GER)<0, for RΠ, HΠ, and χΠ we conveniently plotted lnXΠ(GER) in log scale to have a detailed view of the data for small p, see Figure 1(e–g). Thus, we observe that lnXΠ(GER) for NK, Π1,2, and Π1 [for RΠ, HΠ, and χΠ] is a monotonically increasing [monotonically decreasing] function of p. In contrast, lnIDΠ(GER) is a nonmonotonic function of p, see Figure 1(h).

    Figure 1.  Average logarithm of the multiplicative topological indices of Eqs (1.9)–(1.16) as a function of the probability p of Erdös-Rényi networks of size n. The inset in (h) is an enlargement for small p.

    Then, following Eqs (3.2)–(3.9), in Figure 2 we show again the average logarithm of the MTIs, but now normalized to the network size, lnXΠ(GER)/n, as a function of d. As can be clearly observed in this figure, the curves lnXΠ(GER)/n versus d fall one on top of the other for different network sizes; so, these indices are properly scaled where d works as the scaling parameter. That is, d is the parameter that fixes the average values of the normalized MTIs on ER networks. It is remarkable that the scaling of lnXΠ(GER)/n with d, expected for d1 according to Eqs (3.2)–(3.9), works perfectly well for all values of d; even for d1. Since the topological properties of random graphs and networks, when characterized by indices of the form XΣ(G), have been shown to scale with d for all values of d (see e.g., [15,16,17,18,19]), we should expect other topological measures to also scale with d, such as the MTIs studied here. Also, in Figure 2, we show that Eqs (3.2)–(3.9) (orange-dashed lines) indeed describe well the data (thick full curves) for d10, which can be regarded as the dense limit of the ER model.

    Figure 2.  Average logarithm of the multiplicative topological indices of Eqs (1.9)–(1.16), normalized to the network size n, as a function of the average degree d of Erdös-Rényi networks. Orange dashed lines in (a-h) are Eqs (3.2)–(3.9), respectively. The vertical magenta dashed lines indicate d=10. The inset in (h) is an enlargement for small d.

    As in the previous Subsection, here we start by exploring the dense limit. Indeed, for RG graphs in the dense limit, i.e., when d1, we can approximate dudvd, where

    d=(n1)g(r) (3.10)

    and [34]

    g(r)={r2[π83r+12r2],0r1,132r2[1arcsin(1/r)+arccos(1/r)]+43(2r2+1)r2112r4,1r2. (3.11)

    Thus, we obtain

    lnNK(GRG)nlnd, (3.12)
    lnΠ1(GRG)n2lnd, (3.13)
    lnΠ2(GRG)ndlnd, (3.14)
    lnΠ1(GRG)n12dln(2d), (3.15)
    lnRΠ(GRG)n12dlnd, (3.16)
    lnHΠ(GRG)n12dlnd, (3.17)
    lnχΠ(GRG)nln24d14dlnd, (3.18)

    and

    lnIDΠ(GRG)nln22ddlnd. (3.19)

    Remarkably, the approximate Eqs (3.12)–(3.19) for RG graphs are exactly the same as the corresponding equations for ER graphs, see Eqs (3.2)–(3.9); however, note that the definition of d is different for both models, i.e., compare Eqs (3.1), (3.10), and (3.11).

    Then, in Figure 3 we present the average logarithm of the eight MTIs of Eqs (1.9)–(1.16) as a function of the connection radius r of RG graphs of sizes n={125,250,500,1000}. All averages are computed over ensembles of 107/n random graphs, and each ensemble is characterized by a fixed parameter pair (n,r).

    Figure 3.  Average logarithm of the multiplicative topological indices of Eqs (1.9)–(1.16) as a function of the connection radius r of random geometric graphs of size n. The inset in (h) is an enlargement for small r.

    For comparison purposes, Figure 1 for ER networks and Figure 3 for RG graphs have the same format and contents. In fact, all the observations made in the previous Subsection for ER networks are also valid for RG graphs by replacing GERGRG and pg(r). Therefore, in Figure 4 we plot lnXΠ(GRG)/n as a function of d showing that all curves are properly scaled; that is, curves for different graph sizes fall on top of each other. Also, in Figure 4 we show that Eqs (3.12)–(3.19) (orange-dashed lines) indeed describe the data well (thick full curves) for d10, which can be regarded as the dense limit of RG graphs.

    Figure 4.  Average logarithm of the multiplicative topological indices of Eqs (1.9)–(1.16), normalized to the graph size n, as a function of the average degree d of random geometric graphs. Orange dashed lines in (a-h) are Eqs (3.12)–(3.19), respectively. The vertical magenta dashed lines indicate d=10. The inset in (h) is an enlargement for small d.

    We start by writing approximate expressions for the MTIs on BR networks in the dense limit. Moreover, since edges in a bipartite network join vertices of different sets, and we are labeling here the sets as set 1 and set 2, we replace du by d1 and dv by d2 in the expression for the MTIs. Thus, when n1p1 and n2p1, we can approximate du=d1d1 and dv=d2d2 in Eqs (1.9)–(1.16), with

    d1,2=n2,1p. (3.20)

    Therefore, in the dense limit, the MTIs of Eqs (1.11)–(1.16) on BR networks are well approximated by

    lnΠ2(GBR)n1,2d1,2ln(d1d2), (3.21)
    lnΠ1(GBR)n1,2d1,2ln(d1+d2), (3.22)
    lnRΠ(GBR)n1,212d1,2ln(d1d2), (3.23)
    lnHΠ(GBR)n1,2d1,2[ln2ln(d1+d2)], (3.24)
    lnχΠ(GBR)n1,212d1,2ln(d1+d2), (3.25)

    and

    lnIDΠ(GBR)n1,2d1,2ln(1d12+1d22). (3.26)

    Above we used E(GBR)∣=n1n2p=n1,2d1,2. We note that we are not providing approximate expressions for neither NK(GBR) nor Π1(GBR).

    Now we compute MTIs on ensembles of 107/n BR networks. In contrast to ER and RG networks, now the BR network ensembles are characterized by three parameters: n1, n2, and p. For simplicity, but without lost of generality, in our numerical calculations we consider n1=n2. It is remarkable to notice that, in the case of n1=n2=n/2, where d1=d2=d=np/2, Eqs (3.21)–(3.26) reproduce Eqs (3.4)–(3.9).

    Then, in Figure 5 we present the average logarithm of the eight MTIs of Eqs (1.9)–(1.16) as a function of the probability p of BR networks of sizes n1=n2={125,250,500,1000}. Therefore, by plotting lnXΠ(GBR)/n as a function of d (see Figure 6), we confirm that the curves of the MTIs on BR networks are properly scaled, as predicted by Eqs (3.21)–(3.26); see the orange dashed lines in Figure 6 (c–h). Here, we can also say that d10 can be regarded as de dense limit of BR networks. We have verified (not shown here) that we arrive at equivalent conclusions when n1n2.

    Figure 5.  Average logarithm of the multiplicative topological indices of Eqs (1.9)–(1.16) as a function of the probability p of bipartite random networks of sizes n1 and n2. In all panels, n1=n2={125,250,500,1000}. The inset in (h) is an enlargement for small p.
    Figure 6.  Average logarithm of the multiplicative topological indices of Eqs (1.9)–(1.16), normalized to the network size n, as a function of the average degree d of bipartite random networks of sizes n1 and n2. In all panels, n1=n2={125,250,500,1000}. Orange dashed lines in (a, b) are Eqs (3.2) and (3.3), respectively. Orange dashed lines in (c–h) are Eqs (3.21)–(3.26), respectively. The vertical magenta dashed lines indicate d=10. The inset in (h) is an enlargement for small d.

    Note that in panels (a, b) of Figure 6 we included Eqs (3.2) and (3.3) as orange dashed lines. Those equations were obtained for ER networks, however they reproduce perfectly well both NK(GBR) and Π1(GBR) when d10 since we are considering n1=n2=n/2.

    In this work we have performed a thorough analytical and statistical study of multiplicative topological indices (MTIs) XΠ.

    From an analytical viewpoint, we found several inequalities that relate MTIs among themselves as well as to their additive versions XΣ. To find inequalities between different indices is a classical topic in the study of topological indices.

    Within a statistical approach we computed MTIs on random networks. Some previous works deal with the statistical analysis of (non-multiplicative) topological indices. As models of random networks we have used Erdös-Rényi (ER) networks, random geometric (RG) graphs, and bipartite random (BR) networks. We showed that the average logarithm of the MTIs, lnXΠ(G), normalized to the order of the network, scale with the network average degree d. Thus, we conclude that d is the parameter that fixes the average values of the logarithm of the MTIs on random networks. Moreover, the equivalence among Eqs (3.2)–(3.9) for ER networks, Eqs (3.12)–(3.19) for RG graphs, and Eqs (3.21)–(3.26) for BR networks (when the subsets are equal in size) allows us to state a scaling law that connects the three graph models. That is, given the MTI XΠ, the average of its logarithm divided by the network size is the same function of the average degree regardless the network model:

    lnXΠ(GER)nlnXΠ(GRG)nlnXΠ(GBR)nf(d). (4.1)

    To validate Eq (4.1), without loss of generality we choose three MTIs: the Narumi-Katayama index, the multiplicative sum-connectivity index, and the multiplicative inverse degree index. We apply these indices to ER networks, RG graphs, and BR networks and plot lnNKΠ(G)/n, lnχΠ(G)/n and lnIDΠ(G)/n in Figure 7. There, for each index, we can clearly see that the curves corresponding to the three network models fall one on top of the other, thus validating Eq (4.1). Also, notice that we are using networks of different sizes to stress the scaling law in Eq (4.1). We observe the same scaling behavior in all the other MTIs of Eqs (1.10)–(1.14) (not shown here).

    Figure 7.  Average logarithm of (a) NK(G), (b) χΠ(G), and (c) IDΠ(G), normalized to the network size n, as a function of the average degree d of random geometric graphs of size n=500, Erdös-Rényi networks of size n=250 and bipartite random networks of size n=2n1=2n2=2000. Orange dashed lines are (a) Eq (3.2), (b) Eq (3.8), and (c) Eq (3.9). The inset in (c) is an enlargement for small d.

    Finally, it is fair to mention that we found that the multiplicative version of the geometric-arithmetic index GAΠ(G) on random networks does not scale with the average degree. This situation is quite unexpected for us since all other MTIs we study here scale with d. In addition, our previous statistical and theoretical studies of topological indices of the form XΣ(G) (see Eq (1.1)), showed that the normalized geometric-arithmetic index GAΣ(G) scales with d [16]. Thus, we believe that the multiplicative index GAΠ(G) on random graphs requires further analysis.

    Augmented Reality (AR) algorithms have shown rapid development in recent years when applied to the study of different networks, such as neural and communication networks (see e.g., [35,36]). Therefore, it would be interesting to apply AR techniques in the analysis of random networks.

    We hope that our work may motivate further analytical and computational studies of MTIs.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    This publication is part of the grant PID2019-106433GB-I00 funded by Agencia Estatal de Investigación MCIN/AEI/ 10.13039/501100011033. J.A.M.-B. thanks support from CONACyT (Grant No. 286633), CONACyT-Fronteras (Grant No. 425854), and VIEP-BUAP (Grant No. 100405811-VIEP2023), Mexico. J.M.R. was supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M23), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).

    We would like to thank the referees for their comments which have improved the presentation of the paper.

    Prof. Jose M. Rodriguez is the Guest Editor of special issue "Graph theory and its applications" for AIMS Mathematics. Prof. Jose M. Rodriguez was not involved in the editorial review and the decision to publish this article.

    All authors declare no conflicts of interest in this paper.



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