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Research article

Forecasting stock market volatility: the role of gold and exchange rate

  • The objective of our paper is to show that gold and exchange rate volatility is predictive of stock volatility from both in-sample and out-of-sample perspectives. There exists very significant predictability from gold and exchange rate volatility to Hang Seng Index (HSI) return volatility among in-sample results. The out-of-sample results demonstrate the gold and exchange rate volatility extracts significantly useful information for Hang Seng Index (HSI) return volatility. Furthermore, the performance of the predictive ability of gold and exchange rate volatility is robust during business cycles and incremental framework.

    Citation: Zhifeng Dai, Huiting Zhou, Xiaodi Dong. Forecasting stock market volatility: the role of gold and exchange rate[J]. AIMS Mathematics, 2020, 5(5): 5094-5105. doi: 10.3934/math.2020327

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  • The objective of our paper is to show that gold and exchange rate volatility is predictive of stock volatility from both in-sample and out-of-sample perspectives. There exists very significant predictability from gold and exchange rate volatility to Hang Seng Index (HSI) return volatility among in-sample results. The out-of-sample results demonstrate the gold and exchange rate volatility extracts significantly useful information for Hang Seng Index (HSI) return volatility. Furthermore, the performance of the predictive ability of gold and exchange rate volatility is robust during business cycles and incremental framework.


    The function class H(E) is a collection of the function f which are holomorphic in the open unit disc

    E={z:zC    and    |z|<1}.

    Let Ap denote the class of all functions f which are analytic and p-valent in the open unit disk E and has the Taylor series expansion of the form

    f(z)=zp+n=p+1anzn,  (pN={1,2,...}). (1.1)

    For briefly, we write as:

    A1=A.

    Moreover, S represents the subclass of A which is univalent in open unit disk E. Further in area of Geometric Function Theory, numerous researchers offered their studies for the class of analytic function and its subclasses as well. The role of geometric properties is remarkable in the study of analytic functions, for instance convexity, starlikeness, close-to-convexity. A function fAp is known as p-valently starlike (Sp) and convex (Kp), whenever it satisfies the inequality

    (zf(z)f(z))>0,(zE)

    and

    (1+zf(z)f(z))>0,(zE).

    Moreover, a function f(z)Ap, is said to be p-valently starlike function of order α, written as f(z)Sp(α), if and only if

    (zf(z)f(z))>α,(zE).

    Similarly, a function f(z)Ap, is said to be p-valently convex functions of order α, written as f(z) Kp(α), if and only if

    (1+zf(z)f(z))>α,(zE),

    for some 0α<p. In particular, we have

    Sp(0)=Sp

    and

    Kp(0)=Kp.

    The convolution (Hadamard product) of f(z) and g(z) is defined as:

    f(z)g(z)=n=0anbnzn=g(z)f(z),

    where

    f(z)=n=0anzn and g(z)=n=0bnzn,   (zE).

    Let P denote the well-known Carathéodory class of functions m, analytic in the open unit disk E of the form

    m(z)=1+n=1cnzn, (1.2)

    and satisfy

    (m(z))>0.

    The quantum (or q-) calculus has a great important because of its applications in several fields of mathematics, physics and some related areas. The importance of q-derivative operator (Dq) is pretty recongnizable by its applications in the study of numerous subclasses of analytic functions. Initially in 1908, Jackson [14] defined the q -analogue of derivative and integral operator as well as provided some of their applications. Further in [11] Ismail et al. gave the idea of q -extension of class of q-starlike functions after that Srivastava [37] studied q-calculus in the context of univalent functions theory, also numerous mathematician studied q-calculus in the context of geometric functions theory. Kanas and Raducanu [17] introduced the q -analogue of Ruscheweyh differential operator and Arif et al. [3,4] discussed some of its applications for multivalent functions while Zhang et al. in [50] studied q-starlike functions related with generalized conic domain Ωk,α. By using the concept of convolution Srivastava et al. [40] introduced q-Noor integral operator and studied some of its applications, also Srivastava et al. published set of articles in which they concentrated class of q-starlike functions from different aspects (see [24,41,42,44,46,47]). Additionally, a recently published survey-cum-expository review article by Srivastava [38] is potentially useful for researchers and scholars working on these topics. For some more recent investigation about q -calculus we may refer to [1,18,19,20,21,22,23,25,31,32,33,34,38,39,45].

    For better understanding of the article we recall some concept details and definitions of the q-difference calculus. Throughout the article we presume that

    0<q<1    and    pN={1,2,3...}.

    Definition 1. ([10]) The q-number [t]q for q(0,1) is defined as:

    [t]q={1qt1q,                (tC),n1k=0qk       (t=nN).

    Definition 2. The q-factorial [n]q! for q(0,1) is defined as:

    [n]q!={1,                           (n=0),nk=1[k]q ,                    (nN).

    Definition 3. The q-generalized Pochhammer symbol [t]n,q, tC, is defined as:

    [t]n,q={1,                                               (n=0),[t]q[t+1]q[t+2]q...[t+n1]q,          (nN).

    And the q-Gamma function be defined as:

    Γq(t+1)=[t]qΓq(t)    and    Γq(1)=1.

    Definition 4. The q-integral of any function f(z) was defined be Jackson [15] as follows:

    f(z)dqz=(1q)zn=0f(qnz)qn

    provided that the series on right hand side converges absolutely.

    Definition 5. ([14]) For given q(0,1), the q-derivative operator or q-difference operator of f is defined by:

    Dqf(z)=f(z)f(qz)(1q)z,  z0,q1,=1+n=2[n]qanzn1. (1.3)

    Now we extend the idea of q-difference operator to a function f given by (1.1) from the class Ap as:

    Definition 6. For fAp, let the q-derivative operator (or q -difference operator) be defined as:

    Dqf(z)=f(z)f(qz)(1q)z,  z0,q1,=[p]qzp1+n=p+1[n]qanzn1. (1.4)

    We can observe that for p=1, and q1 in (1.4) we have

    limq1Dqf(z)=f(z).

    Definition 7. An analytic function f(z)Sp(α,q) of p-valent q-starlike functions of order α in E, if f(z)Ap, satisfies the condition

    (zDqf(z)f(z))>α,(zE),

    for some 0α<p.

    Definition 8. An analytic function f(z)Kp(α,q) of p-valent q -convex functions of order α in E, if f(z)Ap, satisfies the condition

    (Dq(zDqf(z))Dqf(z))>α,(zE),

    for some 0α<p.

    Remark 1. Let f(z)Ap, it follows that

    f(z)Kp(α,q) if and only if zDqf(z)[p]qSp(α,q)

    and

    f(z)Sp(α,q) if and only if z0[p]qf(ζ)ζdqζKp(α,q).

    Remark 2. By putting value of parameters α and p we can get some new subclasses of analytic functions:

    Sp(q)=Sp(0,q), S(α,q)=S1(α,q), Kp(q)=Kp(0,q) and K(α,q)=K1(α,q).

    Remark 3. By taking q1, then we obtain two known subclasses Sp(α) and Kp(α) of p -valently starlike and convex functions of order α, introduced by Hayami and Owa in [12].

    Let nN0 and jN. The jth Hankel determinant was introduced and studied in [29]:

    Hj(n)=|an        an+1 an+j1an+1      an+2an+j2                                    an+j1  an+j2an+2j2|,

    where a1=1. The Hankel determinant H2(1) represents a Fekete-Szeg ö functional |a3a22|. This functional has been further generalized as |a3μa22| for some real or complex number μ and also the functional |a2a4a23| is equivalent to H2(2) (see [16]). Babalola [5] studied the Hankel determinant H3(1) (see also [43]). The symmetric Toeplitz determinant Tj(n) is defined as follows:

    Tj(n)=|an     an+1  an+j1an+1                                      an+j1           an|, (1.5)

    so that

    T2(2)=|a2    a3a3    a2|, T2(3)=|a3    a4a4    a3|, T3(2)=|a2    a3     a4a3    a2     a3a4    a3     a2|

    and so on. The problem of finding the best possible bounds for ||an+1||an|| has a long history (see [8]). In particular, several authors [13,44] have studied Tj(n) for several classes.

    For our simplicity, we replace n=n+p1, into (1.5), then the symmetric Toeplitz determinant Tj(n) can be written as:

    Tj(n+p1)=|an+p1        an+pan+p+j2an+p                                                            an+p+j2            an+p1|,

    so that

    T2(p+1)=|ap+1    ap+2ap+2    ap+1|, T2(p+2)=|ap+2    ap+3ap+3    ap+2|, T3(p+1)=|ap+1    ap+2     ap+3ap+2    ap+1     ap+2ap+3    ap+2     ap+1|.

    Hankel determinants generated by perturbed Gaussian, Laguerre and Jacobi weights play an important role in Random Matrix Theory, since they represent the partition functions for the perturbed Gaussian, Laguerre and Jacobi unitary ensembles, see for example [7,26,27,28,49].

    In order to discuss our problems, we need some lemmas.

    Lemma 1. (see [8]). If a function m(z)=1+n=1cnznP, then

    |cn|2,n1.

    The inequality is sharp for

    f(z)=1+z1z.

    Lemma 2. If a function m(z)=[p]q+n=1cnzn satisfies the following inequality

    (m(z))α

    for some α, (0α<p), then

    |cn|2([p]qα),n1.

    The result is sharp for

    m(z)=[p]q+([p]q2α)z1z=[p]q+n=12([p]qα)zn.

    Proof. Let

    l(z)=m(z)α[p]qα=1+n=1cn[p]qαzn.

    Noting that l(z)P and using Lemma 1, we see that

    |cn[p]qα|2, n1,

    which implies

    |cn|2([p]qα), n1.

    Remark 4. When q1, then Lemma 2, reduces to the lemma which was introduced by Hayami et al. [12].

    Lemma 3. ([36]) If m is analytic in E and of the form (1.2), then

    2c2=c21+x(4c21)

    and

    4c3=c31+2(4c21)c1x(4c21)c1x2+2(4c21)(1|x2|)z,

    for some x, zC, with |z|1, and |x|1.

    By virtue of Lemma 3, we have

    Lemma 4. If m(z)=[p]q+n=1cnzn satisfy (m(z)>α, for some α (0α<p), then

    2([p]qα)c2=c21+{4([p]qα)2c21}x

    and

    4([p]qα)2c3=c31+2{4([p]qα)2c21}c1x{4([p]qα)2c21}c1x2+2([p]qα){4([p]qα)2c21}(1|x2|)z,

    for some x, zC, with |z|1, and |x|1.

    Proof. Since l(z)=m(z)α[p]qα=1+n=1cn[p]qαznP, replacing c2 and c3 by c2[p]qαand c3[p]qα in Lemma 3, respectively, we immediately have the relations of Lemma 4.

    Remark 5. When q1, then Lemma 4, reduces to the lemma which was introduced by Hayami et al. [12].

    Lemma 5. ([9]) Let the function m(z) given by (1.2) having positive real part in E. Also let μC, then

    |cnμckcnk|2max(1,|2μ1|),1knk.

    Theorem 1. Let the function f given by (1.1) belong to the class Sp(α,q), then

    |ap+1|2([p]qα)[p+1]q[p]q,|ap+2|2([p]qα)[p+2]q[p]q{1+2([p]qα)[p+1]q[p]q},|ap+3|2([p]qα)[p+3]q[p]q[1+2([p]qα)Λ2{ρ3+2([p]qα)}],

    where Λ2 is given by (3.6).

    Proof. Let  fS(α,q), then their exist a function P(z)=[p]q+n=1cnzn such that (m(z))>α and

    z(Dqf)(z)f(z)=m(z),

    which implies that

    z(Dqf)(z)=m(z)f(z).

    Therefore, we have

    ([n]q[p]q)an=n1l=palcnl, (3.1)

    where np+1,ap=1, c0=[p]q. From (3.1), we have

    ap+1=c1[p+1]q[p]q, (3.2)
    ap+2=1[p+2]q[p]q{c2+c21([p+1]q[p]q)}, (3.3)
    ap+3=1[p+3]q[p]q{c3+Λ1c1c2+Λ2c31}, (3.4)

    where

    Λ1=Λ2ρ3, (3.5)
    Λ2=1([p+1]q[p]q)([p+2]q[p]q), (3.6)
    ρ3=[p+1]q+[p+2]q2[p]q. (3.7)

    By using Lemma 2, we obtain the required result.

    Theorem 2. Let an analytic function f given by (1.1) be in the class Sp(α,q), then

    T3((p+1)Λ3[Ω4+4([p]qα)2Ω5+Ω7+Ω8|12([p]qα)Ω6Ω8|],

    where

    Λ3=4([p]qα)2[Ω1+Ω2(1+Ω3)],Ω1=2([p]qα)[p+1]q[p]q, (3.8)
    Ω2=2([p]qα)[p+3]q[p]q, (3.9)
    Ω3=2([p]qα)Λ2{ρ3+2([p]qα)}, (3.10)
    Ω4=1([p+1]q[p]q)2,Ω5=2Λ2Λ2Λ2ρ4, (3.11)
    Ω6=4Λ2([p+2]q[p]q)Λ2ρ3ρ4,Ω7=2([p+2]q[p]q)2, (3.12)
    Ω8=ρ4=1([p+1]q[p]q)([p+3]q[p]q). (3.13)

    Proof. A detailed calculation of T3(p+1) is in order.

    T3(p+1)=(ap+1ap+3)(a2p+12a2p+2+ap+1ap+3),

    where ap+1, ap+2, and ap+3 is given by (3.2), (3.3) and (3.4).

    Now if fS(α,q), then we have

    |ap+1ap+3||ap+1|+|ap+3|,   Ω1+Ω2(1+Ω3),             (3.14)

    where Ω1, Ω2, Ω3, is given by (3.9), (3.10) and (3.11).

    We need to maximize |a2p+12a2p+2+ap+1ap+3| for fS(α,q), so by writing ap+1, ap+2, ap+3 in terms of c1,c2,c3, with the help of (3.2), (3.3) and (3.4), we get

    |a2p+12a2p+2+ap+1ap+3||Ω4c21Ω5c41Ω6c21c2Ω7c22+Ω8c1c3|,Ω4c21+Ω5c41+Ω7c22+Ω8c1|c3Ω6c1c2Ω8|. (3.15)

    Finally applying Lemmas 2 and 5 along with (3.14) and (3.15), we obtained the required result.

    For q1, p=1 and α=0, we have following known corollary.

    Corollary 1. ([2]). Let an analytic function f be in the class S, then

    T3(2)84.

    Theorem 3. If an analytic function f given by (1.1) belongs to the class Sp(α,q), then

    |ap+1ap+3a2p+2|4([p]qα)2([p+2]q[p]q)2.

    Proof. Making use of (3.2), (3.3) and (3.4), we have

    ap+1ap+3a2p+2=ρ4c1c3+(Λ2ρ3B)c21c2Dc22+(Λ2ρ4Λ2Λ2)c41,

    where

    D=1([p+2]q[p]q)2, B=2Λ2[p+2]q[p]q.

    By using Lemma 3 and we take Υ=4([p]qα)2c21 and Z=(1|x|2)z. Without loss of generality we assume that c=c1, (0c2([p]qα)), so that

    ap+1ap+3a2p+2=λ1c4+λ2Υc2xλ3Υc2x2λ4Υ2x2+λ5ΥcZ, (3.16)

    where

    λ1=ρ44([p]qα)2+Λ2ρ3B2([p]qα)D4([p]qα)2D(Λ2ρ4Λ2Λ2)4([p]qα)2,λ2=ρ42([p]qα)2+Λ2ρ3B2([p]qα)D2([p]qα)2,λ3=ρ44([p]qα)2, λ4=D4([p]qα)2, λ5=ρ42([p]qα).

    Taking the modulus on (3.16) and using triangle inequality, we find that

    |ap+1ap+3a2p+2||λ1|c4+|λ2|Υc2|x|+|λ3|Υc2|x|2+|λ4|Υ2|x|2+|λ5|(1|x|2)cΥ=G(c,|x|).

    Now, trivially we have

    G(c,|x|)>0

    on [0,1], which shows that G(c,|x|) is an increasing function in an interval [0,1], therefore maximum value occurs at x=1 and Max G(c,|1|)=G(c).

    G(c,|1|)=|λ1|c4+|λ2|Υc2+|λ3|Υc2+|λ4|Υ2

    and

    G(c)=|λ1|c4+|λ2|Υc2+|λ3|Υc2+|λ4|Υ2.

    Hence, by putting Υ=4c21 and after some simplification, we have

    G(c)=(|λ1||λ2||λ3|+|λ4|)c4+4(|λ2|+|λ3|2|λ4|)c2+16|λ4|.

    We consider G(c)=0, for optimum value of G(c), which implies that c=0. So G(c) has a maximum value at c=0. Hence the maximum value of G(c) is given by

    16|λ4|. (3.17)

    Which occurs at c=0 or

    c2=4(|λ2|+|λ3|2|λ4|)|λ1||λ2||λ3|+|λ4|.

    Hence, by putting λ4=D4([p]qα)2 and D=1([p+2]q[p]q)2 in (3.17) and after some simplification, we obtained the desired result.

    For q1, p=1 and α=0, we have following known corollary.

    Corollary 2. ([16]). If an analytic function f belongs to the class S, then

    |a2a4a23|1.

    Theorem 4. Let f be the function given by (1.1) belongs to the class Sp(α,q), 0α<p, then

    |ap+2μa2p+1|{2([p]qα)([p+2]q[p]q){ρ1ρ2μ},if  μρ5,2([p]qα)([p+2]q[p]q),           if  ρ5μρ6,2([p]qα)([p+1]q[p]q)2([p+2]q[p]q){ρ2μρ1},if  μρ6,

    where

    ρ1={2([p]qα)([p+1]q[p]q)+([p+1]q[p]q)2},ρ2=2([p]qα)([p+2]q[p]q),ρ5=([p+1]q[p]q){2([p]qα)+([p+1]q[p]q)}12([p]qα)([p+2]q[p]q),ρ6=([p+1]q[p]q)([p]qα+([p+1]q[p]q))([p]qα)([p+2]q[p]q).

    Proof. From (3.2) and (3.3) and we can suppose that c1=c (0c2([p]qα)), without loss of generality we derive

    |ap+2μa2p+1|=1ρ7|{ρ1ρ2μ}c2+([p+1]q[p]q)2{4([p]qα)2c2}ρ|=A(ρ),

    where

    ρ7=2([p]qα)([p+1]q[p]q)2([p+2]q[p]q).

    Applying the triangle inequality, we deduce

    A(ρ)1ρ7|{ρ1ρ2μ}|c2+([p+1]q[p]q)2{4([p]qα)2c2}={1ρ7[{2([p]qα){ρ11ρ12μ}}c2+ρ9],  if μρ8,1ρ7[2{([p]qα)([p+2]q[p]q)μρ10}c2+ρ9],  if μρ8,

    where

    ρ8=2([p]qα)([p+1]q[p]q)+([p+1]q[p]q)22([p]qα)([p+2]q[p]q),ρ9=4([p]qα)2([p+1]q[p]q)2,ρ10=([p+1]q[p]q){([p]qα)+([p+1]q[p]q)},ρ11=([p+1]q[p]q),ρ12=([p+2]q[p]q),ρ13=2([p]qα)([p+1]q[p]q)2([p+2]q[p]q).
    |ap+2μa2p+1|{2([p]qα)([p+2]q[p]q){ρ1ρ2μ},if μρ5, c=2([p]qα),2([p]qα)([p+2]q[p]q),if ρ5μρ8,c=0,2([p]qα)([p+2]q[p]q),if ρ8μρ6,c=0,ρ13{ρ2μ{2([p]qα)ρ11+ρ211}}, if μρ6,c=2([p]qα).

    If q1 in Theorem 4, we thus obtain the following known result.

    Corollary 3. ([12]). Let f be the function given by (1.1) belongs to the class Sp(α), 0α<p, then

    |ap+2μa2p+1|{(pα){{2(pα)+1}4(pα)μ},if  μ12,(pα)             if  12μpα+12(pα),(pα){4(pα)μ{2(pα)+1}},if  μpα+12(pα).

    In this section, firstly we recall that the q-Bernardi integral operator for multivalent functions L(f)=Bqp,β given in [35] as:

    Let fAp, then L:ApAp is called the q-analogue of Benardi integral operator for multivalent functions defined by L(f)=Bqq,β with β>p, where, Bqq,β is given by

    Bqp,βf(z)=[p+β]qzβz0tβ1f(t)dqt, (3.18)
    =zp+n=1[β+p]q[n+β+p]qan+pzn+p, zE,=zp+n=1Bn+pan+pzn+p. (3.19)

    The series given in (3.19) converges absolutely in E.

    Remark 6. For q1, then the operator Bqp,β reduces to the integral operator studied in [48].

    Remark 7. For p=1, we obtain the q-Bernardi integral operator introduced in [30].

    Remark 8. If q1 and p=1, we obtain the familiar Bernardi integral operator studied in [6].

    Theorem 5. If f is of the form (1.1), belongs to the class Sp(α,q), and

    Bqp,βf(z)=zp+n=1Bn+pan+pzn+p,

    where Bqp,β is the integral operator given by (3.18), then

    |ap+1|2([p]qα)([p+1]q[p]q)Bp+1,|ap+2|2([p]qα)([p+2]q[p]q)Bp+2{1+2([p]qα)([p+1]q[p]q)Bp+1},|ap+3|2([p]qα)([p+3]q[p]q)Bp+3[1+2([p]qα)ρ14ρ15],

    where

    ρ14={(([p+1]q[p]q)Bp+1+([p+2]q[p]q)Bp+2)+2([p]qα)},ρ15=([p+1]q[p]q)([p+2]q[p]q)Bp+1Bp+2.

    Proof. The proof follows easily by using (3.19) and Theorem 1.

    Theorem 6. Let an analytic function f given by (1.1) be in the class Sp(α,q), in addition Bqp,β is the integral operator defined by (3.18) and is of the form (3.19), then

    T3((p+1)Υ3[Ω4B2p+1+4([p]qα)2Ω10+Ω7B2p+2+Ω8Bp+1Bp+3|12([p]qα)Bp+1Bp+3Ω11Ω8|],

    where

    Υ3=4([p]qα)2[Ω1Bp+1+Ω2Bp+3(1+Ω9)],Ω9=Λp(ρ14Bp+1Bp+2),Ω10=Λ4Λ5,Ω11=Λ6Λ7,
    Λ4=2Λ2Λ2B2p+1B2p+2,Λ5=Λ2ρ4B2p+1Bp+2Bp+3,Λ6=4Λ2([p+2]q[p]q)Bp+1B2p+2,Λ7=Λ8Λ2ρ4B2p+1Bp+2Bp+3,Λ8=([p+1]q[p]q)Bp+1+([p+2]q[p]q)Bp+2.

    Proof. The proof follows easily by using (3.19) and Theorem 2.

    Theorem 3. If an analytic function f given by (1.1) belongs to the class Sp(α,q), in addition Bqp,β is the integral operator is defined by (3.18) and is of the form (3.19), then

    |ap+1ap+3a2p+2|4([p]qα)2([p+2]q[p]q)2B2p+2.

    Theorem 8. Let f be the function given by (1.1) belongs to the class Sp(α,q), in addition Bqp,β is the integral operator defined by (3.18) and is of the form (3.19), then

    |ap+2μa2p+1|{2([p]qα)([p+2]q[p]q)Bp+2{ρ16ρ2Bp+2μ},if  μρ17,2([p]qα)([p+2]q[p]q)Bp+2,if  ρ17μρ18,2Λ2([p]qα)([p+1]q[p]q)B2p+1Bp+2{ρ2Bp+2μρ16},if  μρ18,

    where

    ρ16={2([p]qα)([p+1]q[p]q)Bp+1+([p+1]q[p]q)2B2p+1},ρ17=([p+1]q[p]q)Bp+1{2([p]qα)+([p+1]q[p]q)Bp+1}12([p]qα)([p+2]q[p]q)Bp+2,ρ18=([p+1]q[p]q)Bp+1([p]qα+([p+1]q[p]q)Bp+1)([p]qα)([p+2]q[p]q)Bp+2,

    and Λ2 is given by (3.6).

    Motivated by a number of recent works, we have made use of the quantum (or q-) calculus to define and investigate new subclass of multivalent q -starlike functions in open unit disk E. We have studied about Hankel determinant, Toeplitz matrices, Fekete–Szegö inequalities. Furthermore we discussed applications of our main results by using q-Bernardi integral operator for multivalent functions. All the results that have discussed in this paper can easily investigate for the subclass of meromorphic q-convex functions (Kp(α,q)) of order α in E, respectively.

    Basic (or q-) series and basic (or q-) polynomials, especially the basic (or q-) hypergeometric functions and basic (or q-) hypergeometric polynomials, are applicable particularly in several diverse areas (see [38], p328). Moreover, in this recently-published survey-cum expository review article by Srivastava [38], the so called (p,q)-calculus was exposed to be a rather trivial and inconsequential variation of the classical q-calculus (see for details [38], p340).

    By this observation of Srivastava in [38], we can make clear link between the q-analysis and (p,q)-analysis and the results for q -analogues which we have included in this paper for 0<q<1, can be easily transformed into the related results for the (p,q)-analogues with (0<q<p1).

    This work is supported by the program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region under the Grant NJYT-18-A14, the Natural Science Foundation of Inner Mongoliaof the people's Republic of China under Grant 2018MS01026, the Natural Science Foundation of the people's Republic of China under Grant 11561001.

    The authors declare that they have no competing interests.



    [1] G. Schwert, Why does stock market volatility change over time? J. Finance, 44 (1989), 1115-1153.
    [2] F. X. Diebold, K. Yilmaz, Macroeconomic Volatility and Stock Market Volatility, Worldwide. (No. w14269). National Bureau of Economic Research, 2008.
    [3] C. Christiansen, M. Schmeling, A. Schrimpf, A comprehensive look at financial volatility prediction by economic variables, J. Appl. Econom., 27 (2012), 956-977. doi: 10.1002/jae.2298
    [4] B. S. Paye, Déjavol: predictive regressions for aggregate stock market volatility using macroeconomic variables, J. Financ. Econ., 106 (2012), 527-546. doi: 10.1016/j.jfineco.2012.06.005
    [5] R. F. Engle, E. Ghysels, B. Sohn, Stock market volatility and macroeconomic fundamentals, Rev. Econ. Stat., 95 (2013), 776-797. doi: 10.1162/REST_a_00300
    [6] C. Conrad, K. Loch, D. Rittler, On the macroeconomic determinants of long-term volatilities and correlations in US stock and crude oil markets, J. Empir. Financ., 29 (2014), 26-40. doi: 10.1016/j.jempfin.2014.03.009
    [7] N. Nonejad, Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why? J. Empir. Financ., 42 (2017), 131-154.
    [8] Y. Wang, F. Ma, Y. Wei, et al. Forecasting realized volatility in a changing world: A dynamic model averaging approach, J. Bank. Financ., 64 (2016), 136-149. doi: 10.1016/j.jbankfin.2015.12.010
    [9] Y. Wang, Z. Pan, C. Wu, Time-Varying Parameter Realized Volatility Models, J. Forecasting, 36 (2017), 566-580. doi: 10.1002/for.2454
    [10] Y. Zhang, Y. Wei, F. Ma, et al. Economic constraints and stock return predictability: A new approach, Int. Rev. Financ. Anal., 63 (2019), 1-9.
    [11] J. Feng, Y. Wang, L. Yin, Oil volatility risk and stock market volatility predictability: Evidence from G7 countries, Energ. Econ., 68 (2017), 240-254. doi: 10.1016/j.eneco.2017.09.023
    [12] Y. Wang, Y. Wei, C. Wu, et al. Oil and the short-term predictability of stock return volatility, J. Empir. Financ., 47 (2018), 90-104. doi: 10.1016/j.jempfin.2018.03.002
    [13] Z. Dai, H. Zhou, F. Wen, et al. Efficient predictability of stock return volatility: the role of stock market implied volatility, N. Am. J. Econ. Finance, 52 (2020), 101174.
    [14] D. G. Baur, B. M. Lucey, Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold, Financ. Rev., 45 (2010) 217-229,
    [15] D. G. Baur, T. K. McDermott, Is gold a safe haven? International evidence, J. Bank. Financ., 34 (2010), 1886-1898. doi: 10.1016/j.jbankfin.2009.12.008
    [16] M. Hood, F. Malik, Is gold the best hedge and a safe haven under changing stock market volatility? Rev. Financ. Econ., 22 (2013), 47-52.
    [17] J. D. Hamilton, Oil and the macroeconomy since World War II, J. Pol. Econ., 91 (1983), 228-248. doi: 10.1086/261140
    [18] D. Roubaud. M. Arouri, Oil prices, exchange rates and stock markets under uncertainty and regime-switching, Financ. Res. Lett., 27 (2018), 28-33. doi: 10.1016/j.frl.2018.02.032
    [19] A. Salisu, H. Mobolaji, Modeling returns and volatility transmission between oil price and US-Nigeria exchange rate, Energy Economics, 39 (2013), 169-176. doi: 10.1016/j.eneco.2013.05.003
    [20] R. Aloui, M. S. Ben Aïssa, D. K. Nguyen, Conditional dependence structure between oil prices and exchange rates: a copula-GARCH approach, J. Int. Money Fin., 32 (2013), 719-738. doi: 10.1016/j.jimonfin.2012.06.006
    [21] M. Pal, P. M. Rao, P. Manimaran, Multifractal detrended cross correlation analysis on gold, crude oil and foreign exchangerate time series, Physica A, 416 (2014), 452-460. doi: 10.1016/j.physa.2014.09.004
    [22] A. Jain, P. C. Biswal, Dynamic linkages among oil price, gold price, exchange rate, and stock market in India, Resources Policy, 49 (2016), 179-185.
    [23] J. F. Li, X. S. Lu, Y. Zhou, Cross-correlations between crude oil and exchange markets for selected oil rich economies, Physica A, 453 (2016), 131-143. doi: 10.1016/j.physa.2016.02.039
    [24] A. K. Mishra, N. Swain, D. K. Malhotra, Volatility spillover between stock and foreign exchange markets: Indian evidence, Int. J. Business, 12 (2007), 343-359.
    [25] D. Choi, V. Fang, T. Fu, Volatility spillovers between New Zealand stock market returns and exchange rate changes before and after the 1997 Asian financial crisis, Asian journal of Finance and Accounting, 1 (2009), 106-117.
    [26] C. Walid, A. Chaker, O. Masood, et al. Stock market volatility and exchange rates in emerging countries: a Markov switching approach, Emerg. Mark. Rev., 12 (2011), 272-292. doi: 10.1016/j.ememar.2011.04.003
    [27] K. Grobys, Are volatility spillovers between currency and equity market driven by economic states? Evidence from the US economy, Econ. Lett., 127 (2015), 72-75. doi: 10.1016/j.econlet.2014.12.034
    [28] N. Oberholzer, S. T. von Boetticher, Volatility Spill-over between the JSE/FTSE Indices and the South African Rand, Proc. Econ. Finan., 24 (2015), 501-510. doi: 10.1016/S2212-5671(15)00618-8
    [29] W. Mensi, M. Beljid, A. Boubaker, et al. Correlations and volatility spillovers across commodity and stock markets: linking energies, food and gold, Econ. Model., 32 (2013), 15-22. doi: 10.1016/j.econmod.2013.01.023
    [30] Z. Dai, X. Chen, F. Wen, A modified Perry's conjugate gradient method-based derivative-free method for solving large-scale nonlinear monotone equations, Appl. Math. Comput., 270 (2015), 378-386.
    [31] T. Choudry, S. Hassan, S. Shabi, Relationship between gold and stock markets during the global financial crisis: evidence from nonlinear causality tests, Int. Rev. Financ. Anal., 41 (2015), 247-256. doi: 10.1016/j.irfa.2015.03.011
    [32] A. I. Maghyereh, T. Awartani, Volatility spillovers and cross-hedging between gold, oil and equities: evidence from the Gulf Cooperation Council countries, Energy Economics, 68 (2017), 440-453. doi: 10.1016/j.eneco.2017.10.025
    [33] S. J. Taylor, Modeling Financial Time Series. John Wiley and Sons, Ltd., 1986.
    [34] Z. F, Dai, H. Zhu, Forecasting stock market returns by combining sum-of-the-parts and ensemble empirical mode decomposition, Appl. Econ., 52 (2020), 2309-2323. doi: 10.1080/00036846.2019.1688244
    [35] T. G. Andersen, T. Bollerslev, Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns, J. Finance, 52 (1997), 975-1005. doi: 10.1111/j.1540-6261.1997.tb02722.x
    [36] T. G. Andersen, T. Bollerslev, F. X. Diebold, et al. The distribution of realized stock return volatility, J. Financ. Econ., 61 (2001), 43-76. doi: 10.1016/S0304-405X(01)00055-1
    [37] T. G. Andersen, T. Bollerslev, F. X. Diebold, et al. Modeling and forecasting realized volatility, Econometrica, 71 (2003), 529-626.
    [38] J. Y. Campbell, S. B. Thompson, Predicting excess stock returns out of sample: can anything beat the historical average? Rev. Financ. Stud., 21 (2008), 1509-1531.
    [39] C. J. Neely, D. E. Rapach, J. Tu, et al. Forecasting the equity risk premium: the role of technical indicators, Manag. Sci., 60 (2014), 1772-1791. doi: 10.1287/mnsc.2013.1838
    [40] D. E. Rapach, J. K. Strauss, G. Zhou, Out-of-sample equity premium prediction: combination forecasts and links to the real economy, Rev. Financ. Stud., 23 (2010), 821-862. doi: 10.1093/rfs/hhp063
    [41] Z. F. Dai, H. T. Zhou, Prediction of stock returns: sum-of-the-parts method and economic constraint method, Sustainability, 12 (2020), 541.
    [42] Z. F. Dai, H. Zhu, Stock return predictability from a mixed model perspective, Pac-Basin. Finac. J., 60 (2020), 101267.
    [43] F. Wang, W. Zhao, S. Jiang, Detecting asynchrony of two series using multiscale cross-trend sample entropy, Nonlinear Dynam., 99 (2020), 1451-1465. doi: 10.1007/s11071-019-05366-y
    [44] Z. F, Dai. H. Zhu, F. Wen, Two nonparametric approaches to mean absolute deviation portfolio selection model, J. Ind. Manag. Optim., 13 (2019), 1.
    [45] Z. F. Dai, X. Dong, J. Kang, et al. Forecasting stock market returns: new technical indicators and two-step economic constraint method, N. Am. J. Econ. Finance, 53 (2020), 101216.
    [46] Z. F. Dai, F. H. Wen, Some improved sparse and stable portfolio optimization problems, Financ. Res. Lett., 27 (2018), 46-52. doi: 10.1016/j.frl.2018.02.026
    [47] F. Wen, Y. Zhao, M. Zhang, et al. Forecasting realized volatility of crude oil futures with equity market uncertainty, Appl. Econ., 51 (2019), 6411-6427. doi: 10.1080/00036846.2019.1619023
    [48] F. Wen, L. Xu, G. Ouyang, et al. Retail investor attention and stock price crash risk: Evidence from China, Int. Rev. Financ. Anal., 65 (2019), 101376.
    [49] Z. F. Dai, H. Zhu, A modified Hestenes-Stiefel-type derivative-free method for large-scale nonlinear monotone equations, Mathematics, 8 (2020), 168.
    [50] T. E. Clark, K. D. West, Approximately normal tests for equal predictive accuracy in nested models, J. Econometrics, 138 (2007), 291-311.
    [51] A. Inoue, L. Kilian, In-sample or out-of-sample tests of predictability: Which one should we use? Economet. Rev., 23 (2004), 371-402.
    [52] Y. Zhang, F. Ma, T. Wang, et al. Out-of-sample volatility prediction: A new mixed-frequency approach, J. Forecasting, 38 (2019), 669-680 doi: 10.1002/for.2590
    [53] Y. Zhang, F. Ma, Y. Liao, Forecasting global equity market volatilities, Int. J. Forecasting, 2020.
    [54] T. Choudhry, F. I. Papadimitriou, S. Shabi, Stock market volatility and business cycle: evidence from linear and nonlinear causality tests, J. Bank. Financ., 66 (2016), 89-101. doi: 10.1016/j.jbankfin.2016.02.005
    [55] J. Chen, F. Jiang, Y. Liu, et al. International volatility risk and Chinese stock return predictability, J. Int. Money Financ., 70 (2016), 183-203.
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