Loading [MathJax]/jax/output/SVG/jax.js
Research article

The heterogeneous linkage of economic policy uncertainty and oil return risks

  • Received: 12 February 2019 Accepted: 05 March 2019 Published: 18 March 2019
  • JEL Codes: C51, E65, G14, G18

  • The recent financial crisis and its aftermath boost the research of economic policy uncertainty and its relevant topics. In this paper, we forecast the oil return risks based on the CAViaR method and further depict the dynamic and heterogeneous features during the crisis (or non-crisis) period, as well as in different markets via DCC-GARCH models. The empirical results show the linkage of economic policy uncertainty and oil return risks, indicating an increasing trend and stronger relationship with major events. Further study shows the heterogeneous feature existing during crisis or non-crisis period, and there is heterogeneity in values and variations of their linkage in different markets. Therefore, policymakers should intervene timely in the crude oil market, release good news, and stabilize oil prices during the crisis period. During the non-crisis period, however, investors need to rationally analyze the price trend of the oil market, thereby preventing possible risks in the market.

    Citation: Hao Dong, Yue Liu, Jiaqi Chang. The heterogeneous linkage of economic policy uncertainty and oil return risks[J]. Green Finance, 2019, 1(1): 46-66. doi: 10.3934/GF.2019.1.46

    Related Papers:

    [1] Grace Noveli Belvy Louvila, Armel Judice Ntsokongo, Franck Davhys Reval Langa, Benjamin Mampassi . A conserved Caginalp phase-field system with two temperatures and a nonlinear coupling term based on heat conduction. AIMS Mathematics, 2023, 8(6): 14485-14507. doi: 10.3934/math.2023740
    [2] Armel Judice Ntsokongo, Daniel Moukoko, Franck Davhys Reval Langa, Fidèle Moukamba . On higher-order anisotropic conservative Caginalp phase-field type models. AIMS Mathematics, 2017, 2(2): 215-229. doi: 10.3934/Math.2017.2.215
    [3] Kiran Sajjan, N. Ameer Ahammad, C. S. K. Raju, M. Karuna Prasad, Nehad Ali Shah, Thongchai Botmart . Study of nonlinear thermal convection of ternary nanofluid within Darcy-Brinkman porous structure with time dependent heat source/sink. AIMS Mathematics, 2023, 8(2): 4237-4260. doi: 10.3934/math.2023211
    [4] Jean De Dieu Mangoubi, Mayeul Evrard Isseret Goyaud, Daniel Moukoko . Pullback attractor for a nonautonomous parabolic Cahn-Hilliard phase-field system. AIMS Mathematics, 2023, 8(9): 22037-22066. doi: 10.3934/math.20231123
    [5] Armel Judice Ntsokongo, Narcisse Batangouna . Existence and uniqueness of solutions for a conserved phase-field type model. AIMS Mathematics, 2016, 1(2): 144-155. doi: 10.3934/Math.2016.2.144
    [6] Nadeem Abbas, Wasfi Shatanawi, Taqi A. M. Shatnawi . Innovation of prescribe conditions for radiative Casson micropolar hybrid nanofluid flow with inclined MHD over a stretching sheet/cylinder. AIMS Mathematics, 2025, 10(2): 3561-3580. doi: 10.3934/math.2025164
    [7] Brice Landry Doumbé Bangola . Phase-field system with two temperatures and a nonlinear coupling term. AIMS Mathematics, 2018, 3(2): 298-315. doi: 10.3934/Math.2018.2.298
    [8] Hyun Geun Lee . A mass conservative and energy stable scheme for the conservative Allen–Cahn type Ohta–Kawasaki model for diblock copolymers. AIMS Mathematics, 2025, 10(3): 6719-6731. doi: 10.3934/math.2025307
    [9] Ahmed Abouelregal, Meshari Alesemi, Husam Alfadil . Thermoelastic reactions in a long and thin flexible viscoelastic cylinder due to non-uniform heat flow under the non-Fourier model with fractional derivative of two different orders. AIMS Mathematics, 2022, 7(5): 8510-8533. doi: 10.3934/math.2022474
    [10] Yonghui Zou, Xin Xu, An Gao . Local well-posedness to the thermal boundary layer equations in Sobolev space. AIMS Mathematics, 2023, 8(4): 9933-9964. doi: 10.3934/math.2023503
  • The recent financial crisis and its aftermath boost the research of economic policy uncertainty and its relevant topics. In this paper, we forecast the oil return risks based on the CAViaR method and further depict the dynamic and heterogeneous features during the crisis (or non-crisis) period, as well as in different markets via DCC-GARCH models. The empirical results show the linkage of economic policy uncertainty and oil return risks, indicating an increasing trend and stronger relationship with major events. Further study shows the heterogeneous feature existing during crisis or non-crisis period, and there is heterogeneity in values and variations of their linkage in different markets. Therefore, policymakers should intervene timely in the crude oil market, release good news, and stabilize oil prices during the crisis period. During the non-crisis period, however, investors need to rationally analyze the price trend of the oil market, thereby preventing possible risks in the market.


    1. Introduction

    G. Caginalp introduced in [1] and [2] the following phase-field systems:

    ut+Δ2uΔf(u)=Δθ, (1.1)
    θtΔθ=ut, (1.2)

    where u is the order parameter and θ is the (relative) temperature. These equations model phase transition processes such as melting/solidification processes and have been studied, e.g., in [4] for a similar phase-field model with a memory term. Eqs. (1.1)-(1.2) consist of the coupling of the Cahn-Hilliard equation introduced in [18] with the heat equation and are known as the conserved phase-field model, in the sens that, when endowed with Neumann boundary conditions, the spatial average of the order parameter is a conserved quantity (see below). We refer the reader to, e.g., [6,7,8,9,10,11,13,14,16,17,19,20,22,23,24,25].

    Equations (1.1) and (1.2) are based on the total free energy

    ψ(u,θ)=Ω(12|u|2+F(u)uθ12θ2)dx, (1.3)

    where Ω is the domain occupied by the material (we assume that it is a bounded and smooth domain of Rn, n=2 or 3) and F=f (typically, F is the double-well potential F(s)=14(s21)2, hence f(s)=s3s). We then introduce the enthalpy H defined by

    H=θψ, (1.4)

    where denotes a variational derivative, so that

    H=u+θ. (1.5)

    The gouverning equations for u and θ are finally given by

    ut=Δuψ, (1.6)
    Ht=divq, (1.7)

    where q is the thermal flux vector. Assuming the classical Fourier law

    q=θ, (1.8)

    we obtain (1.1) and (1.2).

    Now, one drawback of the Fourier law is that it predicts that thermal signals propagate with an infinite speed, which violates causality (the so-called "paradox of heat conduction", see, e.g. [5]). Therefore, several modifications of (1.8) have been proposed in the literature to correct this unrealistic feature, leading to a second order in time equation for the temperature.

    In particular, we considered in [15] (see also [19] the Maxwell-Cattaneo law)

    (1+ηt)q=θ,η>0, (1.9)

    which leads to

    η2θt2+θtΔθ=η2ut2ut. (1.10)

    Green and Naghdi proposed in [21] an alternative treatment for a thermomechanical theory of deformable media. This theory is based on an entropy balance rather than the usual entropy inequality and is proposed in a very rational way. If we restrict our attention to the heat conduction, we recall that proposed three different theories, labelled as type Ⅰ, type Ⅱ and type Ⅲ, respectively. In particular, when type Ⅰ is linearized, we recover the classical theory based on the Fourier law. The linearized versions of the two other theories are decribed by the constitutive equation of type Ⅱ (see [12])

    q=kα,k>0, (1.11)

    where

    α(t)=tt0θ(τ)dτ+α0 (1.12)

    is called the thermal displacement variable. It is pertinent to note that these theories have received much attention in the recent years.

    If we add the constitutive equation (1.9) to equation (1.7), we then obtain the following equations for α (note that αt=θ):

    2αt2kΔα=ut. (1.13)

    Our aim in this paper is to study the model consisting the equation (1.1) (θ=αt) and the temperature equation (1.13). In particular, we obtain the existence and the uniqueness of tne solutions.


    2. Setting of the problem

    We consider the following initial and boundary value problem (for simplificity, we take k=1):

    ut+Δ2uΔf(u)=Δαt, (2.1)
    2αt2Δα=ut, (2.2)
    u=Δu=α=0onΓ, (2.3)
    u|t=0=u0,α|t=0=α0,αt|t=0=α1, (2.4)

    where Γ is the boundary of the spatial domain Ω.

    We make the following assumptions:

    f is of class C2(R),f(0)=0, (2.5)
    f(s)c0,c00,sR, (2.6)
    f(s)sc1F(s)c2c3,c1>0,c2,c30,sR, (2.7)

    where F(s)=s0f(τ)dτ. In particular, the usual cubic nonlinear term f(s)=s3s satisfies these assumptions.

    We futher assume that

    u0H10(Ω)H2(Ω). (2.8)

    Remark 2.1. We take here, for simplicity, Dirichlet boundary conditions. However, we can obtain the same results for Neumann boundary conditions, namely,

    uν=Δuν=αν=0onΓ, (2.9)

    where ν denotes the unit outer normal to Γ. To do so, we rewrite, owing to (2.1) and (2.2), the equations in the form

    ¯ut+Δ2¯uΔ(f(u)f(u))=Δ¯αt, (2.10)
    2¯αt2Δ¯α=¯ut, (2.11)

    where ¯v=vv, |v0|M1, |α0|M2, for fixed positive constants M1 and M2. Then, we note that

    v((Δ)12¯v2+v2)12,

    where, here, Δ denotes the minus Laplace operator with Neumann boundary conditions and acting on functions with null average and where it is understood that

    .=1vol(Ω).,1H1(Ω),H1(Ω).

    Furthermore,

    v(¯v2+v2)12,
    v(¯v2+v2)12,

    and

    v(Δ¯v2+v2)12

    are norms in H1(Ω), L2(Ω), H1(Ω) and H2(Ω), respectively, which are equivalent to the usual ones. We further assume that

    |f(s)|ϵF(s)+cϵ,ϵ>0,sR, (2.12)

    which allows to deal with term f(u).

    We denote by . the usual L2-norm (with associated scalar product ((., .))) and set .1=(Δ)12., where Δ denotes the minus Laplace operator with Dirichlet boundary conditions. More generally, .X denotes the norm in the Banach space X.

    Throughout this paper, the same letters c, c and c denotes (generally positive) constants which may change from line to line, or even in a same line. Similary, the same letter Q denotes monotone increasing (with respect to each argument) functions which may change from line to line, or even in a same line.


    3. A priori estimates

    The estimates derived in this section are formal, but they can easily be justified within a Galerkin scheme.

    We rewrite (2.1) in the equivalent form

    (Δ)1utΔu+f(u)=αt. (3.1)

    We multiply (3.1) by ut and have, integrating over Ω and by parts,

    ddt(u2+2ΩF(u)dx)+2ut21=2((αt,ut)). (3.2)

    We then multiply (2.2) by αt and obtain

    ddt(α2+αt2)=2((αt,ut)). (3.3)

    Summing (3.2) and (3.3), we find a differential inequality of the form

    dE1dt+cut21c,c>0, (3.4)

    where

    E1=u2+2ΩF(u)dx+α2+αt2

    satisfies

    E1c(uH1(Ω)+ΩF(u)dx+α2H1(Ω)+αt2)c,c>0, (3.5)

    hence estimates on u,αL(0,T;H10(Ω)), on utL2(0,T;H1(Ω)) and on αtL(0,T;L2(Ω)).

    We multiply (3.1) by Δut to find

    12ddtΔu2+ut2=((Δf(u),ut))((Δαt,ut)),

    which yields, owing to (2.5) and the continuous embedding H2(Ω)C(¯Ω),

    ddtΔu2+ut2Q(uH2(Ω))2((Δαt,ut)). (3.6)

    Multiplying also (2.2) by Δαt, we have

    ddt(Δα2+αt2)=2((Δαt,ut)). (3.7)

    Summing then (3.6) and (3.7), we obtain

    ddt(Δu2+Δα2+αt2)+ut2Q(uH2(Ω)). (3.8)

    In particular, setting

    y=Δu2+Δα2+αt2,

    we deduce from (3.8) an inequation of the form

    yQ(y). (3.9)

    Let z be the solution to the ordinary differential equation

    z=Q(z),z(0)=y(0). (3.10)

    It follows from the comparison principle that there exists T0=T0(u0H2(Ω),α0H2(Ω),α1H1(Ω)) belonging to, say, (0,12) such that

    y(t)z(t),t[0,T0], (3.11)

    hence

    u(t)2H2(Ω)+α(t)2H2(Ω)+αt(t)2H1(Ω)Q(u0H2(Ω),α0H2(Ω),α1H1(Ω)),tT0. (3.12)

    We now differentiate (3.1) with respect to time and have, noting that 2αt2=Δαut,

    (Δ)1tutΔut+f(u)ut=Δαut. (3.13)

    We multiply (3.13) by tut and find, owing to (2.6)

    ddt(tut21)+32tut2ct(ut2+α2)+ut21,

    hence, noting that ut2cut1ut,

    ddt(tut21)+tut2ct(ut21+α2)+ut21. (3.14)

    In particular, we deduce from (3.4), (3.12), (3.14) and Gronwall's lemma that

    ut211tQ(u0H2(Ω),α0H2(Ω),α1H1(Ω)),t(0,T0]. (3.15)

    Multiplying then (3.13) by ut, we have, proceeding as above,

    ddtut21+ut2c(ut21+α2). (3.16)

    It thus follows from (3.4), (3.16) and Gronwall's lemma that

    ut21ectQ(u0H2(Ω),α0H2(Ω),α1H1(Ω))ut(T0)21,tT0, (3.17)

    hence, owing to (3.15),

    ut21ectQ(u0H2(Ω),α0H2(Ω),α1H1(Ω)),tT0. (3.18)

    We now rewrite (3.1) in the forme

    Δu+f(u)=hu(t),u=0onΓ, (3.19)

    for tT0 fixed, where

    hu(t)=(Δ)1ut+αt (3.20)

    satisfies, owing to (3.4) and (3.18)

    hu(t)ectQ(u0H2(Ω),α0H2(Ω),α1H1(Ω)),tT0. (3.21)

    We multiply (3.19) by u and have, noting that f(s)sc, c0, sR,

    u2chu(t)2+c. (3.22)

    Then, multipying (3.19) by Δu, we find, owing to (2.6),

    Δu2c(hu(t)2+u2). (3.23)

    We thus deduce from (3.21)(3.23) that

    u(t)2H2(Ω)ectQ(u0H2(Ω),α0H2(Ω),α1H1(Ω)),tT0, (3.24)

    and, thus, owing to (3.12)

    u(t)2H2(Ω)ectQ(u0H2(Ω),α0H2(Ω),α1H1(Ω)),t0. (3.25)

    Returning to (3.7), we have

    ddt(Δα2+αt2)Δαt2+ut2. (3.26)

    Noting that it follows from (3.4), (3.16) and (3.18) that

    tT0(Δαt2+ut2)dτectQ(u0H2(Ω),α0H2(Ω),α1H1(Ω)),tT0, (3.27)

    we finally deduce from (3.12) and (3.25)(3.27) that

    u(t)2H2(Ω)+α(t)2H2(Ω)+αt(t)2H1(Ω)ectQ(u0H2(Ω),α0H2(Ω),α1H1(Ω)),t0. (3.28)

    4. Existence and uniqueness of solutions

    We first have the following.

    Theorem 4.1. We assume that (2.5)(2.8) hold and (α0,α1)(H10(Ω)H2(Ω))×H10(Ω). Then, (2.1)(2.4) possesses at last one solution (u,α,αt) such that

    u,αL(0,T;H10(Ω)H2(Ω)),utL2(0,T;H1(Ω))andαtL(0,T;H10(Ω)).

    Proof. The proof is based on (3.28) and, e.g., a standard Galerkin scheme.

    We have, concerning the uniqueness, the following.

    Theorem 4.2. We assume that the assumptions of Theorem 4.1 hold. Then, the solution obtained in Theorem 4.1 is unique

    Proof. Let (u(1),α(1),α(1)t) and (u(2),α(2),α(2)t) be two solutions to (2.1)(2.3) with initial data (u(1)0,α(1)0,α(1)1) and (u(2)0,α(2)0,α(2)1), respectively. We set

    (u,α,αt)=(u(1),α(1),α(1)t)(u(2),α(2),α(2)t)

    and

    (u0,α0,α1)=(u(1)0,α(1)0,α(1)1)(u(2)0,α(2)0,α(2)1).

    Then, (u,α) satisfies

    ut+Δ2uΔ(f(u(1))f(u(2)))=Δαt, (4.1)
    2αt2Δα=ut, (4.2)
    u=α=0onΩ, (4.3)
    u|t=0=u0,α|t=0=α0,αt|t=0=α1. (4.4)

    We multiply (4.1) by (Δ)1ut and (4.2) by αt and have, summing the two resulting equations,

    ddt(u2+α2+αt2)+ut21(f(u(1))f(u(2)))2. (4.5)

    Furthermore,

    (f(u(1))f(u(2))=(10f(u(1)+s(u(2)u(1)))dsu)10f(u(1)+s(u(2)u(1)))dsu+10f(u(1)+s(u(2)u(1)))(u(1)+s(u(2)u(1)))dsuQ(u(1)0H2(Ω),u(2)0H2(Ω),α(1)0H1(Ω),α(2)0H1(Ω),α(1)1H1(Ω),α(2)1H1(Ω))×(u+|u||u(1)|+|u||u(2)|)Q(u(1)0H2(Ω),u(2)0H2(Ω),α(1)0H1(Ω),α(2)0H1(Ω),α(1)1H1(Ω),α(2)1H1(Ω))u. (4.6)

    We thus deduce from (4.5) and (4.6) that

    ddt(u2+α2+αt2)+ut21Q(u(1)0H2(Ω),u(2)0H2(Ω),α(1)0H1(Ω),α(2)0H1(Ω),α(1)1H1(Ω),α(2)1H1(Ω))u2. (4.7)

    In particular, we have a differential inequality of the form

    dE2dtQE2, (4.8)

    where

    E2=u2+α2+αt2

    satisfies

    E2c(u2H1(Ω)+α2H1(Ω)+αt2)c. (4.9)

    It follows from (4.8)(4.9) and Gronwall's lemma that

    u(t)2H1(Ω)+α(t)2H1(Ω)+αt(t)2ceQt(u02H1(Ω)+α02H1(Ω)+α12),t0, (4.10)

    hence the uniqueness, as well as the continuous dependence with respect to the initial data in the H1×H1×L2-norm.


    Acknowledgments

    The authors wish to thank the referees for their careful reading of the paper and useful comments.


    Conflict of interest

    All authors declare no conflicts of interest in this paper.




    [1] Aastveit KA, Bjørnland HC, Thorsrud LA (2015) What drives oil prices? Emerging versus developed economies. J Appl Econ 30: 1013–1028.
    [2] Aastveit KA, Natvik GJ, Sola S (2017) Economic uncertainty and the influence of monetary policy. J Int Money Finance 76: 50–67.
    [3] Alexopoulos M, Cohen J (2015) The power of print: Uncertainty shocks, markets, and the economy. Int Rev Econ Finance 40: 8–28.
    [4] Aloui R, Gupta R, Miller SM (2016) Uncertainty and crude oil returns. Energy Econ 55: 92–100.
    [5] Antonakakis N, Chatziantoniou I, Filis G (2014) Dynamic spillovers of oil price shocks and economic policy uncertainty. Energy Econ, 44: 433–447.
    [6] Aye G, Gupta R, Hammoudeh S, et al. (2015) Forecasting the price of gold using dynamic model averaging. Int Rev Financ Anal 41: 257–266.
    [7] Baker SR, Bloom N, Davis SJ (2016) Measuring economic policy uncertainty. Q J Econ 131: 1593–1636.
    [8] Balcilar M, Bekiros S, Gupta R (2017) The role of news-based uncertainty indices in predicting oil markets: A hybrid nonparametric quantile causality method. Empirical Econ 53: 879–889.
    [9] Balcilar M, Gupta R, Kyei C, et al. (2016) Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test. Open Econ Rev 27: 229–250.
    [10] Baumeister C, Kilian L (2015) Forecasting the real price of oil in a changing world: A forecast combination approach. J Bus Econ Stat 33: 338–351.
    [11] Baumeister C, Kilian L (2016) Forty years of oil price fluctuations: Why the price of oil may still surprise us. J Econ Perspect 30: 139–60.
    [12] Baumeister C, Peersman G (2013) The role of time‐varying price elasticities in accounting for volatility changes in the crude oil market. J Appl Econ 28: 1087–1109.
    [13] Berger T, Uddin GS (2016) On the dynamic dependence between equity markets, commodity futures and economic uncertainty indexes. Energy Econ 56: 374–383.
    [14] Bekiros S, Gupta R, Paccagnini A (2015) Oil price forecastability and economic uncertainty. Econ Lett 132: 125–128.
    [15] Bernal O, Gnabo JY, Guilmin G (2016) Economic policy uncertainty and risk spillovers in the Eurozone. J Int Money Finance 65: 24–45.
    [16] Bernardi M, Catania L (2016) Comparison of Value-at-Risk models using the MCS approach. Comput Stat 31: 579–608.
    [17] Bollerslev T, Engle RF, Wooldridge JM (1988) A capital asset pricing model with time-varying covariances. J Political Econ 96: 116–131.
    [18] Bordo MD, Duca JV, Koch C (2016a) Economic policy uncertainty and the credit channel: Aggregate and bank level US evidence over several decades. J Financ Stab 26: 90–106.
    [19] Bordo MD, Meissner CM (2016b) Fiscal and financial crises. NBER Working Paper, No. 22059.
    [20] Brogaard J, Detzel A (2015) The asset-pricing implications of government economic policy uncertainty. Manage Sci 61: 3–18.
    [21] Caggiano G, Castelnuovo E, Figueres JM (2017) Economic policy uncertainty and unemployment in the United States: A nonlinear approach. Econ Lett 151: 31–34.
    [22] Caporale GM, AliF M, Spagnolo N (2015) Oil price uncertainty and sectoral stock returns in China: A time-varying approach. China Econ Rev 34: 311–321.
    [23] Çolak G, Durnev A, Qian Y (2017) Political uncertainty and IPO activity: Evidence from US gubernatorial elections. J Financ Quant Anal 52: 2523–2564.
    [24] Cunado J, Jo S, de Gracia FP (2015) Macroeconomic impacts of oil price shocks in Asian economies. Energy Policy 86: 867–879.
    [25] Dai Y, Xie W, Jiang Z, et al. (2016) Correlation structure and principal components in the global crude oil market. Empirical Econ 51: 1501–1519.
    [26] Engle R (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20: 339–350.
    [27] Engle RF, Manganelli S (2004) CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. J Bus Econ Stat 22: 367–381.
    [28] Ferraty F, Quintela-Del-Río A (2016) Conditional VAR and Expected Shortfall: A New Functional Approach. Econ Rev 35: 263–292.
    [29] Ftiti Z, Guesmi K, Teulon F (2014) Oil shocks and Economic Growth in OPEC countries No. 2014–064.
    [30] Gao R, Zhang B (2016) How does economic policy uncertainty drive gold–stock correlations? Evidence from the UK. Appl Econ 48: 3081–3087.
    [31] Gkillas K, Katsiampa P (2018) An application of extreme value theory to cryptocurrencies. Econ Lett 164: 109–111.
    [32] Gong X, Wen F, Xia X, et al. (2017) Investigating the risk-return trade-off for crude oil futures using high-frequency data. Appl energy 196: 152–161.
    [33] Handley K, Limão N (2017) Policy uncertainty, trade, and welfare: Theory and evidence for China and the United States. Am Econ Rev 107: 2731–2783.
    [34] Jia X, An H, Fang W, et al. (2015) How do correlations of crude oil prices co-move? A grey correlation-based wavelet perspective. Energy Econ 49: 588–598.
    [35] Juvenal L, Petrella I (2015) Speculation in the oil market. J Appl Econ 30: 621–649.
    [36] Kang W, de Gracia FP, Ratti RA (2017) Oil price shocks, policy uncertainty, and stock returns of oil and gas corporations. J Int Money Finance 70: 344–359.
    [37] Kang W, Ratti RA (2015) Oil shocks, policy uncertainty and stock returns in China. Econ Transition 23: 657–676.
    [38] Kellogg R (2014) The effect of uncertainty on investment: Evidence from Texas oil drilling. Am Econ Rev 104: 1698–1734.
    [39] Laporta AG, L Merlo, Petrella L (2018) Selection of Value at Risk Models for Energy Commodities. Energy Econ 74: 628–643.
    [40] Li Z, Wang C, Nie P, et al. (2018) Green loan and subsidy for promoting clean production innovation. J Cleaner Prod 187: 421–431.
    [41] Li, X, Peng L (2017) US economic policy uncertainty and linkages between Chinese and US stock markets. Econ Modell 61: 27–39.
    [42] Li X, Ma J, Wang S, et al. (2015) How does Google search affect trader positions and crude oil prices? Econ Modell 49: 162–171.
    [43] Li Z, Dong H, Huang Z, et al. (2018) Asymmetric Effects on Risks of Virtual Financial Assets (VFAs) in different regimes: A Case of Bitcoin. Quant Finance Econ 2: 860–883.
    [44] Liu Z, Ye Y, Ma F, et al. (2017) Can economic policy uncertainty help to forecast the volatility: A multifractal perspective. Phys A: Stat Mech its Appl 482: 181–188.
    [45] Mensi W, Hammoudeh S, Shahzad SJH, et al. (2017) Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method. J Banking Finance 75: 258–279.
    [46] Narayan PK, Gupta R (2015) Has oil price predicted stock returns for over a century? Energy Economics 48: 18–23.
    [47] Naser H (2016) Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach. Energy Econ 56: 75–87.
    [48] Phan DHB, Sharma SS, Narayan PK (2015) Oil price and stock returns of consumers and producers of crude oil. J Int Finan Markets, Inst Money 34: 245–262.
    [49] Qureshi K (2016) Value-at-Risk: The Effect of Autoregression in a Quantile Process. arXiv preprint arXiv: 1605.04940.
    [50] Reboredo JC, Uddin GS (2016) Do financial stress and policy uncertainty have an impact on the energy and metals markets? A quantile regression approach. Int Rev Econ Finance 43: 284–298.
    [51] Sim N, Zhou H (2015) Oil prices, US stock return, and the dependence between their quantiles. J Banking Finance 55: 1–8.
    [52] Singleton KJ (2013) Investor flows and the 2008 boom/bust in oil prices. Manage Sci 60: 300–318.
    [53] Tsai IC (2017) The source of global stock market risk: A viewpoint of economic policy uncertainty. Econ Modell 60: 122–131.
    [54] Waisman M, Ye P, Zhu Y (2015) The effect of political uncertainty on the cost of corporate debt. J Finan Stab 16: 106–117.
    [55] Wang J, Wang J (2016) Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations. Energy 102: 365–374.
    [56] Wen F, Xiao J, Huang C, et al. (2018) Interaction between oil and US dollar exchange rate: Nonlinear causality, time-varying influence and structural breaks in volatility. Appl Econ 50: 319–334.
    [57] Wisniewski TP, Lambe BJ (2015) Does economic policy uncertainty drive CDS spreads? Int Rev Finan Anal 42: 447–458.
    [58] Yin L (2016) Does oil price respond to macroeconomic uncertainty? New evidence. Empirical Econ 51: 921–938.
    [59] You W, Guo Y, Zhu H, et al. (2017) Oil price shocks, economic policy uncertainty and industry stock returns in China: Asymmetric effects with quantile regression. Energy Econ 68: 1–18.
    [60] Zhang J, Zhang Y, Zhang L (2015) A novel hybrid method for crude oil price forecasting. Energy Econ 49: 649–659.
    [61] Zhang YJ, Zhang L (2015) Interpreting the crude oil price movements: Evidence from the Markov regime switching model. Appl Energy 143: 96–109.
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6411) PDF downloads(667) Cited by(34)

Article outline

Figures and Tables

Figures(4)  /  Tables(7)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog