<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ASDS</journal-id><journal-title-group><journal-title>Applied Statistics and Data Science</journal-title></journal-title-group><issn>3066-8433</issn><eissn>3066-8441</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ASDS.2025090017</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>波动溢出网络视角下的金融风险管理研究</title><url>https://artdesignp.com/journal/ASDS/1/9/10.61369/ASDS.2025090017</url><author>张欣</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>1</volume><issue>9</issue><history><date date-type="pub"><published-time>2025-11-20</published-time></date></history><abstract>在全球经济高度金融化与不确定性上升的背景下，跨市场风险传染已成为影响金融稳定的关键因素。本文构建国际大宗商品市场与中国金融市场的波动溢出网络，从系统视角研究不同市场间的风险联动格局与传导机制。实证样本覆盖2007&amp;mdash;2025年，包含中国货币、资本、商品、外汇、房地产及黄金市场共十个子市场，以及国际能源、贵金属、工业金属、农产品、软性商品与家畜市场。研究结果表明，系统总体溢出指数为31.97%，约三分之一的波动可由跨市场风险传递解释，显示出显著的系统性风险关联。风险在系统中呈现&amp;ldquo;国际输入&amp;mdash; 国内传导&amp;mdash; 防御吸收&amp;rdquo;的层级扩散结构：国际贵金属、能源与工业金属市场是主要外部风险源，中国金属与能源市场处于风险传导核心位置，而股票与回购利率市场构成风险再分配通道；黄金与债券市场则作为最终吸收端发挥稳定作用。本文进一步提出，应构建跨市场风险监测体系，完善宏观审慎监管框架，并优化避险资产配置结构，以提升我国金融体系的抗冲击能力与稳健性。</abstract><keywords>波动溢出网络,系统性金融风险,国际大宗商品市场,金融风险传染</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]隋建利, 杨庆伟. 国际大宗商品市场与中国金融市场间风险的传染测度与来源追溯 [J]. 财经研究, 2021, 47(08): 139-54.[2]DIEBOLD F X, YILMAZ K. Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets [J]. The Economic Journal, 2009, 119(534): 158-71.[3]DIEBOLD F X, YILMAZ K. Better to give than to receive: Predictive directional measurement of volatility spillovers [J]. International Journal of Forecasting, 2012, 28(1): 57-66.[4]DIEBOLD F X, YILMAZ K. On the network topology of variance decompositions: Measuring the connectedness of financial firms [J]. Journal of Econometrics, 2014, 182(1): 119-34.[5]PAPIEŻ M, RUBASZEK M, SZAFRANEK K, et al. Are European natural gas markets connected? A time-varying spillovers analysis [J]. Resources Policy, 2022, 79.[6]李湛, 尧艳珍, 汤怀林, et al. 中国金融系统风险溢出效应研究&amp;mdash;&amp;mdash;基于溢出指数和波动溢出网络 [J]. 南方经济, 2021, (12): 80-92.[7]JUST M, ECHAUST K. Dynamic spillover transmission in agricultural commodity markets: What has changed after the COVID-19 threat? [J]. Economics Letters, 2022, 217.[8]MENSI W, REBOREDO J C, UGOLINI A, et al. Switching connectedness between real estate investment trusts, oil, and gold markets [J]. Finance Research Letters, 2022, 49.[9]HACHICHA N, BEN AMAR A, BEN SLIMANE I, et al. Dynamic connectedness and optimal hedging strategy among commodities and financial indices [J]. International Review of Financial Analysis, 2022, 83.[10]KOSE M A. Explaining business cycles in small open economies [J]. Journal of International Economics, 2002, 56(2): 299-327.[11]CHAMBERS M J, BAILEY R E. A theory of commodity price fluctuations [J]. Journal of Political Economy, 1996, 104(5): 924-57.[12]TROLLE A B, SCHWARTZ E S. Unspanned stochastic volatility and the pricing of commodity derivatives [J]. The Review of Financial Studies, 2009, 22(11): 4423-61.[13]KNITTEL C R, PINDYCK R S. The simple economics of commodity price speculation [J]. American Economic Journal: Macroeconomics, 2016, 8(2): 85-110.[14]SOCKIN M, XIONG W. Informational frictions and commodity markets [J]. The Journal of Finance, 2015, 70(5): 2063-98.[15]DE V. CAVALCANTI T V, MOHADDES K, RAISSI M. Commodity price volatility and the sources of growth [J]. Journal of Applied Econometrics, 2015, 30(6): 857-73.[16]龙少波, 常婧. 开放条件下国内大宗商品价格影响模型与货币政策的非对称效应&amp;mdash;&amp;mdash;基于开放套利模型与非对称自回归分布滞后模型 [J]. 国际金融研究, 2019, (11): 12-23.[17]FERN&amp;aacute;NDEZ A, GONZ&amp;aacute;LEZ A, RODRIGUEZ D. Sharing a ride on the commodities roller coaster: Common factors in business cycles of emerging economies [J]. Journal of International Economics, 2018, 111: 99-121.[18]LOPEZ-MARTIN B, LEAL J, FRITSCHER A M. Commodity price risk management and fiscal policy in a sovereign default model [J]. Journal of International Money and Finance, 2019, 96: 304-23.[19]ALQUIST R, BHATTARAI S, COIBION O. Commodity-price comovement and global economic activity [J]. Journal of Monetary Economics, 2020, 112: 41-56.[20]刘华军, 陈明华, 刘传明, et al. 中国大宗商品价格溢出网络结构及动态交互影响 [J]. 数量经济技术经济研究, 2017, 34(1): 113-29.[21]GLASSERMAN P, YOUNG H P. Contagion in Financial Networks [J]. Journal of Economic Literature, 2016, 54(3): 779-831.[22]ANDRIOSOPOULOS K, GALARIOTIS E, SPYROU S. Contagion, volatility persistence and volatility spill-overs: The case of energy markets during the European financial crisis [J]. Energy Economics, 2017, 66: 217-27.[23]ACEMOGLU D, OZDAGLAR A, TAHBAZ-SALEHI A. Systemic Risk and Stability in Financial Networks [J]. American Economic Review, 2015, 105(2): 564-608.[24]BENOIT S, COLLIARD J-E, HURLIN C, et al. Where the Risks Lie: A Survey on Systemic Risk* [J]. Review of Finance, 2016, 21(1): 109-52.[25]杨立生, 杨杰. 国际大宗商品价格波动对中国金融市场的风险溢出效应&amp;mdash;&amp;mdash;波动溢出网络视角 [J]. 金融监管研究, 2022, (08): 58-77.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
