پژوهش ها و چشم اندازهای اقتصادی

پژوهش ها و چشم اندازهای اقتصادی

مدلسازی ریسک سیستمی و وابستگی ساختاری بین بازارهای نفت و سهام :رویکرد ترکیبی تجزیه مود متغیر و کاپولا (VMD- Copula)

نوع مقاله : مقالات علمی پژوهشی

نویسندگان
ندا رحمانی خالط آباد 1
Farzaneh Haji hassani 2
کامبیز پیکارجو 2
1 دانشجوی دکترای گروه اقتصاد، دانشکده مدیریت و اقتصاد، واحد علوم تحقیقات ، دانشگاه آزاد اسلامی، تهران، ایران
2 استادیار گروه اقتصاد، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
10.48311/ecor.2025.116545.1003
چکیده
هدف اصلی از این مطالعه، بررسی وابستگی ساختاری بین بازدهی بازارهای نفت و شاخص بورس اوراق بهادار تهران با استفاده از داده­ های روزانه طی دوره 12 آگوست 2015 تا 12 مارس 2025 است. این مطالعه روش تجزیه مود متغیر (VMD) و انواع مختلف توابع کاپولای متقارن و نامتقارن را برای بررسی ساختار وابستگی بین بازارهای نفت و شاخص بورس در افق‌های متفاوت سرمایه‌گذاری ترکیب می‌کند. در مدل­سازی توزیع‌های حاشیه‌ای از الگوهای FIGARCH-GED استفاده شده است. نتایج مطالعه حاکی از آن است که بین بازدهی نفت و شاخص بورس ایران با استفاده از تابع کاپولای ارشمیدسی تابع کاپولای تی‌استیودنت، بهترین توضیح‌دهندگی را برای ساختار وابستگی آنها نشان می‌دهد. همچنین درکوتاه‌مدت و بلندمدت برای بازدهی‌ها، تابع کاپولای تی‌استیودنت بهترین توضیح‌دهندگی را برای ساختار وابستگی بین آنها نشان می‌دهد. این نتیجه، حاکی از این است که هر دو جفت از بازدهی، وابستگی به دنبالۀ بالایی و پایینی یکسانی دارند. لذا در بازدهی ­های مثبت و منفی حدی وابستگی بین هر دو، بیشتر از حالت معمولی است. این مهم نشان‌دهنده وجود سرایت در نوسانات این بازارها است. بدین ترتیب، می‌توان بیان داشت که در بازدهی ­های منفی و مثبت حدی بین بازدهی شاخص‌های مورد بررسی، وابستگی بیشتری رخ می‌دهد. همچنین از اندازه‌گیری‌های (CoVaR) و (ΔCoVaR) برای تعیین اثر نامتقارن سرریز ریسک در جهت صعودی و نزولی بین بازارهای سهام و نفت استفاده شد. نتایج حاصل از اثر سرایت ریسک بین بازارهای نفت و سهام، نشان‌دهنده سرریز ریسک نامتقارن بین بازارها است.
کلیدواژه‌ها
بازار نفت
تجزیه مود متغیر
شاخص سهام
سرریز ریسک
موضوعات

عنوان مقاله English

Modeling Systemic Risk and the Dependence Structure Between Oil and Stock Markets Using a VMD–Based Copula Approach

نویسندگان English

neda rahmany khalet abad 1
Farzaneh Haji hassani 2
kambiz peykarjou 2
1 Ph.D. Student Department of Economics Faculty of Management and Economics Science and Research Branch Islamic Azad University. Tehran. Iran
2 Assistant Professor, Department of Economics, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
چکیده English

Abstract
This study investigates the structural dependence and systemic risk transmission between crude oil (Brent) returns and the Tehran Stock Exchange Price Index (TEPIX) using daily observations over common trading days from August 12, 2015, to March 12, 2025. We propose a multiscale dependence framework that integrates Variational Mode Decomposition (VMD) with time-varying symmetric and asymmetric copula functions to capture nonlinear and tail-dependent co-movements across investment horizons. To model marginal dynamics and account for volatility clustering and long memory, return series are estimated using a generalized autoregressive conditional heteroskedasticity (GARCH) family specification, with the fractionally integrated generalized autoregressive conditional heteroskedasticity model with generalized error distribution (FIGARCH–GED) implemented in the empirical analysis. Dependence is evaluated through elliptical copulas (Gaussian and Student-t) and Archimedean copulas (Frank, Gumbel, and Clayton), thereby allowing for time variation and asymmetric tail behavior. Risk spillovers are quantified using conditional risk measures, including Value at Risk (VaR), Conditional Value at Risk (CoVaR), and ΔCoVaR, under both downside and upside market states to assess directional and asymmetric systemic risk transmission. The findings indicate that extreme market conditions intensify oil–equity dependence, suggesting that diversification benefits may deteriorate precisely when risk management is most critical
Purpose/Aims:
The primary objective of this study is to identify and quantify the dependence structure between oil returns and Iranian stock market returns and to determine whether this dependence varies across short-term and long-term investment horizons. A further objective is to assess whether the oil–stock linkage exhibits tail dependence, characterized by stronger co-movement during extreme positive or negative returns relative to normal market conditions.
Additionally, the study aims to measure asymmetric risk spillovers between the two markets by comparing downside and upside conditional risk transmission using CoVaR-based metrics. By employing systemic-risk-oriented measures, the paper seeks to provide empirical evidence relevant to portfolio diversification, hedging effectiveness, and policy oversight in an inflation-prone economy in which oil price dynamics exert substantial macro-financial influence.
Methodology & Framework:
The empirical framework consists of three integrated stages: marginal modeling, multiscale decomposition, and dependence and risk-spillover estimation.
First, each return series is modeled using a volatility specification drawn from the GARCH family. The FIGARCH–GED model is employed to capture heavy-tailed behavior and long-memory volatility dynamics in the marginal distributions.
Second, VMD is applied to decompose return dynamics into components representing short-run and long-run characteristics. This multiscale decomposition enables horizon-specific analysis of dependence patterns.
Third, the dependence structure is estimated using time-varying copula models, including Gaussian and Student-t copulas as well as Archimedean copulas (Frank, Gumbel, and Clayton). This specification accommodates both symmetric and asymmetric dependence and allows the strength of association to evolve over time.
Finally, VaR and CoVaR measures, including ΔCoVaR, are computed to evaluate how distress (downside) and expansionary (upside) conditions in one market alter the conditional risk exposure of the other market. This approach provides a structured assessment of directional systemic risk transmission.
Findings:
The empirical results indicate that the Student-t copula provides the best representation of the dependence structure between oil and stock returns across both short-term and long-term horizons. This finding suggests the presence of significant upper- and lower-tail dependence, implying strengthened co-movement during extreme positive and negative market outcomes.
The CoVaR-based analysis reveals asymmetric risk spillovers between the oil and stock markets. In particular, the reported downside-adjusted CoVaR differs from the upside CoVaR, indicating that the magnitude and direction of risk transmission vary across adverse and favorable market states. This asymmetric behavior carries important implications for hedge design and stress-period risk management.
Discussion:
The documented tail dependence indicates that oil and equity markets in Iran become more closely interconnected during turbulent or extreme periods, thereby weakening the effectiveness of conventional diversification strategies. The multiscale framework further demonstrates that dependence is horizon-dependent, meaning that short-term trading risk and long-term investment risk respond differently to oil price shocks and stock market fluctuations.
The use of time-varying copulas is particularly important because linear correlation measures may underestimate nonlinear and tail co-movements, especially during episodes of financial stress. The presence of asymmetric CoVaR spillovers further suggests that portfolio risk responds differently to adverse versus favorable oil market movements. Accordingly, risk management frameworks should incorporate distinct downside- and upside-oriented stress scenarios rather than assuming symmetric risk transmission.
In an economy exposed to inflationary pressures and oil-related macro-financial channels, these findings underscore the importance of monitoring oil price dynamics when formulating equity investment strategies and conducting regulatory risk assessments.
Conclusion & Implications:
This study concludes that the oil–stock relationship in Iran is characterized by nonlinear, horizon-dependent, and tail-relevant dependence, most effectively captured by the Student-t copula within the proposed VMD–copula framework. Since dependence intensifies during extreme market conditions, diversification between oil and equities may weaken during crises, thereby amplifying systemic risk when protection is most needed.
The identified asymmetric CoVaR spillovers indicate that hedging effectiveness is state-dependent and that risk control mechanisms should differentiate between downside and upside transmission channels. From a practical perspective, the proposed framework can assist portfolio managers in designing multihorizon allocation and hedging strategies that explicitly account for tail dependence and time variation rather than relying solely on average correlations. For policymakers and financial supervisors, the results highlight the importance of cross-market monitoring and stress testing frameworks that explicitly incorporate oil market conditions as a determinant of equity market risk.

کلیدواژه‌ها English

CoVaR
Crude Oil
Risk Spillover
Tail Dependence
TEPIX
Time-Varying Copula
VaR
VMD
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