تحلیل پویایی بازدهی ETFها تحت تأثیر متغیرهای کلان اقتصادی و رفتار سرمایه‌گذاران: رویکرد مدل GMM-PVAR

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای مهندسی مالی، دانشکده مدیریت، اقتصاد و حسابداری، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران

2 دانشیار، گروه مدیریت، دانشکده مدیریت، اقتصاد و حسابداری، دانشگاه آزاد اسلامی، کرج، ایران

3 استادیار، گروه مدیریت، دانشکده مدیریت، اقتصاد و حسابداری، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران

چکیده

این پژوهش به تحلیل پویایی بازدهی ETFها تحت تأثیر متغیرهای اقتصاد کلان و رفتار سرمایه‌گذاران با استفاده از مدل PVAR و رویکرد GMM می‌پردازد.
داده‌های ماهانه دوره ۱۳۹۲ تا ۱۴۰۲ به‌کار گرفته شده و متغیرهای کلان (نرخ بهره بین‌بانکی، تورم، قیمت طلا، نرخ ارز) و متغیرهای رفتاری (احساسات سرمایه‌گذاران و ریسک‌گریزی) در چارچوب مدل خودرگرسیون برداری ساختاری پنلی تحلیل شده‌اند.
نتایج توابع واکنش آنی نشان می‌دهد شوک مثبت ریسک‌گریزی موجب افت فوری بازده ETFها شده، در حالی که شوک احساسات سرمایه‌گذاران اثری تقویتی و پایدار دارد؛ همچنین نرخ بهره فشار نزولی معناداری بر بازده وارد می‌کند و ETFها نیز در انتقال شوک‌ها نقش فعال دارند، به‌گونه‌ای که افزایش بازده آن‌ها موجب کاهش ریسک‌گریزی و تقویت احساسات می‌شود و متغیرهای کلان نیز واکنش‌های مشخصی از جمله رفتار تدافعی قیمت طلا، واکنش کوتاه‌مدت تورم و روند افزایشی نرخ ارز نشان می‌دهند.
نتایج FEVD بیانگر افزایش وابستگی متقابل متغیرها در افق بلندمدت و نقش برجسته نرخ بهره، ریسک‌گریزی و بازده ETF در توضیح نوسانات سایر متغیرهاست که بر ماهیت پویا و درون‌زای روابط بین عوامل رفتاری، متغیرهای کلان و بازده ETFها و اهمیت ETFها به‌عنوان کانالی برای انتقال شوک‌های مالی تأکید می‌کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Dynamic Analysis of ETF Returns under the Influence of Macroeconomic Variables and Investor Behavior: A GMM-PVAR Approach

نویسندگان [English]

  • Nima Keramat 1
  • Seyed Fakhreddin Fakhrehosseini 2
  • Mahmod khoddam 3
  • Meysam Kaviani 3
1 PhD student in financial engineering, Department of Management, Economics and Accounting, Ka. C, Islamic Azad University, Karaj, Iran
2 Department of Management, Economics and Accounting, Ka. C, Islamic Azad University, Karaj, Iran
3 Assistant Professor, Department of Management, Economics and Accounting, Ka. C, Islamic Azad University, Karaj, Iran
چکیده [English]

This study analyzes the dynamics of ETF returns in response to macroeconomic variables and investor behavior using a Panel Vector Autoregression (PVAR) model with a Generalized Method of Moments (GMM) estimator.
Monthly data for the period 2013–2023 are employed. Macroeconomic variables (interbank interest rate, inflation, gold price, and exchange rate) and behavioral variables (investor sentiment and risk aversion) are analyzed within a structural panel vector autoregression (PVAR) framework.
Impulse-response function results indicate that a positive shock to risk aversion leads to an immediate decline in ETF returns, whereas a shock to investor sentiment has a strengthening, persistent effect. The interest rate also exerts a significant negative pressure on returns. Furthermore, ETFs play an active role in shock transmission, such that increases in ETF returns reduce risk aversion and strengthen investor sentiment. Macroeconomic variables also exhibit distinct responses, including defensive behavior in gold prices, short-term reactions in inflation, and an upward trend in exchange rates.
FEVD results indicate increasing long-run interdependence among variables and highlight the prominent roles of interest rates, risk aversion, and ETF returns in explaining fluctuations in other variables. These findings emphasize the dynamic and endogenous nature of the relationships between behavioral factors, macroeconomic variables, and ETF returns, as well as the importance of ETFs as a channel for financial shock transmission

کلیدواژه‌ها [English]

  • Macroeconomics
  • behavioral finance
  • return dynamics
  • ETF
  • PVAR

مراجع

  1. Abrigo, Michael R. M., and Inessa Love. 2016. Estimation of panel vector autoregression in Stata. The Stata Journal 16: 778–804.
  2. Ahmadi, N., Hamidian, M., & Baghani, A. (2025). Presenting a Stock Market Model based on Risk Appetite, Investor Sentiment, and Monetary Policy Shock Using Smooth Transition Regression (STR). Financial Management Strategy, 13(3), 191-210 (In Persian).
  3. Alomari, M., Selmi, R., Mensi, W., Ko, H. U., & Kang, S. H. (2024). Dynamic spillovers in higher moments and jumps across ETFs and economic and financial uncertainty factors in the context of successive shocks. The Quarterly Review of Economics and Finance, 93, 210-228.
  4. Alvarez, J., & Arellano, M. (2003). The time series and cross‐section asymptotics of dynamic panel data estimators. Econometrica, 71(4), 1121-1159.‏
  5. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680
  6. Baker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment. Journal of Financial Economics, 104(2), 272–287.
  7. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636.‏
  8. Balcilar, M., Roubaud, D., Uzuner, G., & Wohar, M. E. (2021). Housing sector and economic policy uncertainty: a GMM panel VAR approach. International Review of Economics & Finance, 76, 114-126.‏
  9. Blume, L., Easley, D., & O'hara, M. (1994). Market statistics and technical analysis: The role of volume. The journal of finance, 49(1), 153-181.‏
  10. Brana, S., & Prat, S. (2016). The effects of global excess liquidity on emerging stock market returns: Evidence from a panel threshold model. Economic Modelling, 52, 26-34.‏
  11. Brown, G. W., & Cliff, M. T. (2005). Investor sentiment and asset valuation. The journal of Business, 78(2), 405-440.‏
  12. Cepni, O., Dul, W., Gupta, R., & Wohar, M. E. (2021). The dynamics of US REITs returns to uncertainty shocks: A proxy SVAR approach. Research in International Business and Finance, 58, 101433.‏
  13. Dahmene, M., Boughrara, A., & Slim, S. (2021). Nonlinearity in stock returns: Do risk aversion, investor sentiment and, monetary policy shocks matter? International Review of Economics & Finance, 71, 676-699.
  14. Datar, M. K. (2000). Stock market liquidity: Measurement and implications. In Proceedings of the 4th Capital Market Conference.
  15. Demirdogen, Y., & Kaplan, F. (2020). The Relationship between Participation Funds and Macroeconomic Variables: SVAR Analysis Approach. Turkish Journal of Islamic Economics, 7(2).‏
  16. Harris, M., & Raviv, A. (1993). Differences of opinion make a horse race. The Review of Financial Studies, 6(3), 473-506.‏
  17. Hsieh, C. H., Chang, J. R., & Chen, H. H. (2025). Compounding Effects in Leveraged ETFs: Beyond the Volatility Drag Paradigm. arXiv preprint arXiv:2504.20116.‏
  18. Hump, C., & McMillan, D. G. (2014). Investor sentiment and stock return predictability in the UK. International Review of Financial Analysis, 32, 99–108.
  19. Judson, R. A., & Owen, A. L. (1999). Estimating dynamic panel data models: a guide for macroeconomists. Economics letters, 65(1), 9-15.‏
  20. Kaminskyi, A., Nehrey, M., & Rizun, N. (2020). The impact of COVID-induced shock on the risk-return correspondence of agricultural ETFs. CEUR Workshop Proceedings.
  21. Kaviani, M., Fakhrehosseini, S. F., & Jafari, M. (2025). Analysis of feedback Trading of exchange-traded funds with emphasis on price Premium and price Discount in the Tehran Stock Exchange. Financial Research Journal[In Press] (In Persian).
  22. Kaviani, M., Gavara, M., & Agharokh, S. (2024). The Reaction of the Tehran Stock Market to Monetary Shock, Risk Aversion and Investor Sentiments; The Approach Of TR And ARDL Economic Policies and Research, 3(1), 90-110 (In Persian).
  23. Kaviani, M., & Aghaee, M. S. (2023). Analysis of the refinement fund's portfolio from start-up to sectorfund; comparison of theory with practice. Financial and Banking Strategic Studies, 1(3), 167-177 (In Persian).
  24. Kaviani, M., Gavara, M., & Nazari, Z. (2024). An Analysis of the Volatility and Return Spillover of Venture Capital ETFs in the Tehran Stock Exchange. Advances in Finance and Investment, 5(1) (In Persian).
  25. Lee, C. M., Shleifer, A., & Thaler, R. H. (1991). Investor sentiment and the closed‐end fund puzzle. The journal of finance, 46(1), 75-109.‏
  26. Li, J., & Patel, D. (2025). Volatility Dynamics and Spillovers in Cryptocurrency Markets: Evidence from the Bitcoin ETF Approval. NYU Stern School of Business Research Paper.‏
  27. Mehdiabadi, M., & Mohammadipour, R. (2019). Determining the nonlinear effect of the money market interest rate on the Tehran stock exchange by the means of generalized autoregressive conditional heteroskedasticity (GARCH) model and smooth transition regression (STR) model.‏ Journal of Financial Engineering and Securities Management, 10(40), 126-151 (In Persian).
  28. Mehregan, N., & Ahmadi Ghomi, M. A. (2016). Exchange rate shocks and financial markets: An application of panel vector autoregression model (Panel VAR). Journal of Economic Research and Policies, 23(75), 103-130. (In Persian).
  29. Nandal, S., & Kumar, A. (2021). Risk and Return Dynamics of ETF and Index Funds: A Review of Existing Literature. Journal of Management Research and Analysis, 8(3), 145–151.
  30. Pefqeli, L. (2023). US Monetary Policy and Capital Flow to Equity, Bond, and Commodities
  31. Rajabian, M., Sabahi, A., Lotfalipour, M. R., & Behnameh, M. (2018). The Effect of Macroeconomic Stability Indices Shocks on “TEPIX” by Bayesian VAR Model Approach. Economic Growth and Development Research, 9(33), 79-90 (In Persian).
  32. shaerattar, M., & Mirzapour babajan, A. (2021). The effect of underlying asset shocks on the Gold exchange traded funds’ pricing deviation. Financial Knowledge of Securities Analysis, 14(51), 97-110 (In Persian).
  33. shaerattar, M., & Mirzapourbabajan, A. (2020). Gold Exchange Traded Fund : Price Discovery and Performance Analysis. Financial Engineering and Portfolio Management, 11(44), 426-445 (In Persian)
  34. Shenjere, P. A., Ferreira-Schenk, S., & Moodley, F. (2025). Does investor sentiment influence South African ETF flows during different market conditions?. Economies, 13(1), 10.‏
  35. Werner, J. (2018). Risk aversion. In The new Palgrave dictionary of economics (pp. 11728-11733). Palgrave Macmillan, London.‏
  36. Zhao, M., & Park, H. (2024). Research on the dynamic interrelationship between economic policy uncertainty and stock market returns. Journal of Risk and Financial Management, 17(8), 347.
نظریه های کاربردی اقتصاد
دوره 13، شماره 2 - شماره پیاپی 49
شماره تابستان سال 1405
خرداد 1405
صفحه 195-216

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