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Spillover Effects Between Energy and Precious Metals Markets: A DCC-GARCH Approach Based on Wavelet Coherence Analysis

Year 2025, Volume: 33 Issue: 64, 557 - 585, 26.04.2025
https://doi.org/10.17233/sosyoekonomi.2025.02.24

Abstract

In this study, using daily return data between October 1, 2012, and June 4, 2024, the volatility spillovers between energy commodity markets and precious metal markets are investigated using the Dynamic Conditional Correlation approach based on wavelet coherence Analysis, which allows for the analysis of relationships between markets in time-frequency space. According to the findings of the wavelet coherence analysis, a long-run, mostly positive interdependence effect was observed from Brent oil returns to gold, silver, and platinum returns, as well as a long-run interdependence effect from palladium returns to natural gas returns. In the dynamic conditional correlation analyses for the identified long-run investment cycles, conditional correlation and volatility persistence findings are obtained for each investment cycle. The findings are significant for investors with long-term investment horizons.

Supporting Institution

Herhangi bir kurum tarafından desteklenmemiştir.

References

  • Akbulaev, N. (2023), “The impact of energy prices on precious metals: A comparison of the SARS-COV2 period and prior period”, International Journal of Energy Economics and Policy, 13(2), 433-440.
  • Alkhazali, O.M. & T.A. Zoubi (2020), “Gold and portfolio diversification: A stochastic dominance analysis of the Dow Jones Islamic indices”, Pacific-Basin Finance Journal, 60, 101264.
  • AlKulaib, Y. & F. Almudhaf (2012), “Does gold shine in the portfolio of a Kuwaiti investor?”, International Journal of Economics and Finance, 4(1), 160-166.
  • Arif, I. et al. (2019), “Relationship between oil price and white precious metals return: New evidence from quantile-on-quantile regression”, Pakistan Journal of Commerce and Social Sciences, 13(2), 515-528.
  • Aydoğdu, A. (2024), “Farklı Yatırım Ufuklarına Göre Kripto Para Birimlerinin Volatilite Modellemesi”, Doktora Tezi, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü, Denizli.
  • Aydoğdu, A. (2024), “Volatility spillovers of global economic policy uncertainty and fear index among cryptocurrencies: A wavelet-based DCC-GARCH approach”, Journal of Applied Microeconometrics, 4(1), 13-29.
  • Barro, R.J. et al. (2020), “The coronavirus and the Great Influenza Pandemic: Lessons from the “Spanish Flu” for the coronavirus’s potential effects on mortality and economic activity”, AEI Economics Working Paper, 2020-02.
  • Baur, D.G. & T.K. McDermott (2010), “Is gold a safe haven? International evidence”, Journal of Banking & Finance, 34(8), 1886-1898.
  • Bollerslev, T. (1990), “Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model”, The Review of Economics and Statistics, 72(3), 498-505.
  • Bouri, E. et al. (2017), “Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions”, Finance Research Letters, 23, 87-95
  • Chen, Y. & F. Qu (2019), “Leverage effect and dynamics correlation between international crude oil and China’s precious metals”, Physica A: Statistical Mechanics and its Applications, 534, 122319.
  • Cornish, C.R. et al. (2006), “Maximal overlap wavelet statistical analysis with application to atmospheric turbulence”, Boundary-Layer Meteorology, 119(2), 339-374.
  • Crowley, P.M. (2007), “A guide to wavelets for economists”, Journal of Economic Surveys, 21(2), 207-267.
  • Değirmenci, N. & Z. Abdioğlu (2017), “Finansal piyasalar arasındaki oynaklık yayılımı”, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 54, 104-125.
  • Demirer, R. et al. (2018), “Global risk aversion and emerging market return comovements”, Economics Letters, 173, 118-121.
  • Deniz, D. et al. (2018), “Kıymetli madenlerin portföy çeşitlendirmesine katkısı: Bist uygulaması”, Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 5(2), 366-382.
  • Dewandaru, G. et al. (2017), “Regional spillovers across transitioning emerging and frontier equity markets: A multi-time scale wavelet analysis”, Economic Modelling, 65, 30-40.
  • Diebold, F.X. & K. Yilmaz (2012), “Better to give than to receive: Predictive directional measurement of volatility spillovers”, International Journal of Forecasting, 28(1), 57-66.
  • Engle, R. (2002), “Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models”, Journal of Business & Economic Statistics, 20(3), 339-350.
  • Engle, R.F. & K. Sheppard (2001), “Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH”, NBER Working Paper No. w8554.
  • Erdoğan, S. et al. (2020), “Natural resource abundance, financial development and economic growth: An investigation on Next-11 countries”, Resources Policy, 65, 101559.
  • Ewing, B.T. & F. Malik (2013), “Volatility transmission between gold and oil futures under structural breaks”, International Review of Economics & Finance, 25, 113-121.
  • Gencay, R. et al. (2002), An introduction to wavelets and other filtering methods in finance and economics, Academic Press.
  • Goodell, J.W. & S. Goutte (2021), “Diversifying equity with cryptocurrencies during COVID-19”, International Review of Financial Analysis, 76, 101781.
  • Gorton, G. & K.G. Rouwenhorst (2006), “Facts and Fantasies about Commodity Futures”, Financial Analysts Journal, 62(2), 47-68.
  • Guiso, L. et al. (2018), “Time varying risk aversion”, Journal of Financial Economics, 128(3), 403-421.
  • Haq, I.U. & E. Bouri (2022), “Sustainable versus conventional cryptocurrencies in the face of cryptocurrency uncertainty indices: An analysis across time and scales”, Journal of Risk and Financial Management, 15(10), 442.
  • Jana, S. et al. (2023), “Revisiting the cryptocurrencies role in stock markets: ADCC-GARCH and wavelet coherence”, Macroeconomics and Finance in Emerging Market Economies, 17(1), 110-135.
  • Kamışlı, M. et al. (2017), “Emtia fiyatlari birbirlerini etkiler mi? Asimetrik frekans nedensellik analizi”, Uluslararası Yönetim İktisat ve İşletme Dergisi, 13(13), 1079-1093.
  • Kangallı-Uyar, S.G. (2021), “Uluslararası döviz piyasalarında finansal bulaşıcılık ve karşılıklı bağımlılık: Wavelet uyum analizi”, Finans Politik ve Ekonomik Yorumlar, (656), 115-147.
  • Kırkulak-Uludağ, B. & Z. Lkhamazhapov (2017), “Volatility dynamics of precious metals: Evidence from Russia”, Finance a Úvěr-Czech Journal of Economics and Finance, 4(67), 300-317.
  • Kumar, S. et al. (2022), “Volatility spillover among prices of crude oil, natural gas, exchange rate, gold, and stock market: Fresh evidence from exponential generalized autoregressive conditional heteroscedastic model analysis”, Journal of Public Affairs, 22(4), e2594.
  • Lehkonen, H. & K. Heimonen (2014), “Timescale-dependent stock market comovement: BRICS vs. developed markets”, Journal of Empirical Finance, 28, 90-103.
  • Mandacı, P.E. et al. (2020), “Dynamic connectedness and portfolio strategies: Energy and metal markets”, Resources Policy, 68, 101778.
  • Marín-Rodríguez, N.J. et al. (2023), “A wavelet analysis of the dynamic connectedness among oil prices, green bonds, and CO2 emissions”, Risks, 11(1), 15.
  • Mensi, W. et al. (2015), “Precious metals, cereal, oil and stock market linkages and portfolio risk management: Evidence from Saudi Arabia”, Economic Modelling, 51, 340-358.
  • Moralı, T. & U. Uyar (2018), “Kıymetli metaller piyasasının fraktal analizi”, Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 11(3), 2203-2218.
  • Phillips, P.C. & P. Perron (1988), “Testing for a unit root in time series regression”, Biometrika, 75(2), 335-346.
  • Rajwani, S. et al. (2023), “Dynamic linkages of energy commodities with bullion and metal market: Evidence of portfolio hedging”, American Business Review, 26(1), 148-179.
  • Ramsey, J. (2002), “Wavelets in economics and finance: Past and future”, Studies in Nonlinear Dynamics & Econometrics, 6(3), 1-27.
  • Reboredo, J.C. & M.A. Rivera-Castro (2013), “A wavelet decomposition approach to crude oil price and exchange rate dependence”, Economic Modelling, 32(1), 42-57.
  • Rehman, M.U. & X.V. Vo (2021), “Energy commodities, precious metals and industrial metal markets: A nexus across different investment horizons and market conditions”, Resources Policy, 70, 101843.
  • Rehman, M.U. et al. (2018), “Precious metal returns and oil shocks: A time varying connectedness approach”, Resources Policy, 58, 77-89.
  • Roll, R. (2013), “Volatility, correlation, and diversification in a multi-factor world”, The Journal of Portfolio Management, 39(2), 11-18.
  • Rua, A. & L. Nunes (2009), “International comovement of stock market returns: A wavelet analysis”, Journal of Empirical Finance, 16(4), 632-639.
  • Santana, T.P. et al. (2023), “Effects of interdependence and contagion on crude oil and precious metals according to ρDCCA: A COVID-19 case study”, Sustainability, 15(5), 3945.
  • Sertkaya, B. (2022), “Katılım endeksinin döviz kuru ve altın fiyatlarıyla ilişkisi: Türkiye için ARDL sınır testi yaklaşımı”, Bulletin of Economic Theory and Analysis, 7(1), 173-188.
  • Shafiullah, M. et al. (2021), “Quantile causality and dependence between crude oil and precious metal prices”, International Journal of Finance & Economics, 26(4), 6264-6280.
  • Temel, F. & H. Güneş (2022), “Emtialarda haftanın günü anomalisinin EGARCH modeli ile test edilmesi”, içinde: M.F. Buğan (ed.), Finansal piyasaların evrimi: Davranışsal finans, İslami finans, FinTech (87-103), Orion Kitapevi.
  • Torrence, C. & G.P. Compo (1998), “A Practical Guide to Wavelet Analysis”, Bulletin of the American Meteorological Society, 79(1), 61-78.
  • Torrence, C. & P.J. Webster (1999), “Interdecadal changes in the ENSO-monsoon system”, Journal of Climate, 12(8), 2679-2690.
  • Trabelsi, N. et al. (2021), “Effects of price of gold on Bombay Stock Exchange sectoral indices: New evidence for portfolio risk management”, Research in International Business and Finance, 55, 101316.
  • Tse, Y.K. & A.K.C. Tsui (2002), “A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations”, Journal of Business & Economic Statistics, 20(3), 351-362.
  • Uyar, U. & S. Kangallı-Uyar (2021), “Sermaye ve altın piyasaları arasındaki yayılım etkisi: Wavelet’e dayalı dinamik koşullu korelasyon yaklaşımı”, içinde: M. Ural & Ü. Aydın (eds.), Finansal ekonometri uygulamaları: Kavram, teori, uygulama (309-335), Seçkin Yayıncılık.
  • Vacha, L. & J. Baruník (2012), “Comovement of energy commodities revisited: Evidence from wavelet coherence analysis”, Energy Economics, 34(1), 241-247.
  • Xu, N.R. (2019), “Global risk aversion and international return comovements”, SSRN, https://dx.doi.org/10.2139/ssrn.3174176
  • Yang, L. et al. (2016), “Interdependence of foreign exchange markets: A wavelet coherence analysis”, Economic Modelling, 55, 6-14.
  • Yaya, O.S. et al. (2016), “Volatility persistence and returns spillovers between oil and gold prices: Analysis before and after the global financial crisis”, Resources Policy, 49, 273-281.
  • Yaya, O.S. et al. (2022), “Time variation between metal commodities and oil, and the impact of oil shocks: GARCH-MIDAS and DCC-MIDAS analyses”, Resources Policy, 79, 103036.
  • Yıldırım, D.Ç. et al. (2018), “Regime-dependent effect of crude oil price on BRICS stock markets”, Emerging Markets Finance and Trade, 54(8), 1706-1719.
  • Yıldırım, D.Ç. et al. (2020), “Time-varying volatility spillovers between oil prices and precious metal prices”, Resources Policy, 68, 101783.
  • Yoon, S.M. et al. (2019), “Network connectedness and net spillover between financial and commodity markets”, The North American Journal of Economics and Finance, 48, 801-818.
  • Zhang, C. et al. (2018), “The effect of global oil price shocks on China’s precious metals market: A comparative analysis of gold and platinum”, Journal of Cleaner Production, 186, 652-661.

Enerji ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı

Year 2025, Volume: 33 Issue: 64, 557 - 585, 26.04.2025
https://doi.org/10.17233/sosyoekonomi.2025.02.24

Abstract

Bu çalışmada 01.10.2012-04.06.2024 tarihleri arasındaki günlük getiri verileri kullanılarak enerji emtia piyasaları ile kıymetli metal piyasaları arasındaki volatilite yayılımı, zaman-frekans uzayında piyasalar arasındaki ilişkilerin incelenmesine olanak sağlayan Wavelet Uyum Analizi Dayalı Dinamik Koşullu Korelasyon yaklaşımı ile araştırılmıştır. Wavelet uyum analizi bulgularına göre, Brent petrol getirilerinden altın, gümüş ve platin getirilerine doğru uzun dönemli ve çoğunlukla pozitif; paladyum getirisinden doğal gaz getirilerine doğru ise uzun dönemli karşılıklı bağımlılık etkisi tespit edilmiştir. Belirlenen uzun dönemli yatırım döngüleri için uygulanan dinamik koşullu korelasyon analizlerinde ise, her bir yatırım döngüsü için koşullu korelasyon ve volatilite kalıcılığı bulgularına ulaşılmıştır. Elde edilen bulgular, özellikle uzun vadeli yatırım ufkuna sahip yatırımcılar için önem arz etmektedir.

References

  • Akbulaev, N. (2023), “The impact of energy prices on precious metals: A comparison of the SARS-COV2 period and prior period”, International Journal of Energy Economics and Policy, 13(2), 433-440.
  • Alkhazali, O.M. & T.A. Zoubi (2020), “Gold and portfolio diversification: A stochastic dominance analysis of the Dow Jones Islamic indices”, Pacific-Basin Finance Journal, 60, 101264.
  • AlKulaib, Y. & F. Almudhaf (2012), “Does gold shine in the portfolio of a Kuwaiti investor?”, International Journal of Economics and Finance, 4(1), 160-166.
  • Arif, I. et al. (2019), “Relationship between oil price and white precious metals return: New evidence from quantile-on-quantile regression”, Pakistan Journal of Commerce and Social Sciences, 13(2), 515-528.
  • Aydoğdu, A. (2024), “Farklı Yatırım Ufuklarına Göre Kripto Para Birimlerinin Volatilite Modellemesi”, Doktora Tezi, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü, Denizli.
  • Aydoğdu, A. (2024), “Volatility spillovers of global economic policy uncertainty and fear index among cryptocurrencies: A wavelet-based DCC-GARCH approach”, Journal of Applied Microeconometrics, 4(1), 13-29.
  • Barro, R.J. et al. (2020), “The coronavirus and the Great Influenza Pandemic: Lessons from the “Spanish Flu” for the coronavirus’s potential effects on mortality and economic activity”, AEI Economics Working Paper, 2020-02.
  • Baur, D.G. & T.K. McDermott (2010), “Is gold a safe haven? International evidence”, Journal of Banking & Finance, 34(8), 1886-1898.
  • Bollerslev, T. (1990), “Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model”, The Review of Economics and Statistics, 72(3), 498-505.
  • Bouri, E. et al. (2017), “Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions”, Finance Research Letters, 23, 87-95
  • Chen, Y. & F. Qu (2019), “Leverage effect and dynamics correlation between international crude oil and China’s precious metals”, Physica A: Statistical Mechanics and its Applications, 534, 122319.
  • Cornish, C.R. et al. (2006), “Maximal overlap wavelet statistical analysis with application to atmospheric turbulence”, Boundary-Layer Meteorology, 119(2), 339-374.
  • Crowley, P.M. (2007), “A guide to wavelets for economists”, Journal of Economic Surveys, 21(2), 207-267.
  • Değirmenci, N. & Z. Abdioğlu (2017), “Finansal piyasalar arasındaki oynaklık yayılımı”, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 54, 104-125.
  • Demirer, R. et al. (2018), “Global risk aversion and emerging market return comovements”, Economics Letters, 173, 118-121.
  • Deniz, D. et al. (2018), “Kıymetli madenlerin portföy çeşitlendirmesine katkısı: Bist uygulaması”, Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 5(2), 366-382.
  • Dewandaru, G. et al. (2017), “Regional spillovers across transitioning emerging and frontier equity markets: A multi-time scale wavelet analysis”, Economic Modelling, 65, 30-40.
  • Diebold, F.X. & K. Yilmaz (2012), “Better to give than to receive: Predictive directional measurement of volatility spillovers”, International Journal of Forecasting, 28(1), 57-66.
  • Engle, R. (2002), “Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models”, Journal of Business & Economic Statistics, 20(3), 339-350.
  • Engle, R.F. & K. Sheppard (2001), “Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH”, NBER Working Paper No. w8554.
  • Erdoğan, S. et al. (2020), “Natural resource abundance, financial development and economic growth: An investigation on Next-11 countries”, Resources Policy, 65, 101559.
  • Ewing, B.T. & F. Malik (2013), “Volatility transmission between gold and oil futures under structural breaks”, International Review of Economics & Finance, 25, 113-121.
  • Gencay, R. et al. (2002), An introduction to wavelets and other filtering methods in finance and economics, Academic Press.
  • Goodell, J.W. & S. Goutte (2021), “Diversifying equity with cryptocurrencies during COVID-19”, International Review of Financial Analysis, 76, 101781.
  • Gorton, G. & K.G. Rouwenhorst (2006), “Facts and Fantasies about Commodity Futures”, Financial Analysts Journal, 62(2), 47-68.
  • Guiso, L. et al. (2018), “Time varying risk aversion”, Journal of Financial Economics, 128(3), 403-421.
  • Haq, I.U. & E. Bouri (2022), “Sustainable versus conventional cryptocurrencies in the face of cryptocurrency uncertainty indices: An analysis across time and scales”, Journal of Risk and Financial Management, 15(10), 442.
  • Jana, S. et al. (2023), “Revisiting the cryptocurrencies role in stock markets: ADCC-GARCH and wavelet coherence”, Macroeconomics and Finance in Emerging Market Economies, 17(1), 110-135.
  • Kamışlı, M. et al. (2017), “Emtia fiyatlari birbirlerini etkiler mi? Asimetrik frekans nedensellik analizi”, Uluslararası Yönetim İktisat ve İşletme Dergisi, 13(13), 1079-1093.
  • Kangallı-Uyar, S.G. (2021), “Uluslararası döviz piyasalarında finansal bulaşıcılık ve karşılıklı bağımlılık: Wavelet uyum analizi”, Finans Politik ve Ekonomik Yorumlar, (656), 115-147.
  • Kırkulak-Uludağ, B. & Z. Lkhamazhapov (2017), “Volatility dynamics of precious metals: Evidence from Russia”, Finance a Úvěr-Czech Journal of Economics and Finance, 4(67), 300-317.
  • Kumar, S. et al. (2022), “Volatility spillover among prices of crude oil, natural gas, exchange rate, gold, and stock market: Fresh evidence from exponential generalized autoregressive conditional heteroscedastic model analysis”, Journal of Public Affairs, 22(4), e2594.
  • Lehkonen, H. & K. Heimonen (2014), “Timescale-dependent stock market comovement: BRICS vs. developed markets”, Journal of Empirical Finance, 28, 90-103.
  • Mandacı, P.E. et al. (2020), “Dynamic connectedness and portfolio strategies: Energy and metal markets”, Resources Policy, 68, 101778.
  • Marín-Rodríguez, N.J. et al. (2023), “A wavelet analysis of the dynamic connectedness among oil prices, green bonds, and CO2 emissions”, Risks, 11(1), 15.
  • Mensi, W. et al. (2015), “Precious metals, cereal, oil and stock market linkages and portfolio risk management: Evidence from Saudi Arabia”, Economic Modelling, 51, 340-358.
  • Moralı, T. & U. Uyar (2018), “Kıymetli metaller piyasasının fraktal analizi”, Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 11(3), 2203-2218.
  • Phillips, P.C. & P. Perron (1988), “Testing for a unit root in time series regression”, Biometrika, 75(2), 335-346.
  • Rajwani, S. et al. (2023), “Dynamic linkages of energy commodities with bullion and metal market: Evidence of portfolio hedging”, American Business Review, 26(1), 148-179.
  • Ramsey, J. (2002), “Wavelets in economics and finance: Past and future”, Studies in Nonlinear Dynamics & Econometrics, 6(3), 1-27.
  • Reboredo, J.C. & M.A. Rivera-Castro (2013), “A wavelet decomposition approach to crude oil price and exchange rate dependence”, Economic Modelling, 32(1), 42-57.
  • Rehman, M.U. & X.V. Vo (2021), “Energy commodities, precious metals and industrial metal markets: A nexus across different investment horizons and market conditions”, Resources Policy, 70, 101843.
  • Rehman, M.U. et al. (2018), “Precious metal returns and oil shocks: A time varying connectedness approach”, Resources Policy, 58, 77-89.
  • Roll, R. (2013), “Volatility, correlation, and diversification in a multi-factor world”, The Journal of Portfolio Management, 39(2), 11-18.
  • Rua, A. & L. Nunes (2009), “International comovement of stock market returns: A wavelet analysis”, Journal of Empirical Finance, 16(4), 632-639.
  • Santana, T.P. et al. (2023), “Effects of interdependence and contagion on crude oil and precious metals according to ρDCCA: A COVID-19 case study”, Sustainability, 15(5), 3945.
  • Sertkaya, B. (2022), “Katılım endeksinin döviz kuru ve altın fiyatlarıyla ilişkisi: Türkiye için ARDL sınır testi yaklaşımı”, Bulletin of Economic Theory and Analysis, 7(1), 173-188.
  • Shafiullah, M. et al. (2021), “Quantile causality and dependence between crude oil and precious metal prices”, International Journal of Finance & Economics, 26(4), 6264-6280.
  • Temel, F. & H. Güneş (2022), “Emtialarda haftanın günü anomalisinin EGARCH modeli ile test edilmesi”, içinde: M.F. Buğan (ed.), Finansal piyasaların evrimi: Davranışsal finans, İslami finans, FinTech (87-103), Orion Kitapevi.
  • Torrence, C. & G.P. Compo (1998), “A Practical Guide to Wavelet Analysis”, Bulletin of the American Meteorological Society, 79(1), 61-78.
  • Torrence, C. & P.J. Webster (1999), “Interdecadal changes in the ENSO-monsoon system”, Journal of Climate, 12(8), 2679-2690.
  • Trabelsi, N. et al. (2021), “Effects of price of gold on Bombay Stock Exchange sectoral indices: New evidence for portfolio risk management”, Research in International Business and Finance, 55, 101316.
  • Tse, Y.K. & A.K.C. Tsui (2002), “A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations”, Journal of Business & Economic Statistics, 20(3), 351-362.
  • Uyar, U. & S. Kangallı-Uyar (2021), “Sermaye ve altın piyasaları arasındaki yayılım etkisi: Wavelet’e dayalı dinamik koşullu korelasyon yaklaşımı”, içinde: M. Ural & Ü. Aydın (eds.), Finansal ekonometri uygulamaları: Kavram, teori, uygulama (309-335), Seçkin Yayıncılık.
  • Vacha, L. & J. Baruník (2012), “Comovement of energy commodities revisited: Evidence from wavelet coherence analysis”, Energy Economics, 34(1), 241-247.
  • Xu, N.R. (2019), “Global risk aversion and international return comovements”, SSRN, https://dx.doi.org/10.2139/ssrn.3174176
  • Yang, L. et al. (2016), “Interdependence of foreign exchange markets: A wavelet coherence analysis”, Economic Modelling, 55, 6-14.
  • Yaya, O.S. et al. (2016), “Volatility persistence and returns spillovers between oil and gold prices: Analysis before and after the global financial crisis”, Resources Policy, 49, 273-281.
  • Yaya, O.S. et al. (2022), “Time variation between metal commodities and oil, and the impact of oil shocks: GARCH-MIDAS and DCC-MIDAS analyses”, Resources Policy, 79, 103036.
  • Yıldırım, D.Ç. et al. (2018), “Regime-dependent effect of crude oil price on BRICS stock markets”, Emerging Markets Finance and Trade, 54(8), 1706-1719.
  • Yıldırım, D.Ç. et al. (2020), “Time-varying volatility spillovers between oil prices and precious metal prices”, Resources Policy, 68, 101783.
  • Yoon, S.M. et al. (2019), “Network connectedness and net spillover between financial and commodity markets”, The North American Journal of Economics and Finance, 48, 801-818.
  • Zhang, C. et al. (2018), “The effect of global oil price shocks on China’s precious metals market: A comparative analysis of gold and platinum”, Journal of Cleaner Production, 186, 652-661.
There are 63 citations in total.

Details

Primary Language Turkish
Subjects Capital Market, Financial Economy
Journal Section Articles
Authors

Aslan Aydoğdu 0000-0001-9732-0614

Umut Uyar 0000-0001-6217-8283

Early Pub Date April 14, 2025
Publication Date April 26, 2025
Submission Date August 5, 2024
Acceptance Date March 20, 2025
Published in Issue Year 2025 Volume: 33 Issue: 64

Cite

APA Aydoğdu, A., & Uyar, U. (2025). Enerji ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı. Sosyoekonomi, 33(64), 557-585. https://doi.org/10.17233/sosyoekonomi.2025.02.24
AMA Aydoğdu A, Uyar U. Enerji ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı. Sosyoekonomi. April 2025;33(64):557-585. doi:10.17233/sosyoekonomi.2025.02.24
Chicago Aydoğdu, Aslan, and Umut Uyar. “Enerji Ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı”. Sosyoekonomi 33, no. 64 (April 2025): 557-85. https://doi.org/10.17233/sosyoekonomi.2025.02.24.
EndNote Aydoğdu A, Uyar U (April 1, 2025) Enerji ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı. Sosyoekonomi 33 64 557–585.
IEEE A. Aydoğdu and U. Uyar, “Enerji ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı”, Sosyoekonomi, vol. 33, no. 64, pp. 557–585, 2025, doi: 10.17233/sosyoekonomi.2025.02.24.
ISNAD Aydoğdu, Aslan - Uyar, Umut. “Enerji Ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı”. Sosyoekonomi 33/64 (April 2025), 557-585. https://doi.org/10.17233/sosyoekonomi.2025.02.24.
JAMA Aydoğdu A, Uyar U. Enerji ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı. Sosyoekonomi. 2025;33:557–585.
MLA Aydoğdu, Aslan and Umut Uyar. “Enerji Ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı”. Sosyoekonomi, vol. 33, no. 64, 2025, pp. 557-85, doi:10.17233/sosyoekonomi.2025.02.24.
Vancouver Aydoğdu A, Uyar U. Enerji ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı. Sosyoekonomi. 2025;33(64):557-85.