The impact of risk aversion on dynamic conditional correlations across markets: Application of DCC-EGARCH and quantile regression

Year 2024, Volume: 10 Issue: 3, 416 - 430, 30.10.2024

Özge Dinç Cavlak , Ecenur Uğurlu Yıldırım

https://doi.org/10.30855/gjeb.2024.10.3.007

Abstract

Market integration is a frequently examined phenomenon that significantly influences investors' strategies and decision-making processes. In this context, investors' risk aversion plays a crucial role in the relationships across markets; unexpected shocks and panic can affect investor behavior, leading to market comovement. Based on this premise, this study examines the dynamic correlations between developed, emerging, and U.S. markets using EGARCH and DCC-EGARCH models, while analyzing the impact of risk aversion using a quantile regression approach. The findings reveal that while correlations between developed and U.S. markets exhibit high volatility, the correlations between developed and emerging markets are low. Additionally, the study finds that the time-varying conditional correlations between the U.S. and emerging markets are also quite low, suggesting potential benefits for portfolio diversification. Finally, the impact of risk aversion on the dynamic correlations between the three different market indices is examined using the quantile regression method; with results indicating that as quantile levels increase, the effect of risk aversion on dynamic conditional correlations also increases.

Keywords

Market Integration, Risk Aversion, DCC-EGARCH, Quantile Regression

Riskten kaçınma düzeyinin piyasalar arası dinamik koşullu korelasyonlar üzerindeki etkisi: DCC-EGARCH ve kantil regresyon uygulaması

Abstract

Piyasa entegrasyonu, yatırımcıların stratejilerini ve karar alma süreçlerini önemli ölçüde etkileyen bir olgu olarak sıklıkla incelenmektedir. Bu bağlamda yatırımcıların riskten kaçınma düzeyleri piyasalar arası ilişkiler üzerinde etkili rol oynamakta; beklenmedik şoklar ve panik, yatırımcı davranışını etkileyerek piyasaların birlikte hareket etmesine yol açabilmektedir. Buradan hareketle mevcut çalışma, gelişmiş, gelişmekte olan ve ABD piyasaları arasındaki dinamik korelasyonları EGARCH ve DCC-EGARCH modelleri ile inceleyerek riskten kaçınmanın etkisini kantil regresyon yaklaşımıyla analiz etmektedir. Araştırmanın bulguları, gelişmiş piyasalar ile ABD piyasaları arasındaki korelasyonların yüksek seviyelerde olduğunu; buna karşın, gelişmiş piyasalar ile gelişmekte olan piyasalar arasındaki korelasyonların düşük olduğunu göstermiştir. Ayrıca, ABD ile gelişmekte olan piyasalar arasındaki zamana bağlı koşullu korelasyonların da oldukça düşük olduğu sonucuna varılmış, bu bulgunun portföy çeşitlendirmesi bağlamında değerlendirilebileceği ortaya konmuştur. Son olarak, riskten kaçınmanın üç farklı piyasa endeksi arasındaki dinamik korelasyonlar üzerindeki etkisi, kantil regresyon yöntemi kullanılarak incelenmiş; bulgular, kantil dilimleri arttıkça riskten kaçınma düzeyinin dinamik koşullu korelasyonlar üzerindeki etkisinin de yükseldiğini göstermektedir.

Keywords

Piyasa Entegrasyonu, Riskten Kaçınma, DCC-EGARCH, Kantil Regresyon

References

• Adekoya, O. B., Oliyide, J. A. ve Oduyemi, G. O. (2021). How COVID-19 upturns the hedging potentials of gold against oil and stock markets risks: nonlinear evidences through threshold regression and markov-regime switching models. Resources Policy, 70, 101926. Doi: 10.1016/j.resourpol.2020.101926
• Adrian, T. ve Shin, H. S. (2013). Procyclical leverage and value-at-risk. The Review of Financial Studies 27(2), 373-403. Doi: https://doi.org/10.1093/rfs/hht068
• Barro, R. J., Ursúa, J. F. ve Weng, J. (2020). The Coronavirus and the Great Influenza Pandemic: Lessons From the “Spanish Flu” for the Coronavirus’s Potential Effects on Mortality and Economic Activity. NBER Working Paper, WP. Doi: https://doi.org/10.3386/w26866. No. 26866.
• Bai, L., Wei, Y., Wei, G., Li, X. ve Zhang, S. (2020). Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective. Financial Research Letters, 40, 101709. Doi: https://doi.org/10.1016/j.frl.2020.101709
• Baker, M. ve Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61, 1645-1680. Doi: https://doi.org/10.1111/j.1540-6261.2006.00885.x
• Bakshi, G. ve Wu, L. (2010). The behavior of risk and market prices of risk over the Nasdaq bubble period. Management Science, 56, 2251-2264. Doi: 10.1287/mnsc.1100.1256
• Bekaert, G., Engstrom, E. ve Xing, Y. (2009). Risk, uncertainty, and asset prices. Journal of Financial Economics, 91, 59-82. Doi:10.1016/j.jfineco.2008.01.005
• Bekaert, G., Engstrom, E. C. ve Xu, N. R. (2019). The Time Variation in Risk Appetite and Uncertainty. NBER Working Paper No. w25673, Available at SSRN: https://ssrn.com/abstract=3359430
• Bekaert, G., Hoerova, M. ve Duca, M. L. (2013). Risk, uncertainty and monetary policy. Journal of Monetary Economics, 60(7), 771-788. Doi: 10.1016/j.jmoneco.2013.06.003
• Bernoth, K. ve Erdogan, B. (2012). Sovereign bond yield spreads: A time-varying coefficient approach. Journal of International Money and Finance, 31, 639-656. Doi: https://doi.org/10.1016/j.jimonfin.2011.10.006
• Bıyıklı, S. İ. (2022). COVID-19 pandemisinin seçili dünya borsaları üzerindeki etkisi. Finansal Araştırmalar ve Çalışmalar Dergisi, 14(27), 309-323. Doi: 10.14784/marufacd.1148493
• Campbell, R., Koedijk, K. ve Kofman, P. (2002). Increased correlation in bear markets. Financial Analysts Journal, 58, 87-94. Doi: https://doi.org/10.2469/faj.v58.n1.2512
• Cappiello, L., Engle, R. F. ve Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537-572. Doi: https://doi.org/10.1093/jjfinec/nbl005
• Chen, Y., Li, W. ve Jin, X. (2018). Volatility spillovers between crude oil prices and new energy stock price in China. Romanian Journal of Economic Forecasting, 21(2), 43-62.
• Cheng, F. Yang, S. ve Zhou K. (2020). Quantile partial adjustment model with application to predicting energy demand in China. Energy, 191, 116519. Doi: 10.1016/j.energy.2019.116519
• Çamurlu, S. ve Erilli, N. (2019). Kantil regresyon analizinde bootstrap tahmini. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 35(2), 16-25.
• Dai, Z. ve Chang, X. (2021). Forecasting stock market volatility: Can the risk aversion measure exert an important role? North American Journal of Economics and Finance, 58, 101510. Doi: 10.1016/j.najef.2021.101510
• Degirmenci, N. ve Abdioglu, Z. (2017). Volatility spillover between financial markets. Dumlupınar University Journal of Social Sciences, 54, 104-125. Doi: 10.1108/S1569-375920190000101003
• Demirer, R. ve Jategaonkar, S. (2020). Time-varying risk aversion and the profitability of momentum trades. Applied Finance Letters, 9, 43-54. Doi: 10.24135/afl.v9i0.193
• Demirer, R., Omay, T., Yuksel, A. ve Yuksel, A. (2018). Global Risk Aversion and Emerging Market Return Comovements. Economics Letters, 173, 118-121. Doi: 10.1016/j.econlet.2018.09.027
• Dickey, D. A. ve Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072. Doi: https://doi.org/10.2307/1912517
• Dimitriou, D., Kenourgios, D. ve Simos, T. (2013). Global Financial Crises and Emerging Stock Market Contagion: A Multivariate FIAPARCH-DCC Approach. International Review of Financial Analysis, 30, 46-56. Doi: https://doi.org/10.1016/j.irfa.2013.05.008
• Dinç-Cavlak, Ö. (2024). Sürdürülebilir hisse senedi endekslerinin DCC-GARCH modeli ile incelenmesi ve petrol fiyatlarının bu ilişkiye etkisi. KOCATEPEİİBFD, 26(1), 48-58. Doi: https://doi.org/10.33707/10.33707/akuiibfd.1335551
• Do, A., Powell, R., Yong, J. ve Singh, A. (2020). Time-varying asymmetric volatility spillover between global markets and China’s A, B and H-shares using EGARCH and DCC-EGARCH models. The North American Journal of Economics and Finance, 54, 101096. Doi: https://doi.org/10.1016/j.najef.2019.101096
• Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157-168. Doi: 10.1257/jep.15.4.157
• 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. Doi: 10.1198/073500102288618487
• Fassas, A. P. (2020). Risk aversion connectedness in developed and emerging equity markets before and after the COVID-19 pandemic. Heliyon, 6(12). Doi: https://doi.org/10.1016/j.heliyon.2020.e05715
• Forbes, K. ve Rigobon, R. (2002). No Contagion, Only Interdependence: Measuring Stock Market Co-movements. The Journal of Finance, 57, 2223-2261. Doi: DOI: 10.1111/0022-1082.00494
• Hao, L. ve Naiman, D. Q. (2007). Quantile Regression (No. 149). Thousand Oaks: SAGE Publications, Inc.
• He, Z. ve Krishnamurthy, A. (2013). Intermediary asset pricing. American Economic Review, 103, 73270. Doi: 10.1257/aer.103.2.732
• Jebran, K., Chen, S., Ullah, I. ve Mirza, S.S. (2017). Does volatility spillover among stock markets varies from normal to turbulent period? Evidence from emerging markets of Asia. The Journal of Finance and Data Science, 3, 20-30. Doi: https://doi.org/10.1016/j.jfds.2017.06.001
• Ji, X., Wang, S., Xiao, H., Bu, N. ve Lin, X. (2022). Contagion Effect of Financial Markets in Crisis: An Analysis Based on the DCC-MGARCH Model. Mathematics,10,1819. Doi: https://doi.org/10.3390/math10111819
• Kayral, İ. E. ve Tandoğan, N. Ş. (2020). BİST100, döviz kurları ve altının getiri ve volatilitesinde COVID-19 etkisi. Gaziantep University Journal of Social Sciences, Special Issue: 687-701. Doi: https://doi.org/10.21547/jss.786384
• Kearney, C. ve Lucey, B. M. (2004). International equity market integration: Theory, evidence and implications. International Review of Financial Analysis, 13(5), 571-583. Doi: https://doi.org/10.1016/j.irfa.2004.02.013
• Kocaarslan, B., Sari, R., Gormus, A. ve Soytas, U. (2017). Dynamic correlations between BRIC and US stock markets: The asymmetric impact of volatility expectations in oil, gold and financial markets. Journal of Commodity Markets, 7, 41-56. Doi: https://doi.org/10.1016/j.jcomm.2017.08.001
• Koenker, R. W. ve D'Orey, V. (1987). Algorithm AS 229: Computing regression quantiles. Applied statistics, 36(3), 383-393. Doi:10.2307/2347802
• Koenker, R. ve Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 33-50. Doi: https://doi.org/10.2307/1913643
• Koenker, R. ve Hallock, K. F. (2001). Quantile regression. Journal of economic perspectives, 15(4), 143-156. Doi: 10.1257/jep.15.4.143
• Koutmos, G. (2012). Modeling interest rate volatility: an extended EGARCH approach. Managerial Finance, 38(6), 628-635. Doi: 10.1108/03074351211226265
• Lemmon, M. ve Portniaguina, E. (2006). Consumer confidence and asset prices: Some empirical evidence. The Review of Financial Studies, 19, 1499-1529. Doi: 10.2139/ssrn.335240
• Longin, F. ve Solnik, B. (2001). Extreme Correlation of International Equity Market. The Journal of Finance, 56, 649-676. Doi: https://doi.org/10.1111/0022-1082.00340
• Mensi, W., Hammoudeh, S., Nguyen, D. K. ve Hoon, S. (2016). Global Financial Crisis and Spillover Effects among the U.S. and BRICS Stock Markets. International Review of Economics and Finance, 42, 257-276. Doi: 10.1016/j.iref.2015.11.005
• Miranda-Agrippino, S. ve Rey, H. (2015). World asset markets and the global financial cycle. CEPR Discussion Paper No. DP10936, Available at SSRN: https://ssrn.com/abstract=2691541.
• Morales, L. ve Andreosso-O’Callaghan, B. (2012). The current global financial crisis: Do Asian stock markets show contagion or interdependence effects? Journal of Asian Economics, 23, 616-626. Doi: https://doi.org/10.1016/j.asieco.2012.09.002
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the econometric society, 347-370. Doi: https://doi.org/10.2307/2938260
• Nitoi, M. ve Pochea, M. M. (2019). What drives European Union stock market co-movements? Journal of International Money and Finance, 97, 57-69. Doi: 10.1016/j.jimonfin.2019.06.004
• Pan, J. (2002). The jump-risk premia implicit in options: Evidence from an integrated time-series study. Journal of financial economics, 63, 3-50. Doi: https://doi.org/10.1016/S0304-405X(01)00088-5
• Phillips, P. C. ve Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. Doi: https://doi.org/10.2307/2336182
• Rey, H. (2018). Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence. NBER Working Paper: 21162.
• Syllignakis, M. N. ve Kouretas, G.P. (2011). Dynamic correlation analysis of financial contagion: evidence from the Central and Eastern European markets. International Review of Economics and Finance, 20, 717-732. Doi: https://doi.org/10.1016/j.iref.2011.01.006
• Tareghian, R. ve Rasmussen, P. F. (2013). Statistical downscaling of precipitation using quantile regression. Journal of hydrology, 487, 122-135. Doi: https://doi.org/10.1016/j.jhydrol.2013.02.029
• Ugurlu-Yildirim, E. (2021). Covid-19 Pandemi ve 2008 Ekonomik Kriz Dönemlerinde Riskten Kaçınma Düzeyinin ABD ve BRIC Piyasa Entegrasyonu Üzerindeki Değişen Etkisi. Muhasebe ve Finansman Dergisi, 90, 185-208. Doi: https://doi.org/10.25095/mufad.866684
• Xu, N. R. (2019). Global risk aversion and international return comovements. Available at SSRN 3174176. Doi: 10.2139/ssrn.3174176
• Xu, B. ve Lin, B. (2018). What Cause Large Regional Differences in PM2.5 Pollutions in China? Evidence from Quantile Regression Model. Journal of Cleaner Production, 174, 447-461. Doi: https://doi.org/10.1016/j.jclepro.2017.11.008
• Xu, B. ve Lin, B. (2020). Investigating drivers of CO2 emission in China’s heavy industry: A quantile regression analysis. Energy, 206, 118159. Doi: 10.1016/j.energy.2020.118159
• Yıldırım, D. C., Esen, Ö. ve Ertuğrul, H. M. (2022). Impact of the COVID-19 pandemic on return and risk transmission between oil and precious metals: Evidence from DCC-GARCH model. Resources Policy, 79, 102939. Doi: 10.1016/j.resourpol.2022.102939