The Impact of Investor Sentiment (VIX) and Sovereign Credit Risk (CDS) on Stock Market Sector Indices: An Application of the Quantile-on-Quantile Regression Method

Abstract

This study examines the effects of investor sentiment, represented by the Volatility Index (VIX), and sovereign credit risk, reflected by the five-year CDS premium, on two benchmark indices (XU100, XU030) and eight sectoral indices (XBANK, XUSIN, XUHIZ, XUTEK, XHOLD, XULAS, XUMAL, XKMYA) listed on Borsa Istanbul. The Quantile-on-Quantile Regression method is applied using daily data from January 2015 to May 2025. The findings reveal that both indicators have statistically significant and varying impacts on index returns not only at the mean level but also across different quantiles of the return distribution. The effect of VIX is found to be predominantly negative and strong during high-volatility periods, whereas some sectors display positive pricing behavior under low-volatility conditions. In the case of CDS premiums, the results vary by sector, with notable positive responses at higher quantiles, particularly in XBANK, XULAS, and XHOLD indices. These findings demonstrate that the effects of risk indicators emerge in a heterogeneous and asymmetric manner, depending on the market regime and sectoral structure. It is concluded that investor behavior is sensitive to market conditions, and accounting for sector-specific vulnerabilities is essential for effective policy formulation and portfolio management.

Keywords

• Investor sentiment (VIX)
• sovereign credit default swap (CDS) premium
• quantile-on-quantile regression
• sectoral indices
• financial market volatility

Yatırımcı Duyarlılığı ve Ülke Kredi Riskinin Borsa Sektör Endekslerine Etkisi: Kantil-Kantil Regresyon Yöntemi Uygulaması

Öz

Bu çalışmada, yatırımcı duyarlılığını temsil eden volatilite endeksi (VIX) ile ülke kredi riskini yansıtan beş yıllık CDS primi kullanılarak, Türkiye’de Borsa İstanbul’daki iki gösterge endeks (XU100, XU030) ve sekiz sektör endeksi (XBANK, XUSIN, XUHIZ, XUTEK, XHOLD, XULAS, XUMAL, XKMYA) üzerindeki etkiler incelenmiştir. Ocak 2015–Mayıs 2025 dönemine ait günlük verilerle Kantil-Kantil Regresyon yöntemi uygulanmıştır. Bulgular, her iki göstergenin de endeks getirileri üzerinde sadece ortalama düzeyde değil, dağılımın farklı kantillerinde de anlamlı ve değişken etkiler yarattığını ortaya koymuştur. VIX’in etkisi özellikle yüksek volatilite dönemlerinde negatif yönlü ve güçlü olurken, düşük volatilite koşullarında bazı sektörlerde pozitif fiyatlamalar gözlenmiştir. CDS priminde ise sektöre göre farklılık gösteren etkiler tespit edilmiş, özellikle XBANK, XULAS ve XHOLD endekslerinde yüksek kantillerde pozitif tepki öne çıkmıştır. Elde edilen bulgular, piyasa rejimine ve sektörel yapıya bağlı olarak risk göstergelerinin etkilerinin heterojen ve asimetrik biçimde ortaya çıktığını göstermektedir. Sonuç olarak, yatırımcı davranışlarının piyasa koşullarına duyarlı olduğu ve sektörel kırılganlıkların dikkate alınmasının politika ve portföy yönetimi açısından önem taşıdığı sonucuna varılmıştır.

Anahtar Kelimeler

• Yatırımcı duyarlılığı (VIX)
• ülke kredi risk primi (CDS)
• kantil-kantil regresyon
• sektör endeksleri
• finansal piyasa volatilitesi

Kaynakça

1. Abdelmalek, W. (2022). Investor Sentiment, realized volatility and stock returns. Review of Behavioral Finance, 14(5), 668–700. https://doi.org/10.1108/rbf-12-2020-0301.
2. Acharya, V. V., & Johnson, T. C. (2007). Insider-trading in credit derivatives. Journal of Financial Economics, 84(1), 110–141. https://doi.org/10.1016/j.jfineco.2006.05.003
3. Ahmad, W., Hernandez, J.A., Saini, S., & Mishra, R.K. (2021). The US equity sectors, implied volatilities, and COVID-19: What does the spillover analysis reveal?. Resources Policy, 72, 102102. https://doi.org/10.1016/j.resourpol.2021.102102
4. Akgüneş, A. O. (2021). VIX endeksinde meydana gelen değişmelerin BIST endeksleri üzerine etkisi: ARDL sınır testi yaklaşımı. Yönetim ve Ekonomi Araştırmaları Dergisi, 19(1), 237-252. https://doi.org/10.11611/yead.877076
5. Alexander, C., & Imeraj, A. (2021). The bitcoin VIX and its variance risk premium. Journal of Alternative Investments, 23(4), 84–109. https://doi.org/10.3905/jai.2020.1.112
6. Aloui, C., Shahzad, S., Hkiri, B., Hela, B., & Khan, M. (2021). On the investors’ sentiments and the Islamic stock-bond interplay across investments’ horizons. Pacific-Basin Finance Journal, 65. https://doi.org/10.1016/j.pacfin.2020.101491
7. Badshah, I., Bekiros, S., Lucey, B.M., & Uddin, G.S. (2018). Asymmetric linkages among the fear index and emerging market volatility indices. Emerging Markets Review, 37, 17-31. https://doi.org/10.1016/j.ememar.2018.03.002
8. Bağcı, B., Çıtak, F., & Şişman, M. Y. (2020). Koronavirüs pandemisinin havayolu şirketlerinin hisse senetleri üzerine etkisi: Kantil-Kantil regresyon modeli uygulaması. Gaziantep Üniversitesi Sosyal Bilimler Dergisi, 19(COVID-19 Special Issue), 429-446. https://doi.org/10.21547/jss.782043

9. Bardgett, C., Gourier, E., & Leippold, M. (2019). Inferring volatility dynamics and risk premia from the S&P 500 and VIX markets. Journal of Financial Economics, 131(3), 593–618. https://doi.org/10.1016/j.jfineco.2018.09.010
10. Borensztein, E., Cowan, K., & Valenzuela, P. (2013). Sovereign ceilings “Lite”?: The impact of sovereign ratings on corporate ratings. J. Bank. Financ., 37(11), 4014-4024. https://doi.org/10.1016/j.jbankfin.2013.07.006
11. Bossman, A., Gherghina, Ş. C., Asafo-Adjei, E., Adam, A. M., & Agyei, S. K. (2022). Exploring the asymmetric effects of economic policy uncertainty and implied volatilities on energy futures returns: Novel insights from quantile-on-quantile regression. Journal of Business Economics and Management, 23(6). https://doi.org/10.3846/jbem.2022.18282
12. Bossman, A., Gubareva, M., & Teplova, T. (2023). EU sectoral stocks amid geopolitical risk, market sentiment, and crude oil implied volatility: An asymmetric analysis of the Russia-Ukraine tensions. Resources Policy. 82, 103515. https://doi.org/10.1016/j.resourpol.2023.103515
13. Broock, W. A., Dechert, W. D., Scheinkman, J. A. ve LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197–235. https://doi.org/10.1080/07474939608800353
14. Cavallo, E., & Valenzuela, P. (2010). The determinants of corporate risk in emerging markets: an option-adjusted spread analysis. Int. J. Financ. Econ., 15(1), 59-74 https://doi.org/10.1002/ijfe.398
15. Chau, F., Deesomsak, R., & Koutmos, D. (2016). Does investor sentiment really matter? International Review of Financial Analysis, 48, 221-232 https://doi.org/10.1016/j.irfa.2016.10.003
16. Chen, B., & Sun, Y. (2022). The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods. The North American Journal of Economics and Finance, 63, 101831. https://doi.org/10.1016/j.najef.2022.101831
17. Chudik, A., & Fratzscher, M. (2011). Identifying the global transmission of the 2007-2009 financial crisis in a GVAR model. European Economic Review, 55(3), 325-339. https://doi.org/10.1016/j.euroecorev.2010.12.003
18. Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. https://doi.org/10.1080/01621459.1979.10481038
19. Çetinkaya, Y., & Üçler. G. (2024). Kredi temerrüt takas primi (CDS) ve korku endeksinin (VIX) Borsa İstanbul endeksleri üzerine etkisi. Necmettin Erbakan Üniversitesi Siyasal Bilgiler Fakültesi Dergisi, 6(2), 402-419. https://doi.org/10.51124/jneusbf.2024.95
20. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.1080/01621459.1979.10482531
21. Hashmi, S. M., Chang, B. H., & Rong, L. (2021). Asymmetric effect of COVID-19 pandemic on E7 stock indices: Evidence from quantile-on-quantile regression approach. Research in International Business and Finance, 58, 101485. https://doi.org/10.1016/j.ribaf.2021.101485
22. Fender, I., Hayo, B., & Neuenkirch, M. (2012). Daily pricing of emerging market sovereign CDS before and during the global financial crisis. Journal of Banking & Finance, 36(10), 2786-2794. https://doi.org/10.1016/j.jbankfin.2012.06.017
23. Giot, P. (2005). Implied volatility indexes and daily value at risk models. Journal of Derivatives, 12(4), 54–64. https://doi.org/10.3905/jod.2005.517186
24. Gunay, S., & Can, G. (2022). The source of financial contagion and spillovers: an evaluation of the covid pandemic and the global financial crisis. PloS ONE. 17(1). https://doi.org/10.1371/journal.pone.0261835
25. Jurkowska, A., Ozcelebi, O., & Fijorek, K. (2024). VIX, sectoral volatility and financial contagion in emerging markets: TVP-VAR analysis for the Fragile Five. Economic Systems, 48(1), 101084. https://doi.org/10.1016/j.ecosys.2023.101084
26. Kang, S. H., Hernandez, J.A., Rehman, M.U., Shahzad, S.J.H., & Yoon, S.M. (2023). Spillovers and hedging between US equity sectors and gold, oil, Islamic stocks and implied volatilities. Resources Policy, 81, 103286. https://doi.org/10.1016/j.resourpol.2022.103286
27. Kurt Cihangir, Ç. (2020). Volatility spillover effects from global and national variables to sovereign CDS spreads: evidence from Turkey. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 11(26), 45-61. https://doi.org/10.21076/vizyoner.654420
28. Longstaff, F.A., Pan, J., Pedersen, L.H., & Singleton, K.J. (2011). How sovereign is sovereign credit risk? American Economic Journal: Macroeconomics, 3 (2), 75–103. https://doi.org/10.1257/mac.3.2.75
29. Önem, H. B. (2022). Döviz kurları ve CDS primi oynaklığının BIST endekslerine yayılım etkisi. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 6(2), 274-293. https://doi.org/10.31200/makuubd.1117597
30. Pazarcı, Ş., Kar, A., Kılıç, E., & Umut, A. (2022). Türkiye’de borsa, döviz kuru, CDS primi ve VIX endeksi ilişkisinin ampirik analizi. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 24(3), 1090-1103. https://doi.org/10.32709/akusosbil.1084718
31. Shahzad, S.J.H., Aloui, C., & Jammazi, R. (2020). On the interplay between US sectoral CDS, stock and VIX indices: fresh insights from wavelet approaches. Financ. Res. Lett., 33, 101208. https://doi.org/10.1016/j.frl.2019.06.006
32. Sim, N., & Zhou, H. (2015). Oil prices, US stock return and the dependence between their quantiles. Journal of Banking & Finance. 55, 1–8. https://doi.org/10.1016/j.jbankfin.2014.03.005
33. Smales, L. A. (2022). Spreading the fear: the central role of CBOE VIX in global stock market uncertainty. Global Finance Journal, 51, 100679. https://doi.org/10.1016/j.gfj.2021.100679
34. Song, J. H., Zhang, Z. P., & So, M. K. P. (2021) On the predictive power of network statistics for financial risk indicators. Journal of International Financial Markets, Institutions and Money, 75. https://doi.org/10.1016/j.intfin.2021.101420
35. Sönmez, Y., Baydaş, Y., & Kılıç, E. (2023). CDS primleri ile seçili BIST endeksleri arasındaki volatilite yayılımı. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 64, 29-34. https://doi.org/10.18070/erciyesiibd.1173962
36. Stone, C. J. (1977) Consistent nonparametric regression. The Annals of Statistics, 5(4), 595–620. https://doi.org/10.1214/aos/1176343886
37. Şahinler, A. N. (2025) CDS primleri ve BİST100 endeksi arasında risk durumunda nedensellik ilişkisi. Iğdır Üniversitesi Sosyal Bilimler Dergisi, 39, 109-128. https://doi.org/10.54600/igdirsosbilder.1617731
38. Şenol, Z. (2021) Borsa endeksi, döviz kuru, faiz oranları ve CDS primleri arasındaki oynaklık yayılımları: Türkiye örneği. Business and Economics Research Journal, 12(1), 111-126. http://dx.doi.org/10.20409/berj.2021.313
39. Whaley, R. E. (2000) The investor fear gauge. Journal of Portfolio Management, 26(3), 12–17. https://doi.org/10.3905/jpm.2000.319728
40. Zivot, E., & Andrews, D. W. K. (1992) Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270. https://doi.org/10.1198/073500102753410372