Analysis of the interaction of Participation 30 Index with Dow Jones Islamic Markets Index and CBOE Volatility Index

Yıl 2023, , 246 - 256, 01.12.2023

Halilibrahim Gökgöz , Cantürk Kayahan

https://doi.org/10.33707/akuiibfd.1257942

Cited By: 2

Öz

This study aims to examine the dynamic relationship between Islamic markets and global financial risk factors using the Dow Jones Islamic Markets World Index (DJIM), Participation 30 Index (KATLM 30), and the CBOE Volatility Index (VIX). The analysis applies the DCC-GARCH model to the daily return series from January 3, 2014, to December 31, 2021. The results reveal a negative interaction between VIX and the Islamic indices throughout the study period. Furthermore, the dynamic correlation coefficient between VIX and DJIM (-0.755040) was higher than that between VIX and KATLM 30 (-0.180328), while the dynamic correlation coefficient between KATLM 30 and DJIM (0.26989) was weak and positive. These findings suggest that KATLM 30 is less affected by global risks, exhibits less integration into the global financial system, and serves as a better diversifier for international investment portfolios than DJIM. This study provides valuable insights for investors and portfolio managers and contributes to enhancing portfolio management strategies.

Anahtar Kelimeler

DJIMI, VIX, Islamic Index, Volatility, DCC-GARCH

Katılım 30 Endeksi’nin Dow Jones İslami Piyasalar Endeksi ve CBOE Volatilite Endeksi ile etkileşiminin analizi

Öz

Bu çalışma, Dow Jones İslami Piyasalar Dünya Endeksi (DJIM), Katılım 30 Endeksi (KATLM 30) ve CBOE Oynaklık Endeksi'ni (VIX) kullanarak İslami piyasalar ile küresel finansal risk faktörleri arasındaki dinamik ilişkiyi incelemeyi amaçlamaktadır. Analiz, DCC-GARCH modelini 3 Ocak 2014 - 31 Aralık 2021 günlük getiri serisine uyguluyor. Sonuçlar, çalışma dönemi boyunca VIX ile İslami endeksler arasında negatif bir etkileşim olduğunu ortaya koyuyor. Ayrıca VIX ile DJIM arasındaki dinamik korelasyon katsayısı (-0,755040), VIX ile KATLM 30 arasındakinden (-0,180328) daha yüksek, KATLM 30 ile DJIM arasındaki dinamik korelasyon katsayısı (0,26989) ise zayıf ve pozitiftir. Bu bulgular, KATLM 30'un küresel risklerden daha az etkilendiğini, küresel finansal sisteme daha az entegrasyon sergilediğini ve uluslararası yatırım portföyleri için DJIM'den daha iyi bir çeşitlendirici olarak hizmet ettiğini göstermektedir. Bu çalışma, yatırımcılar ve portföy yöneticileri için değerli bilgiler sağlamakta ve portföy yönetimi stratejilerinin geliştirilmesine katkıda bulunmaktadır.

Anahtar Kelimeler

DJIMI, VIX, Islamic Index, Volatility, DCC-GARCH

Kaynakça

• Adekoya, O. B., Oliyide, J. A. & Tiwari, A. K. (2022). Risk transmissions between sectoral Islamic and conventional stock markets during COVID-19 pandemic: What matters more between actual COVID-19 occurrence and speculative and sentiment factors? Borsa Istanbul Review, 22 (2), 363–376.
• Ahmad, W., Rais, S. & Shaik, A.R. (2018). Modelling the directional spillovers from DJIM index to conventional benchmarks: different this time? Quart. Rev. Econ. Finan., 67, 14–27.
• Ajmi, A. N., Hammoudeh, S., Nguyenc, D. K., & Sarafrazi, S. (2014). How strong are the causal relationships between Islamic stock markets and conventional financial systems? Evidence from linear and nonlinear tests. Journal of International Financial Markets, Institutions, and Money, 28, 213–227.
• Ali, S., Shahzad, S.J.H., Raza, N. & Al-Yahyaee, K.H. (2018). Stock market efficiency: a comparative analysis of Islamic and conventional stock markets. Physica A, 503, 139–153.
• Al-Khazali, O.M., Leduc, G. & Alsayed, M.S. (2016). A market efficiency comparison of Islamic and Non-Islamic stock Indices. Emerging Market and Trade, 52, 1587–1605.
• Antar, M. & Alahouel, F. (2020). Co-movements and diversification opportunities among Dow Jones Islamic indexes. International Journal of Islamic and Middle Eastern Finance and Management, 13 (1), 94–115.
• Arfaoui, M., & Raggad, B. (2021). Do Dow Jones Islamic equity indices undergo speculative pressure? New insights from a nonlinear and asymmetric analysis. International Journal of Finance & Economics, https://doi.org/10.1002/ijfe.2495
• Baykut, E., & Çonkar, K. (2020). An assessment of relationship between BIST-30 and Participation-30 indices. Muhasebe ve Finans İncelemeleri Dergisi, 3 (2), 163-174
• Bayram, K., & Othman, A. H. (2019). Islamic Versus Conventional Stock Market Indices Performance: Empirical Evidence from Turkey. IQTISHADIA, 12 (1), 74-86.
• Bekaert, G., Harvey, C. R. & Ng, A. (2003). Market integration and contagion. Journal of Business, 78 (1), 39–70.
• Bezgin, M. S., & Karaçayir, E. (2022). Volatility Interaction between Dow Jones Sukuk Index and Selected Stock Indices. Journal of Research in Economics, Politics & Finance, 7 (3), 697-712.
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307-327.
• Canbaz, M. F. & Baykut, E. (2021). The effect of Covid-19 outbreak on participation (Islamic) index. BSEU Journal of Social Sciences, 6 (2), 273-283.
• Dania, A. & Malhotra, D.K. (2013). An empirical examination of the dynamic linkages of faith-based socially responsible investing. The Journal of Wealth Management, 16 (1), 65–79.
• Do, A. Powell, R., Yong, J. & Singh, A. (2019). 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, 10196.
• Duncan, A. & Kabundi, A. (2013). Domestic and foreign sources of volatility spillover to South African asset classes. Economic Modeling, 31, 566–573.
• Engle, R.F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20, 339–350.
• Foglie, A. & Panetta, I. (2020). Islamic stock market versus conventional: Are Islamic investing a ‘Safe Haven’ for investors? A systematic literature review. Pacific-Basin Finance Journal, 64, 101435.
• Forbes, K. & Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. The Journal of Finance, 57, 2223–2261.
• Girard, E. & Hassan, M. K. (2008). Is There a Cost to Faith-Based Investing: Evidence from FTSE Islamic Indices. The Journal of Investing, 112-121.
• Haddad, H. B., Mezghani, I. & Al Dohaiman, M. (2020). Common stocks, common transmission mechanisms and time-varying connectedness among Dow Jones Islamic stock market indices and global risk factors. Economic Systems, 44, 100760.
• Hammoudeh, S., Mensi, W., Reboredo, J. & Nguyen, S. (2014). Dynamic dependence of the global Islamic equity index with global conventional equity market indices and risk factors. Pacific-Basin Finance Journal, 30, 189–206.
• Hanif, M. & Bhatti, A. (2018). Causality among Stock Market and Macroeconomic Factors: A Comparison of Conventional and Islamic Stocks. Journal of Islamic Business and Management, 8 (2). https://doi.org/10.26501/jibm/2018.0802-006
• Jawadi, F., Jawadi, N. & Louhichi, W. (2014). Conventional and Islamic stock price performance: an empirical investigation. Int. Econ. 137, 73–87.
• Kahyaoğlu, S. B. & Akkuş, H. T. (2020). Volatility Spillover Between Conventional Stock Index and Participation Index: The Turkish Case. S. Grima, E. Özen ve H. Boz (Ed.), Contemporary Issues in Business Economics and Finance (Contemporary Studies in Economic and Financial Analysis, Vol. 104) içinde (ss. 1–17).
• Kandemir, T. & Gökgöz, H. (2022). Bitcoin emtialar için çeşitlendiriciden fazlası mı? Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 7 (2), 227-240.
• Kandemir, T., Vurur, N. S. & Gökgöz, H. (2022). Türkiye’nin CDS primleri ile BİST 100, döviz kurları ve tahvil faizleri arasındaki etkileşimin cDCC-EGARCH ve varyansta nedensellik analizleriyle incelemesi. KMÜ Sosyal ve Ekonomik Araştırmalar Dergisi, 24 (42), 510-526.
• Majdoub, J. & Mansour, W. (2014). Islamic equity market integration and volatility spillover between emerging and US stock markets. The North American Journal of Economics and Finance, 29, 452–470.
• Mensi, W., Hammoudeh, S. & Tiwari, A. K. (2016). New evidence on hedges and safe havens for Gulf stock markets using the wavelet-based quantile. Emerging Markets Review, 28, 155–183.
• Mensi, W., Hammoudeh, S., Reboredo, J. C. & Nguyen, D. C. (2015). Are Sharia stocks, gold and U.S. Treasury hedges and/or safe havens for the oil based GCC markets? Emerging Markets Review, 24, 101–121.
• Mensi, W., Rehman, M. U., Maitra, D., Al-Yahyaee, K. H. & Vo, X. V. (2022). Frequency spillovers and portfolio risk implications between Sukuk, Islamic stock and emerging stock markets. Quarterly Review of Economics and Finance, in press. https://doi.org/10.1016/j.qref.2022.10.012
• Najeeb, S. F., Bacha, O. & Masih, M. (2015). Does Heterogeneity in Investment Horizons Affect Portfolio Diversification? Some Insights Using M-GARCH-DCC and Wavelet Correlation Analysis. Emerging Markets Finance & Trade, 51, 188–208.
• Naifar, N. (2016). Do global risk factors and macroeconomic conditions affect global Islamic index dynamics? A quantile regression approach. Quarterly Review of Economics and Finance, 61, 29-39.
• Nazlıoğlu, S., Hammoudeh, S. & Gupta, R. (2015). Volatility transmission between Islamic and conventional equity markets: evidence from causality-in-variance test. Applied Economics, 47 (46), 4996-5011.
• Raza, N., Ali, S., Shahzad, S.J.H., Rehman, M.U. & Salman, A., 2019. Can alternative hedging assets add value to Islamic-conventional portfolio mix: evidence from MGARCH models. Resour. Pol. 61, 210–230.
• Seçme, O., Aksoy, M. & Uysal, Ö. (2016). Katılım Endeksi Getiri, Performans ve Oynaklığının Karşılaştırmalı Analizi. The Journal of Accounting and Finance, 107-128.
• Shamsuddin, A. (2014). Are Dow Jones Islamic equity indices exposed to interest rate risk? Econ. Model. 39, 273–281.
• Sial, M. S., Cherian, J., Meero, A., Salman, A., Rahman, A. A., Samad, S. & Negrut, C. V. (2022). Determining financial uncertainty through the dynamics of sukuk bonds and prices in emerging market indices. Risks, 10 (3), 61. https://doi.org/10.3390/risks10030061
• Şensoy, A., Aras, G. & Hacihasanoglu, E. (2015). Predictibility dynamics of Islamic and conventional equity markets. North Am. J. Econ. Financ. 31, 222–248.
• Tsay, R. S. (2013). Multivariate Time Series Analysis: with R and Financial Applications. ABD: John Wiley & Sons.
• Uçar, G. & Kandemir, T. (2022). BIST 50 ve KATILIM 30 Endeksleri Arasındaki Eşbütünleşme ve Nedensellik İlişkilerinin Değerlendirilmesi. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 7 (3), 417-432.