OPTIMAL HEDGE RATIO OF SUKUK AND ISLAMIC EQUITY: A NOVEL APPROACH
Main Article Content
Abstract
This research applies a novel model to compute a hedge ratio. Specifically, the model modifies volatility forecasts of an exponentially weighted moving average method to account for the fat-tailed distribution of returns. This simpler model aims to overcome the widely-known drawback of the complex GARCH models that a long daily return period is required to ensure the model’s convergence. The data are Islamic exchange-traded funds: SP Funds Dow Jones Global Sukuk ETF, Wahed FTSE USA Shariah ETF, and iShares MSCI EM Islamic UCITS ETF. Sukuk act as a diversifier over the turmoil period since they are positively correlated with Islamic equity and their volatility is less than that of Islamic equity. This work also implements widely-used methods such as Dynamic Equicorrelation-GARCH, GO-GARCH, asymmetric DCC-GARCH, naïve approach, and linear regression. Two forms of data splitting and a rolling-window analysis are carried out to reduce data mining bias. All models generate one-step ahead forecasts of hedge ratios. Applying wavelet-transformed returns and utility analysis incorporating third and fourth moments, the proposed models produce better performance than the competing models. The results remain the same irrespective of different hedging instruments (precious metals) and asset classes.
Downloads
Article Details
Issue
Section
Journal of Islamic Monetary Economics and Finance is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
References
Ahmad, W., Sadorsky, P., & Sharma, A. (2018). Optimal hedge ratios for clean energy equities. Economic Modelling, 72, 278–295. https://doi.org/10.1016/j.econmod.2018.02.008
Alam, M., & Ansari, V. A. (2020). Are Islamic indices a viable investment avenue? An empirical study of Islamic and conventional indices in India. International Journal of Islamic and Middle Eastern Finance and Management, 13(3), 503–518.
Alexander, C., & Barbosa, A. (2008). Hedging index exchange traded funds. Journal of Banking and Finance, 32(2), 326–337.
Alexander, C., & Dakos, M. (2023). Assessing the accuracy of exponentially weighted moving average models for Value-at-Risk and Expected Shortfall of crypto portfolios. Quantitative Finance, 23(3), 393–427.
Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762. https://doi.org/10.1016/j.eneco.2020.104762
Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & Perez de Gracia, F. (2018). Oil volatility, oil and gas firms and portfolio diversification. Energy Economics, 70, 499–515. https://doi.org/10.1016/j.eneco.2018.01.023
Arif, M., Naeem, M. A., Hasan, M., M Alawi, S., & Taghizadeh-Hesary, F. (2022). Pandemic crisis versus global financial crisis: Are Islamic stocks a safe-haven for G7 markets? Economic Research-Ekonomska Istraživanja, 35(1), 1707–1733.
Bandhu Majumder, S. (2022). Searching for hedging and safe haven assets for Indian equity market – a comparison between gold, cryptocurrency and commodities. Indian Growth and Development Review, 15(1), 60–84.
Batten, J. A., Kinateder, H., Szilagyi, P. G., & Wagner, N. F. (2019). Hedging stocks with oil. Energy Economics, 93, 104422.
Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. The Financial Review, 45(2010), 217–229.
Bauwens, L., Laurent, S., & Rombouts, J. V. K. (2006). Multivariate GARCH models: A survey. Journal of Applied Econometrics, 21(1), 79–109.
Bouri, E., Lucey, B., & Roubaud, D. (2020). Cryptocurrencies and the downside risk in equity investments. Finance Research Letters, 33, 101211. https://doi.org/10.1016/j.frl.2019.06.009
Bouri, E., Shahzad, S. J. H., Roubaud, D., Kristoufek, L., & Lucey, B. (2020). Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis. The Quarterly Review of Economics and Finance, 77, 156–164. https://doi.org/10.1016/j.qref.2020.03.004
Brixton, A., Brooks, J., Hecht, P., Ilmanen, A., Maloney, T., & McQuinn, N. (2023). A changing stock–bond correlation: Drivers and implications. The Journal of Portfolio Management, 49(4), 64-80.
Calder, R. (2010). Short-selling repilication in Islamic finance: Innovation and debate in Malaysia and beyond. In Current Issues in Islamic Banking and Finance (pp. 277–315). https://doi.org/10.1142/9789812833938_0014
Cappiello, L., Engle, R., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572.
Catania, L., & Grassi, S. (2022). Forecasting cryptocurrency volatility. International Journal of Forecasting, 38(3), 878–894.
Dharani, M., Hassan, M. K., Rabbani, M. R., & Huq, T. (2022). Does the Covid-19 pandemic affect faith-based investments? Evidence from global sectoral indices. Research in International Business and Finance, 59, 101537. https://doi.org/10.1016/j.ribaf.2021.101537
Drobetz, W., Schröder, H., & Tegtmeier, L. (2020). The role of catastrophe bonds in an international multi-asset portfolio: Diversifier, hedge, or safe haven? Finance Research Letters, 33, 101198. https://doi.org/10.1016/j.frl.2019.05.016
Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157–170.
Engle, R., & Kelly, B. (2012). Dynamic equicorrelation. Journal of Business & Economic Statistics, 30(2), 212–228.
Ghaemi Asl, M., & Rashidi, M. M. (2021). Dynamic diversification benefits of Sukuk and conventional bonds for the financial performance of MENA region companies: empirical evidence from COVID-19 pandemic period. Journal of Islamic Accounting and Business Research, 12(7), 979–999.
Hamma, W., Ghorbel, A., & Jarboui, A. (2021). Hedging Islamic and conventional stock markets with other financial assets: Comparison between competing DCC models on hedging effectiveness. Journal of Asset Management, 22(3), 179–199.
Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497.
Hasan, Z. (2018). Academic sociology: The alarming rise in predatory publishing and its consequences for Islamic economics and finance. ISRA International Journal of Islamic Finance, 10(1), 6–18.
Injadat, E. (2018). the Practical Model of Hedging in Islamic Financial Markets. International Journal of Economics, Commerce and Management United Kingdom, 6(6), 134–140.
Ismal, R. (2022). Assessing the application of Islamic and conventional hedgings in Indonesia. International Journal of Islamic and Middle Eastern Finance and Management, 15(1), 32–47.
Izadi, S., & Hassan, M. K. (2018). Portfolio and hedging effectiveness of financial assets of the G7 countries. Eurasian Economic Review, 8(2), 183–213.
Jalkh, N., Bouri, E., Vo, X. V., & Dutta, A. (2020). Hedging the risk of travel and leisure stocks: The role of crude oil. Tourism Economics, 27(7), 1337-1356.
Jena, S. K., Tiwari, A. K., & Roubaud, D. (2018). Comovements of gold futures markets and the spot market: A wavelet analysis. Finance Research Letters, 24, 19–24. https://doi.org/https://doi.org/10.1016/j.frl.2017.05.006
Jeribi, A., & Fakhfekh, M. (2021). Portfolio management and dependence structure between cryptocurrencies and traditional assets: evidence from FIEGARCH-EVT-Copula. Journal of Asset Management, 22(3), 224–239.
Kamara, A., Korajczyk, R. A., Lou, X., & Sadka, R. (2016). Horizon Pricing. The Journal of Financial and Quantitative Analysis, 51(6), 1769–1793.
Karim, S., & Naeem, M. A. (2022). Do global factors drive the interconnectedness among green, Islamic and conventional financial markets? International Journal of Managerial Finance, 18(4), 639–660.
Kroner, K. F., & Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. The Journal of Financial and Quantitative Analysis, 28(4), 535–551.
Li, X., & Zakamulin, V. (2020). Stock volatility predictability in bull and bear markets. Quantitative Finance, 20(7), 1149–1167.
Liu, W., Semeyutin, A., Lau, C. K. M., & Gozgor, G. (2020). Forecasting value-at-risk of cryptocurrencies with riskmetrics type models. Research in International Business and Finance, 54. https://doi.org/10.1016/j.ribaf.2020.101259
Naeem, M. A., Mbarki, I., Alharthi, M., Omri, A., & Shahzad, S. J. H. (2021). Did COVID-19 impact the connectedness between green bonds and other financial markets? evidence from time-frequency domain with portfolio implications. Frontiers in Environmental Science, 9(May), 1–15. https://doi.org/10.3389/fenvs.2021.657533
Naeem, M. A., Raza Rabbani, M., Karim, S., & Billah, S. M. (2023). Religion vs ethics: Hedge and safe haven properties of Sukuk and green bonds for stock markets pre- and during COVID-19. International Journal of Islamic and Middle Eastern Finance and Management, 16(2), 234–252.
Narayan, P. K., & Sharma, S. S. (2016). Intraday return predictability, portfolio maximisation, and hedging. Emerging Markets Review, 28, 105–116. https://doi.org/10.1016/j.ememar.2016.08.017
Nekhili, R., & Sultan, J. (2022). Hedging Bitcoin with conventional assets. Borsa Istanbul Review, 22(4), 641–652. https://doi.org/10.1016/j.bir.2021.09.003
Nguyen, T. T. H., Naeem, M. A., Balli, F., Balli, H. O., & Vo, X. V. (2021). Time-frequency comovement among green bonds, stocks, commodities, clean energy, and conventional bonds. Finance Research Letters, 40, 101739. https://doi.org/10.1016/j.frl.2020.101739
Qureshi, S., Aftab, M., Bouri, E., & Saeed, T. (2020). Dynamic interdependence of cryptocurrency markets: An analysis across time and frequency. Physica A: Statistical Mechanics and Its Applications, 559, 125077. https://doi.org/10.1016/j.physa.2020.125077
Ryan, L. (2021). Bonds don’t need to be negatively correlated with equities. The Journal of Investing, 30(60), 70-80.
Samitas, A., Papathanasiou, S., & Koutsokostas, D. (2021). The connectedness between Sukuk and conventional bond markets and the implications for investors. International Journal of Islamic and Middle Eastern Finance and Management. https://doi.org/10.1108/IMEFM-04-2020-0161
Sharma, U., & Karmakar, M. (2023). Measuring minimum variance hedging effectiveness: Traditional vs. sophisticated models. International Review of Financial Analysis, 87, 102621. https://doi.org/10.1016/j.irfa.2023.102621
Silahli, B., Dingec, K. D., Cifter, A., & Aydin, N. (2021). Portfolio value-at-risk with two-sided Weibull distribution: Evidence from cryptocurrency markets. Finance Research Letters, 38, 101425.
Sisodia, G., Joseph, A., & Dominic, J. (2022). Whether corporate green bonds act as armour during crises? Evidence from a natural experiment. International Journal of Managerial Finance, 18(4), 701–724.
Stamos, M. (2022). Forecasting stock market volatility. The Journal of Portfolio Management, 49(3), 129-137.
State Bank of Pakistan. (2020). Adoption of AAOIFI Shariah Standard No. 49. Retrieved October 25, 2023, from https://www.sbp.org.pk/ibd/2020/C3-Annex-A.pdf
Van der Weide, R. (2002). GO-GARCH: A Multivariate generalized orthogonal GARCH model. Journal of Applied Econometrics, 17(5), 549–564.
Wang, Y., Wu, C., & Yang, L. (2015). Hedging with futures: Does anything beat the naïve hedging strategy? Management Science, 61(12), 2870–2889.
Yousaf, I., Ali, S., Naveed, M., & Adeel, I. (2021). Risk and return transmissions from crude oil to Latin American stock markets during the crisis: Portfolio implications. SAGE Open, 11(2). https://doi.org/10.1177/21582440211013800
Zghal, R., Melki, A., & Ghorbel, A. (2022). Do commodities hedge regional stock markets at the same effectiveness level? Evidence from MGARCH models. International Journal of Emerging Markets, (ahead-of-print).