TY - JOUR T1 - An Extensive Analysis of FTSE 100 Realized Volatility with Different Information Channels TT - An Extensive Analysis of FTSE 100 Realized Volatility with Different Information Channels AU - Korkusuz, Burak PY - 2025 DA - May Y2 - 2025 DO - 10.29023/alanyaakademik.1565468 JF - Alanya Akademik Bakış PB - Alanya Alaaddin Keykubat Üniversitesi WT - DergiPark SN - 2651-4192 SP - 469 EP - 482 VL - 9 IS - 2 LA - en AB - Bu makale, Avrupa Birliği ve Amerika Birleşik Devletleri'nden gelen dış bilgi akışlarının FTSE 100 endeksinin oynaklığı üzerindeki etkisini, 5 dakikalık gün içi verilerden türetilen gerçekleşen varyans (RV) verilerini kullanarak araştırmaktadır. Dış faktörler, Birleşik Krallık'a özgü, Avrupa bölgesi ve ABD odaklı gruplar olarak kategorize edilerek, bu değişkenler HAR-RV modeline entegre edilmiştir ve böylece oynaklık tahminlerinin doğruluğu artırılmıştır. Ampirik sonuçlar, küresel ve bölgesel faktörlerin, özellikle S&P 500 ve NASDAQ gibi ABD piyasa göstergelerinin, FTSE 100 oynaklığı üzerinde önemli bir etkisi olduğunu, ancak Birleşik Krallık'a özgü yerel faktörlerin ek bilgi içermediğini göstermektedir. Tüm ABD odaklı değişkenleri içeren ABD odaklı Kitchen-Sink modeli, hem örnek içi hem de örnek dışı tahminlerde en etkili model olduğunu kanıtlamıştır. Yüksek frekanslı verilerin kullanımı bu bağlamda kritik öneme sahiptir, çünkü piyasa oynaklığının daha hassas bir şekilde ölçülmesine ve tahmin edilmesine olanak tanımaktadır. Bu bulgular, FTSE 100 gibi uluslararası yönelimli hisse senedi endekslerinin oynaklığını modelleme ve tahmin etmede geniş bir dış faktör yelpazesinin dahil edilmesinin önemini vurgulamaktadır. KW - Volatilite Tahmini KW - Gerçekleşen Volatilite KW - HAR-RV-X modeli KW - Bilgi Kanalları KW - FTSE 100 N2 - This paper investigates the influence of external information flows from the European Union and the United States on the volatility of the FTSE 100 index, using realized variance (RV) data derived from 5-minute intraday intervals. By categorizing external factors into UK-specific, neighbouring, and wider international groups, the study integrates these variables into the HAR-RV model to improve the accuracy of volatility forecasts. The empirical results indicate that interntional and neighbouring countries’ factors, particularly US market indicators such as the S&P 500 and NASDAQ, significantly impact FTSE 100 volatility, whilst domestic UK factors contain no additional information. The international Kitchen-Sink model, which includes all international variables, proves to be the most effective in the in-sample and out-of-sample forecasting. The use of high-frequency data is crucial in this context, as it allows for more precise measurement and forecasting of market volatility. These findings emphasize the importance of incorporating a broad range of external factors in modelling and forecasting the volatility of internationally-oriented stock indices such as the FTSE 100. CR - Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905. https://doi.org/10.2307/2527343 CR - Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modelling and forecasting realized volatility. Econometrica, 71(2), 579-625. https://doi.org/10.1111/1468-0262.00418 CR - Asai, M., Gupta, R., & McAleer, M. (2019). The impact of jumps and leverage in forecasting the co-volatility of oil and gold futures. Energies, 12(17), 33-79. https://doi.org/10.3390/en12173379 CR - Asai, M., Gupta, R., & McAleer, M. (2020). Forecasting volatility and co-volatility of crude oil and gold futures: effects of leverage, jumps, spillovers, and geopolitical risks. International Journal of Forecasting, 36(3), 933–948. https://doi.org/10.1016/j.ijforecast.2019.10.003 CR - Barndorff-Nielsen, O. E., Kinnebrock, S., & Shephard, N. (2010). Measuring downside risk: realized semi-variance. In Volatility and Time Series Econometrics: Essays in Honour of Robert F. Engle, T. Bollerslev, J. Russell, and M. Watson, eds. Oxford; New York: Oxford University Press, 117–136. https://dx.doi.org/10.2139/ssrn.1262194 CR - Bonato, M., Gkillas, K., Gupta, R., & Pierdzioch, C. (2020). Investor happiness and predictability of the realized volatility of oil price. Sustainability, 12(10), 4309. https://doi.org/10.3390/su12104309 CR - Bouri, E., Gupta, R., Pierdzioch, C., & Salisu, A. A. (2021). El Niño and forecastability of oil-price realized volatility. Theoretical and Applied Climatology, 144, 1173–1180. https://doi.org/10.1007/s00704-021-035691 CR - Christensen, K., Siggaard, M., & Veliyev, B. (2023). A machine learning approach to volatility forecasting. Journal of Financial Econometrics, 21(5), 1680-1727. https://doi.org/10.1093/jjfinec/nbac020 CR - Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196. https://doi.org/10.1093/jjfinec/nbp001 CR - Corsi, F., & Renò, R. (2012). Discrete-time volatility forecasting with persistent leverage effect and the link with continuous-time volatility modelling. Journal of Business & Economic Statistics, 30(3), 368-380. https://doi.org/10.1080/07350015.2012.663261 CR - Degiannakis, S., & Filis, G. (2017). Forecasting oil price realized volatility using information channels from other asset classes. Journal of International Money and Finance. 76, 28–49. https://doi.org/10.1016/j.jimonfin.2017.05.006 CR - Demirer, R., Gupta, R., Pierdzioch, C., & Shahzad, S. J. H. (2020). The predictive power of oil price shocks on realized volatility of oil: A note. Resources Policy, 69, 101856. https://doi.org/10.1016/j.resourpol.2020.101856 CR - Demirer, R., Gkillas, K., Gupta, R., & Pierdzioch, C. (2021). Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests. Journal of the Operational Research Society, 1936668. https://doi.org/10.1080/01605682.2021.1936668 CR - Duan, Y., Chen, W., Zeng, Q., & Liu, Z. (2018). Leverage effect, economic policy uncertainty and realized volatility with regime switching. Physica A, 493(C), 148-154. https://doi.org/10.1016/j.physa.2017.10.040 CR - Dutta, A., Soytas, U., Das, D., & Bhattacharyya, A. (2022). In search of time-varying jumps during the turmoil periods: evidence from crude oil futures markets. Energy Economics, 114, 106275. https://doi.org/10.1016/j.eneco.2022.106275 CR - Gkillas, K., Gupta, R., & Pierdzioch, C. (2019). Forecasting (downside and upside) realized exchange-rate volatility: Is there a role for realized skewness and kurtosis?. Physica A: Statistical Mechanics and its Applications, 532, 121867. https://doi.org/10.1016/j.physa.2019.121867 CR - Gkillas, K., Gupta, R., & Pierdzioch, C. (2020). Forecasting realized oil-price volatility: the role of financial stress and asymmetric loss. Journal of International Money and Finance, 104, 102137. https://doi.org/10.1016/j.jimonfin.2020.102137 CR - Gupta, R., & Pierdzioch, C. (2021b). Climate risks and the realized volatility of oil and gas prices: results of an out-of-sample forecasting experiment. Energies, 14(23), 8085. https://doi.org/10.3390/en14238085 CR - Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497. https://doi.org/10.3982/ECTA5771 CR - Hansen, P. R., Lunde, A., & Nason, J. M. (2003). Choosing the best volatility models: the model confidence set approach. Oxford Bulletin of Economics and Statistics, 65(1), 839–861. https://doi.org/10.1046/j.0305-9049.2003.00086.x CR - Hol, E., & Koopman, S. J. (2002). Stock index volatility forecasting with high frequency data. Tinbergen Institute Discussion Paper, 02-068/4, 1-26. https://hdl.handle.net/10419/86000 CR - Kambouroudis, D. S., McMillan, D., & Tsakou, K. (2021). Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility. Journal of Futures Markets, 41(10), 1618–1639. https://doi.org/10.1002/fut.22241 CR - Korkusuz, B., Kambouroudis, D., & McMillan, D. (2023). Do extreme range estimators improve realized volatility forecasts? evidence from G7 stock markets. Finance Research Letters, 55, 103992. https://doi.org/10.1016/j.frl.2023.103992 CR - Liang, C., Li, Y., Ma, F., & Zhang, Y. (2022). Forecasting international equity market volatility: a new approach. Journal of Forecasting, 41(7), 1433-1457. https://doi.org/10.1002/for.2869 CR - Liang, C., Wei, Y., Lei, L., & Ma, F. (2022). International equity market volatility forecasting: new evidence. International Journal of Finance and Economics, 27(1), 594-609. https://doi.org/10.1002/ijfe.2170 CR - Liu, J., Ma, F., & Zhang, Y. (2019). Forecasting the Chinese stock volatility across international stock markets. Physica A, 525, 466-477. https://doi.org/10.1016/j.physa.2019.03.097 CR - Liu, L. Y., Patton, A. J., & Sheppard, K. (2015). Does anything beat 5-minute realized variance? a comparison of realized measures across multiple asset classes. Journal of Econometrics, 187(1), 293-311. https://doi.org/10.1016/j.jeconom.2015.02.008 CR - Luo, J., Demirer, R., Gupta, R., & Ji, Q. (2022). Forecasting oil and gold volatilities with sentiment indicators under structural breaks. Energy Economics, 105, 105751. https://doi.org/10.1016/j.eneco.2021.105751 CR - Ma, F., Wahab, M., Liu, J., & Liu, L. (2018). Is economic policy uncertainty important to forecast the realized volatility of crude oil futures? Applied Economics, 50(18), 2087–2101. https://doi.org/10.1080/00036846.2017.1388909 CR - Mei, D., Liu, J., Ma, F., & Chen, W. (2017). Forecasting stock market volatility: do realized skewness and kurtosis help? Physica A, 481, 153-159. https://doi.org/10.1016/j.physa.2017.04.020 CR - Müller, U. A., Dacorogna, M. M., Davé, R. D., Olsen, R. B., Pictet, O. V., & Von Weizsäcker, J. E. (1997). Volatilities of different time resolutions, analysing the dynamics of market components. Journal of Empirical Finance, 4(2-3), 213-239. https://doi.org/10.1016/S0927-5398(97)00007-8 CR - Nishimura, Y., & Bianxia, S. U. N. (2024). Impacts of Donald Trump's tweets on volatilities in the European stock markets. Finance Research Letters, 72, 106491. https://doi.org/10.1016/j.frl.2024.106491 CR - Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics, 160(1), 246 - 256. https://doi.org/10.1016/j.jeconom.2010.03.034 CR - Peng, H., Chen, R., Mei, D., & Diao, X. (2018) Forecasting the realized volatility of the Chinese stock market: do the G7 stock markets help? Physica A, 501, 78–85. https://doi.org/10.1016/j.physa.2018.02.093 CR - Salisu, A. A., Gupta, R., Bouri, E., & Ji, Q. (2022). Mixed-frequency forecasting of crude oil volatility based on the information content of international economic conditions. Journal of Forecasting. 41(1), 134–157. https://doi.org/10.1002/for.2800 CR - Wang, H. (2019). VIX and volatility forecasting: a new insight. Physica A, 533, 121951. https://doi.org/10.1016/j.physa.2019.121951 CR - Wang, J., Lu, X., He, F., & Ma, F. (2020). Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU? International Review of Financial Analysis, 72, 1057-5219. https://doi.org/10.1016/j.irfa.2020.101596 CR - Zhou, W., Pan, J., & Wu, X. (2019). Forecasting the realized volatility of CSI 300. Physica A: Statistical Mechanics and Its Applications, 531, 121799. https://doi.org/10.1016/j.physa.2019.121799 UR - https://doi.org/10.29023/alanyaakademik.1565468 L1 - https://dergipark.org.tr/en/download/article-file/4280905 ER -