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An Extensive Analysis of FTSE 100 Realized Volatility with Different Information Channels

Year 2025, Volume: 9 Issue: 2, 469 - 482, 30.05.2025

Burak Korkusuz

https://doi.org/10.29023/alanyaakademik.1565468

Abstract

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.

Keywords

Volatilite Tahmini Gerçekleşen Volatilite HAR-RV-X modeli Bilgi Kanalları FTSE 100

References

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  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497. https://doi.org/10.3982/ECTA5771
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Wang, H. (2019). VIX and volatility forecasting: a new insight. Physica A, 533, 121951. https://doi.org/10.1016/j.physa.2019.121951
  • 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
  • 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

An Extensive Analysis of FTSE 100 Realized Volatility with Different Information Channels

Year 2025, Volume: 9 Issue: 2, 469 - 482, 30.05.2025

Burak Korkusuz

https://doi.org/10.29023/alanyaakademik.1565468

Abstract

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.

Keywords

Volatility Forecasting Realized Volatility HAR-RV-X model Information Channels FTSE 100

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497. https://doi.org/10.3982/ECTA5771
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Wang, H. (2019). VIX and volatility forecasting: a new insight. Physica A, 533, 121951. https://doi.org/10.1016/j.physa.2019.121951
  • 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
  • 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
There are 38 citations in total.

Details

Primary Language English
Subjects Econometric and Statistical Methods, Economic Models and Forecasting
Journal Section Makaleler
Authors

Burak Korkusuz 0000-0001-9374-2350

Publication Date May 30, 2025
Submission Date October 11, 2024
Acceptance Date April 28, 2025
Published in Issue Year 2025 Volume: 9 Issue: 2

Cite

APA Korkusuz, B. (2025). An Extensive Analysis of FTSE 100 Realized Volatility with Different Information Channels. Alanya Akademik Bakış, 9(2), 469-482. https://doi.org/10.29023/alanyaakademik.1565468