Volatility Forecasting with Intraday Data: The Evolution of Realized Variance and the HAR-RV Model

Öz

Güniçi Verilerle Volatilite Tahmini: Gerçekleştirilmiş Varyansın Evrimi ve HAR-RV Modeli

Öz

Gerçek volatilite istatistiki olarak gizlidir, bu nedenle gerçek volatiliteyi temsil edecek bir temsili ölçüte ihtiyaç vardır. Özellikle GARCH türü modellerin uygulamalarında, günlük getirilerin karesi veya mutlak değeri bu gerçek volatiliteyi temsil için kullanılır. Fakat, 2000'lerin başından itibaren, veri depolama teknolojilerindeki gelişmeler sayesinde yüksek frekansta verilere kolay erişim, volatilite araştırmalarını günlük bazlı yöntemlerden güniçi bazlı yöntemlere evirmiştir. Bu da güniçi verilerin kullanımını akademide ve pratikte oldukça dikkat çekici hale getirmiştir. Andersen ve Bollerslev (1998) yüksek frekansta verilerden türetilen bir volatilite ölçüsü geliştirmiştir. Bu ölçü, güniçi getirilerin kareleri toplamı ile hesaplanan "gerçekleştirilmiş varyans (realized variance)" olarak adlandırılır. Gerçekleştirilmiş varyansın, günlük kapanış fiyatına göre hesaplanan getirilerin karesinden daha hassas bir volatilite ölçüsü olduğu gösterilmiştir, böylece de gelecekteki volatilite hakkında daha fazla bilgi içerir. Heterojen Piyasa Hipotezi’ne dayanarak, Corsi (2009) gerçekleştirilmiş varyans için Heterojen Oto-Regresif (HAR-RV) modelini önermektedir. Bu çalışma, finansal piyasalarda daha etkin tahminler elde etmek için intraday (güniçi) verilerin ve uygun model spesifikasyonlarının kullanılmasının önemine dikkat çekmektedir.

Anahtar Kelimeler

• Volatilite
• Temsili Volatilite
• Koşullu Varyans
• Gerçekleştirilmiş Varyans
• GARCH
• HAR-RV

Kaynakça

1. Akgiray, V. (1989). Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts, Journal of Business, 62, 55-80.
2. Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P. (2001). The distribution of exchange rate volatility. Journal of the American Statistical Association, 96, 42–55.
3. Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P. (2003). Modelling and forecasting realized volatility. Econometrica, 71(2), 579-625.
4. Andersen, T. G., Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905.
5. Andersen, T. G., Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance, 4, 115–158.
6. Andersen, T. G., Dobrev, D., Schaumburg, E. (2012). Jump-robust volatility estimation using nearest neighbor truncation. Journal of Econometrics, 169, 75–93.
7. Balaban, E., Bayar, A., Faff, R. W. (2006). Forecasting stock market volatility: Further international evidence, The European Journal of Finance. 12, 171-188.
8. Barndorff-Nielsen, O., S. Kinnebrock, N. Shephard. (2010). Measuring downside risk: Realized semi-variance. 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.

9. Blair, B. J., Poon, S.-H., Taylor, S. J. (2001). Forecasting S&P100 volatility: The incremental information content of implied volatilities and high-frequency index returns. Journal of Econometrics, 105(1), 5-26. Forecasting and empirical methods in finance and macroeconomics.
10. Bollerslev, T. (2009). Glossary to ARCH (GARCH), the NBER.
11. Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: A multivariate Generalized ARCH model. Review of Economics and Statistics, 72, 498-505.
12. Bollerslev, T., Engle, R.F., Wooldridge, J.M. (1988). A capital asset pricing model with time varying covariances. Journal of Political Economy, 96, 116–131.
13. Bollerslev, T., S. Z. Li, V. Todorov. (2016). Roughing up Beta: Continuous vs. Discontinuous Betas and the Cross-Section of Expected Stock Returns. Journal of Financial Economics, 120, 464–490.
14. Bollerslev, T., Li, S., Zhao, B., (2018). Good volatility, bad volatility and the cross-section of stock returns. J. Finance Quantitative Analysis Forthcoming.
15. Bu, R., Rodrigo, H., Izzeldin, M., Murphy, A., Tsionas, M. (2023). The contribution of jump signs and activity to forecasting stock price volatility. Journal of Empirical Finance, 70, 144-164.
16. Caporin, M. (2021). The role of jumps and asset liquidity in realized volatility modelling and forecasting. Journal of Financial Econometrics, 1-26.
17. Celik, S., Ergin, H. (2014). Volatility forecasting using high frequency data: Evidence from stock markets. Economic Modelling, 36, 176–190.
18. Chortareas, G., Jiang, Y., Nankervis, J. (2011). Forecasting exchange rate volatility using high-frequency data: Is the euro different? International Journal of Forecasting, 27, 1089-1107.
19. Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196.
20. Cumby, R., Figlewski, S., Hasbrouck, J. (1993). Forecasting volatilities and correlations with EGARCH models. Journal of Derivatives, 1, 51-63.
21. 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.
22. Degiannakis, S., Delis, P., Filis, G., Giannopoulos, G. (2025). Trading VIX on volatility forecasts: another volatility puzzle? Journal of Forecasting.
23. Ding, Z., Granger, C. W., Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83-106.
24. Ding, Y., Kambouroudis, D., McMillan, D. G. (2025). Forecasting Realised Volatility Using Regime-Switching Models. International Review of Economics & Finance, 104171.
25. Diebold, F.X., Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119, 158–171.
26. Diebold, F.X., Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66.
27. Diebold, F.X., Yilmaz, K. (2014). On the network topology of variance decompositions: measuring the connectedness of financial firms, Journal of Econometrics, 182, 119–134.
28. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
29. Engle, R. F., Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews, 5(1), 1-50.
30. Engle, R., Lee, G. (1993). A permanent and transitory component model of stock return volatility, Working Paper No. 92-44R, University of California San Diego.
31. Engle, R. F., Gallo, G. M. (2006). A multiple indicators model for volatility using intra-daily data. Journal of econometrics, 131(1-2), 3-27.
32. Engle, R. (2002). New frontiers for arch models. Journal of Applied Econometrics, 17(50), 425-446.
33. Engle, R. F., Kroner, K. F. (1995). Multivariate simultaneous generalised ARCH. Econometric Theory, 11, 122-150.
34. Fang, N., Jiang, W., Luo, R. (2017). Realized Semivariances and the Variation of Signed Jumps in China’s Stock Market. Emerging Markets Finance and Trade, 53(3), 563-586.
35. Feunou, B., Okou, C. (2018). Good volatility, bad volatility, and option pricing. J. Finance Quantitative Analysis, 1–33.
36. Garman, M.B., Klass, M.J. (1980). On the estimation of security price volatilities from historical data. J. Bus. 53, 67–78.
37. Glosten, L. R., Jagannathan, R., Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Technical report.
38. Hansen, P. R., Lunde, A., (2005), A forecast comparison of volatility models: Does anything beat a GARCH(1,1)?. Journal of Applied Econometrics, 20, pp. 873-889.
39. Hansen, P. R., Lunde, A. (2010). Forecasting volatility using high frequency data, A systematic review. 1-37.
40. Hol Uspensky, E., Koopman, S. J. (2002). Stock index volatility forecasting with high frequency data.
41. Hu, N., Yin, X., Yao, Y. (2025). A novel HAR-type realized volatility forecasting model using graph neural network. International Review of Financial Analysis, 98, 103881.
42. Izzeldin, M., Kabir, M. K., Pappas, V., Tsionas, M. (2019). Forecasting realized volatility using ARFIMA and HAR models. Quantitative Finance. 19(10). 1627-1638.
43. Izzeldin, M., Muradoglu, Y., Pappas, V., Sivaprasad, S. (2021). The impact of Covid-19 on G7 stock markets volatility: Evidence from a ST-HAR model, International Review of Financial Analysis. 74(C), number S1057521921000144.
44. Junior, M. V. W., Pereira, P. L. V. (2011). Modeling and forecasting of realized volatility: Evidence from Brazil. Brazilian Review of Econometrics, 31(2), 315-337.
45. Koopman, S. J., Jungbacker, B., Hol, E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility measurements. Journal of Empirical Finance, 12, 445–475.
46. Koopman, S. J., Hol Uspensky, E. (2002). The stochastic volatility in mean model: Empirical evidence from international stock markets. Journal of applied Econometrics, 17(6), 667-689.
47. Korkusuz, B. (2023). Forecasting realized volatility: Evidence from nordic stock markets. İstatistik Araştırma Dergisi, 13(2), 1-12.
48. 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.
49. Luo, Q., Ma, F., Wang, J., Wu, Y. (2024). Changing determinant driver and oil volatility forecasting: A comprehensive analysis. Energy Economics, 129, 107187.
50. Martens, M., Zein, J. (2004). Predicting financial volatility: High-frequency time-series forecasts vis-`a-vis implied volatility. Journal of Futures Markets, 24, 1005–1028.
51. Martens, M. (2002). Measuring and forecasting S&P 500 index‐futures volatility using high‐frequency data. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 22(6), 497-518.
52. McMillan, D. G., Speight, A. E. H. (2004). Daily volatility forecasts: Reassessing the performance of GARCH models. Journal of Forecasting, 23(6), 449-460.
53. Mensi, W., Boubaker, F.Z., Al-Yahyaee, K.H., Kang, S.H. (2018). Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets. Finance Res. Lett., 25, 230–238.
54. Merton, Robert C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8, 1-39.
55. 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) (1997), 213-239.
56. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370.
57. Pagan, Adrian R., G. William Schwert. (1990). Alternative models for Conditional Stock Market Volatility, Econometrics, 45(1-29), 267-90.
58. Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. J. Bus. 53, 61–65.
59. Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics, 160(1), 246-256.
60. Patton, A. J., Sheppard, K. (2015). Good volatility, bad volatility: Signed jumps and the persistence of volatility. The Review of Economics and Statistics, 97(3), 683-697.
61. Patton, A. J., Sheppard, K. (2009). Optimal combinations of realized volatility estimators. International Journal of Forecasting, 25(2), 218-238.
62. Poon, Ser-Huang, Clive Granger, W.J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2 (June)), 478–539.
63. Rejeb, A. B., Arfaoui, M. (2016). Financial market interdependencies: A quantile regression analysis of volatility spillover. Research in International Business and Finance, 36, 140–157.
64. Rogers, L.C.G., Satchell, S.E., (1991). Estimating variance from high, low, and closing Prices. Ann. Appl. Probab, 1, 504–512.
65. Sevi, B. (2014). Forecasting the volatility of crude oil futures using intraday data. European Journal of Operational Research, 235, 643–659.
66. Shephard, N., Sheppard, K. (2010). Realising the future: Forecasting with high‐frequency‐based volatility (HEAVY) models. Journal of Applied Econometrics, 25(2), 197-231.
67. Shiller, R. J. (1989). Co-movements in stock prices and co-movements in dividends. The Journal of Finance, 44(3), 719–729.
68. Slim, S., Tabche, I., Koubaa, Y., Osman, M., Karathanasopoulos, A. (2023). Forecasting realized volatility of Bitcoin: The informative role of price duration. Journal of Forecasting, 1– 21.
69. Tse, Y. K. (1991). Stock return volatility in the Tokyo stock exchange. Japan and the World Economy, (3), 285-298.
70. Tse, Y. K., Tung, S. H. (1992). Forecasting volatility in the Singapore stock market. Asia Pacific Journal of Management, 9, 1-13.
71. Yang, D., Zhang, Q. (2000). Drift independent volatility estimation based on high, low, open and closes prices. J. Bus. 73, 477–491.
72. Ding, Y., Kambouroudis, Dimos S., McMillan, David G. (2023). Forecasting Realised Volatility Using Regime-Switching Models. Available at SSRN: https://ssrn.com/abstract=4415386 or http://dx.doi.org/10.2139/ssrn.4415386.
73. Yu, L., Li, J., Tang, L. (2015). Dynamic volatility spillover effect analysis between carbon market and crude oil market a DCC-ICSS approach. Int. J. Global Energy Issues 38, 242–256.
74. Wu, X., Hou, X. (2019). Forecasting realized variance using asymmetric HAR model with time-varying coefficients. Finance Research Letters, 30, 89-95.
75. Zhang, Y., He, M., Wang, Y., & Wen, D. (2025). Model specification for volatility forecasting benchmark. International Review of Financial Analysis, 97, 103850.