تحلیل رفتار متغیر تلاطم تحقق‎یافته در بورس اوراق بهادار تهران مبتنی بر رهیافت مدل‎های خودرگرسیونی ناهمگن

نوع مقاله : مقاله علمی پژوهشی

نویسنده

استادیار، گروه مهندسی مالی، دانشکده مهندسی صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران. ایران

چکیده

هدف: هدف این پژوهش، بررسی رفتار متغیر تلاطم تحقق‎یافته در ارتباط با داده‎های با فراوانی زیاد شاخص سهام بورس اوراق بهادار تهران، در فاصله زمانی 9 اردیبهشت 1391 تا 17 مرداد 1397 است.
روش: برای دستیابی به هدف پژوهش و تحلیل و بررسی رفتار متغیر تلاطم تحقق‎یافته، از سه گونه مختلف مدل‎های خودرگرسیونی ناهمگن، شامل HAR-RV-CJ، HAR-RV و HAR-RVJ استفاده شده است.
یافته‎ها: نتایج به‎دست آمده از سه مدل مختلف نشان می‎دهد که تلاطم تحقق‎یافته تخمینی در بازار، به نحو مطلوبی از طریق معامله‎گرانی که به‎صورت روزانه و در چارچوب مدل HAR-RVJ فعالیت می‎کنند، توضیح داده شده است. علاوه بر این، منبعث از فرضیه مشهور بازار ناهمگن، درمی‎یابیم که در مقایسه عملکردی تمام افق‎های زمانی مطالعه، مقادیر مربوط به چهار معیار ارزیابی (شامل RMSE، MAE و غیره) در مدل فوق از مدل‎های HAR-RV-CJ و HAR-RV کمتر است.
نتیجه‎گیری: عملکرد پیش‎بینی درون نمونه‎ای در مدل HAR-RVJ و در ارتباط با متغیر تلاطم آتی شاخص بورس اوراق بهادار تهران، از آنچه در مدل‎های HAR-RV و HAR-RV-CJ به‎دست آمده است، بهتر بوده و بین تمام معیارها بیشترین امتیاز را کسب کرده است. همچنین در حالت بررسی برون نمونه‎ای نیز باید گفت که فقط در افق زمانی ماهانه، مدل ساده HAR-RV نسبت به دو مدل دیگر برتری داشته است.
 

کلیدواژه‌ها

عنوان مقاله [English]

Analysis of Realized Volatility in Tehran Stock Exchange using Heterogeneous Autoregressive Models Approach

نویسنده [English]

  • Majid Mirzaee
Assistant Prof., Department of Financial Engineering, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
چکیده [English]

Objective: The present study aims atinvestigating the behavior of realized volatility for high-frequency data of Tehran Stock Index from April28th, 2012 to August 8th, 2018.
Methods: Three different types of HAR models including of HAR-RV-CJ, HAR-RV and HAR-RVJ were used to analyze the Realized Volatility.
Results: The obtained results of three diverse models revealed that the estimated Realized Volatility in market was described appropriately by the traders who work daily and in the framework of HAR-RVJ model. Moreover, based on the Heterogeneous Market Hypothesis, we found out that in comparative performance for all of time horizons in this study, the results of four evaluative criteria (including of MSE, RMSE and etc.) in HAR-RVJ model is lower than HAR-RV-CJandHAR-RV.
Conclusion: The in-sample forecasting performance of HAR-RVJ, in relation to Future Volatility of Tehran Stock Exchange Index, was better than the results we obtained from the alternative models in the study (HAR-RVandHAR-RV-CJ) and the best scores were observed among all the criteria. In addition, for the out-of-sample analysis, the simple HAR-RV model had superiority over the other two models only in the Monthly time horizon.

کلیدواژه‌ها [English]

  • Heterogeneous autoregressive model
  • Realized volatility
  • Heterogeneous market hypothesis
  • High-frequency data
  • Jump

مراجع

ابونوری، اسمعیل؛ عبداللهی، محمدرضا (1391). مدل‎سازی نوسانات بخش‎های مختلف بازار سهام ایران با استفاده از مدل گارچ چندمتغیره. فصلنامه تحقیقات مالی، 14(1)، 1-16.
سید حسینی، سیدمحمد؛ ابراهیمی، سید بابک (1392). مدل‎سازی مقایسه‎ای سرایت تلاطم با در نظرگرفتن اثر حافظه بلندمدت (مطالعه موردی: سه شاخص منتخب صنایع). فصلنامه تحقیقات مالی، 15(1)، 51-74.
کشاورز حداد، غلامرضا؛ بابایی، آرش (1390). مدل‎سازی تلاطم بازده نقدی در بورس سهام تهران با استفاده از داده‎های پانل و مدل GARCH.فصلنامه تحقیقات مالی، 13(31)، 41-72.
نبوی چاشمی، سید علی؛ مختاری نژاد، ماریه (1395) مقایسه مدل‎های حرکت براونی و براونی کسری و گارچ در برآورد نوسانات بازده سهام.مجلهمهندسیمالیومدیریتاوراقبهادار، 29(3)، 25-44.
 
References
Abonouri, E., Abdollahi, M. (2012). Modeling Volatility of Iran Stock Exchange by using Multivariate GARCH model. Journal of Financial Research, 14(1), 1-16. (in Persian)
Andersen, T. G., & Bollerslev, T. (1997). Heterogeneous information arrivals and return volatility dynamics: Uncovering the long‐run in high frequency returns. The journal of Finance, 52(3), 975-1005.
Andersen, T., Bollerslev, T., Diebold, F. X. & Labys, P. (2003). Modeling and Forecasting Realized Volatility. Econometrica, 71(2), 529–626.
Andersen, T., Bollerslev, T., Diebold, F. X., & Labys, P. (1999). The distribution of exchange rate volatility. NBER Working Paper, No. 6961. 
Andersen, T.G., Bollerslev, ‎T., & ‎Diebold, F.X.‎ (2007). Roughing it up: ‎Including jump components in the measurement, modeling, and forecasting of return volatility. Review of Economics and Statistics, 89(4), 701–720.
Awartani, B., Corradi, V., & Distaso, W. (2009). Assessing market microstructure effects via realized volatility measures with an application to the Dow jones industrial average stocks. Journal of Business & Economic Statistics, 27(2), 251-265.
Back, K. (1991). Asset pricing for general processes. Journal of Mathematics Economics, 20(4), 371–395.
Barndorf-Nielsen, O.‎E. & Shephard, N. (2004b). Realized power variation and stochastic volatility. Bernoulli, 9(2), 243-265.
Barndorf-Nielsen, O.‎E. & Shephard, N. (2006). Econometrics of testing for jumps in financial economics using bi-power variation. Journal of Financial Econometrics, 4(1), 1–30.
Barndorf-Nielsen, ‎O.‎E., & Shephard, ‎N.‎ (2004a).‎ Power and bi-power variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2(1), ‎1-37.
Bauwens, L., Rime, D., & Sucarrat, G. (2006). Exchange rate volatility and the mixture of distribution hypothesis. Empirical Economics, 30(4), 889-911.
Bhattacharyya, M., Kumar, M, D., & Kumar, R. (2009). Optimal sampling frequency for volatility forecast models for the Indian stock markets. Journal of Forecasting, 28(1), 38-54.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Chevallier, J. & Sévi, B. (2009). On the realized volatility of the ECX CO2 emissions 2008 future contract: distribution, dynamics and forecasting. Working Paper.
Chung, H.M., Huang, C.S., & Tseng, T.C. (2008). Modeling and forecasting of realized volatility based on high-frequency data: evidence from Taiwan. International Research Journal of Finance and Economics, 22(3), 178-191.
Clements, A., & Liao, Y. (2013). Modeling and forecasting realized volatility: getting the most out of the jump component (No. 93). National Centre for Econometric Research.
Corsi, F. (2004). A simple long memory model of realized volatility. Working paper, University of Southern Switzerland.
Corsi, F., (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174–196.
Corsi, F., Mittnik, S., & Pigorsch, U. (2008). The volatility of realized volatility. Econometric Reviews, 27(1-3), 46-78.
Corsi, F., Pirino, D. & Reno, R. (2009). Volatility Forecasting: The Jumps Do Matter. Working paper. Available at: http://ssrn.com/abstract=1115783.
Darrat, A. F., Rahman, S., & Zhong, M.‎ (2003). Intraday trading volume and return volatility of the DJIA stocks: A note. Journal of Banking and Finance, 27(10), 2035-2043.
Engle, R.‎F.‎ (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation. Econometrica, 50(4), 987-1008.
Ghysels, E., & Sinko, A. (2006). Comment on Hansen and Lunde JBES paper. Journal of Business and Economic Forecasting, 26(2), 192-194.
Ghysels, E., Santa-Clara, P., & Valkanov, R. (2006). Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131(1-2), 59-95.
Holmes, P. & Tomsett, M. (2004). Information and noise in UK futures markets. Journal of Futures Markets, 24(8), 711-731.
Huang, C., Gong, X., Chen, X., & Wen, F. (2013). Measuring and forecasting volatility in Chinese stock market using HAR-CJ-M model. In Abstract and Applied Analysis (Vol. 2013). Hindawi.
Kalev, P.‎S., Liu, W. M., Pham, ‎P.‎K. (2004). Public information arrival and the volatility of intraday stock returns. Journal of Banking and Finance, 28(6), 1441-1467.
Keshavarz Hadad., Gh., Babaei., A. (2011). Modeling the return Volatility in Tehran Stock Exchange by Panel Data and GARCH model. Journal of Financial Research, 13(31), 41-72.  (in Persian)
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(3), 445–475.
Luu, J.‎C., Martens, M. (2003). Testing the Mixture-of-distributions Hypothesis using 'Realized Volatility'. Journal of Futures Markets, 23(7), 661-679.
Lux,‎ T. & Marchesi, M. (1999). Scaling and criticality in a stochastic multi-agent model of financial market. Nature, 397(6719), 498.
McAleer, M., Medeiros, M.‎C.‎ (2008). A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries. Journal of Econometrics, 147(1), 104-119.
Müller, ‎U., Dacorogna, M.,‎ Dav´e, R.,‎ Olsen, R., Pietet, O., & von Weizsacker, J. (1997). Volatilities of different time resolutions – analyzing the dynamics of market components. Journal of Empirical Finance, 4(2-3), 213–239.
Nabavi Chashemi, A. & Mokhtari Nejad, M. (2016). Comparison of Brownian Motion, Fractional Motion and GARCH Models to estimate Volatility of Stock returns. Journal of Financial Engineering and Portfolio Management, 29(3), 25-44. (in Persian)
Oomen, R. (2001). Using high frequency stock market index data to calculate, model and forecast realized return variance. Economics Discussion Paper No. 2001/6, European University.
Seďa, P. (2012). Performance of Heterogeneous Autoregressive Models of Realized Volatility: Evidence from US Stock Market. World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 6(12), 3421-3428.
Seyed Hosseini, S. & Ebrahimi, B. (2013). Comparative Modeling of Volatility Spillover with Long Memory Effect (case study: three selected industry). Journal of Financial Research, 15(1), 51-74. (in Persian)
Taylor, S.J. (1994). Modeling Stochastic Volatility: A Review and Comparative Study. Mathematical Finance, 4(2), 183-204.
Wink Júnior, M. V., & Pereira, P. L. V. (2011). Modeling and Forecasting of Realized Volatility: Evidence from Brazil. Brazilian Review of Econometrics, 31(2), 315-337.
تحقیقات مالی
دوره 20، شماره 3
1397
صفحه 365-388

فایل ها

هم رسانی

ارجاع به این مقاله

آمار