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Sains Malaysiana 46(1)(2017): 107–116 http://dx.doi.org/10.17576/jsm-2017-4601-14
The HARX-GJR-GARCH skewed-t multipower
realized volatility modelling for S&P 500
(Pemodelan Kemeruapan Terealisasi Pelbagai-Kuasa
HARX-GJR-GARCH terpencong-t untuk S&P 500)
CHIN WEN CHEONG1*,
LEE MIN CHERNG2, NADIRA MOHAMED ISA1 3 & POO KUAN
HOONG4
1Faculty of Management,
Multimedia University, 63100 Cyberjaya, Selangor Darul Ehsan, Malaysia
2Lee Kong Chian Faculty of
Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus,
Jalan Sungai Long, Bandar Sungai Long, 43000 Kajang, Selangor Darul Ehsan, Malaysia
3Faculty of Science and
Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul
Ehsan, Malaysia
4The Nielsen Company (M)
Sdn. Bhd., 46100 Petaling Jaya, Selangor Darul Ehsan, Malaysia
Received: 8 October
2015/Accepted: 25 April 2016
ABSTRACT
The heterogeneous autoregressive (HAR)
models are used in modeling high frequency multipower realized volatility of
the S&P 500 index. Extended from the standard realized volatility, the
multipower realized volatility representations have the advantage of handling
the possible abrupt jumps by smoothing the consecutive volatility. In order to
accommodate clustering volatility and asymmetric of multipower realized
volatility, the HAR model is extended by the threshold
autoregressive conditional heteroscedastic (GJR-GARCH)
component. In addition, the innovations of the multipower realized volatility
are characterized by the skewed student-t distributions. The extended model
provides the best performing in-sample and out-of-sample forecast evaluations.
Keywords: GARCH; HAR;
realized volatility
ABSTRAK
Model autoregresi
heterogen (HAR) digunakan dalam pemodelan kemeruapan
terealisasi pelbagai-kuasa untuk indeks S&P500. Lanjutan daripada kemeruapan terealisasi
piawai, kemeruapan pelbagai-kuasa mempunyai kelebihan menangani
kemungkinan perubahan mendadak dengan pelicinan kemeruapan berturutan.
Untuk permodelan kemeruapan kelompok dan tak
simetri, model HAR dilanjutkan
dengan komponen autoregresi heteroskedastik bersyarat ambang (GJR-GARCH).
Selain itu, inovasi kemeruapan terealisasi
dicirikan dengan taburan student-t terpencong. Model
lanjutan HAR
memberi prestasi terbaik dalam penilaian penganggaran
dan ramalan.
Kata
kunci: GARCH; HAR;
kemeruapan terealisasi REFERENCES
Andersen, T.G.,
Bollerslev, T. & Diebold, F.X. 2006. Roughing it up: Including jump
components in the measurement, modeling and forecasting of return volatility. Review
of Economics and Statistics 89: 701-720.
Andersen,
T. & Bollerslev, T. 1998. Answering the skeptics: Yes, standard
volatility models do provide accurate forecasts. International Economic
Review 39: 885-906.
Barndorff-Nielsen,
O.E., Graversen, S.E., Jacod, J., Podolskij, M. & Shephard, N. 2006. A central limit theorem
for realised power and bipower variations of continuous semi-martingales. Journal
of Financial Econometrics 2(1): 1-37.
Barndorff-Nielsen,
O.E. & Shephard, N. 2002. Estimating quadratic variation using realised
volatility. Journal of Applied Econometrics 17: 457-477.
Barndorff-Nielsen,
O.E. & Shephard, N. 2004. Power and bipower variation
with stochastic volatility and jumps. Journal of Financial
Econometrics 2(1): 1-37.
Bollerslev,
T. & Ghysels, E. 1996. Periodic autoregressive conditional heteroscedasticity. Journal
of Business & Economic 14(2): 139-151.
Cervelló, R.R., Guijarro, F. &
Michniuk, K. 2015. Stock market trading rule based on pattern recognition and
technical analysis: Forecasting the DJIA index with intraday data. Expert System and Application 42(14):
5963-5975.
Cheong, C.W., Lee, M.C. & Grace Yap, L.C. 2016a. Heterogeneous autoregressive model with structural break
using nearest neighbor truncation volatility estimators for DAX. SpringerPlus 5(1883): 1-13.
Cheong, C.W., Lee, M.C. & Grace Yap, L.C. 2016b. Modelling financial market volatility
using asymmetric-skewed-ARFIMAX and -HARX models. Inžinerinė Ekonomika 27(4):
373-381.
Cheong, C.W. 2013. The
computational of stock market volatility from the perspective of heterogeneous
market hypothesis. Economic Computation and Economic Cybernetics Studies and
Research 47(2): 247-260.
Corsi, F. 2009. A simple
approximate long memory model of realized volatility. Journal of Financial
Econometrics 7: 174-196.
Corsi,
R., Mittnik, S., Pigorsch, C. & Pigorsch, U. 2008. The volatility of
realized volatility. Econometric Reviews 27: 46-78.
Dacorogna, M., Ulrich,
M., Richard, O. & Oliveier, P. 2001. Defining efficiency in heterogeneous
markets. Quantitative Finance 1: 198-201.
Degiannakis,
S. & Floros, C. 2013. Modeling. CAC40 volatility
using ultra-high frequency data. Research in International Business
and Finance 28: 68-81.
Fama,
E. 1998. Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics 49: 283-306.
Glosten, L.R.,
Jaganathan, R. & Runkle, D. 1993. On the relation between
the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance 8: 1779-1801.
Hong,
Y., Li, H. & Zhao, F. 2004. Out-of-sample performance of discrete-time spot
interest rate models. Journal of Business & Economic Statistics 22(4):
457-473.
Inkaya, Y. & Oku, Y.
2014. Analysis of volatility feedback and leverage effects on
the ISE30 index using high frequency data. Journal of Computational
and Applied Mathematics 259: 377-384.
Jorion, P. 2006. Value-at-Risk:
The New Benchmark for Controlling Market Risk. 3rd ed. Chicago:
McGraw-Hill.
Lambert, P. &
Laurent, S. 2001. Modelling financial time Series Using GARCH-Type models and a
skewed student density. Mimeo, Universit?e de Li?ege.
Malkiel, B.G. 2003. The
efficient market hypothesis and its critics. The Journal of Economic
Perspectives 17(1): 59-82.
Muller,
U., Dacorogna, M., Dav, R., Olsen, R., Pictet, O. & Ward, J. 1993. Fractals and intrinsic
time - a challenge to econometricians. XXXIX-th International AEA
Conference on Real Time Econometrics. pp. 14-15.
Patton, A.J. 2011.
Volatility forecast comparison using imperfect volatility proxies. Journal
of Econometrics 160(1): 246-256.
Wang,
X., Wu, C. & Xu, W. 2015. Volatility forecasting: The role of lunch-break
returns, overnight returns, trading volume and leverage effects. International
Journal of Forecasting 31: 609-619.
Zu, Y. & Boswijk,
H.P. 2014. Estimating spot volatility with high frequency financial data. Journal
of Econometrics 181(2): 117-135.
*Corresponding
author; email: wcchin@mmu.edu.my
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