Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Long memory or structural break? Empirical evidences from index volatility in stock market

Long memory or structural break? Empirical evidences from index volatility in stock market The purpose of this paper is to explore whether stock index volatility series exhibit real long memory.Design/methodology/approachThe authors employ sequential procedure to test structural break in volatility series, and use DFA and 2ELW to estimate long memory parameter for the whole samples and subsamples, and further apply adaptive FIGARCH (AFIGARCH) to describe long memory and structural break.FindingsThe empirical results show that stock index volatility series are characterized by long memory and structural break, and therefore it is appropriate to use AFIGARCH to model stock index volatility process.Originality/valueThis study empirically investigates the properties of long memory and structural break in stock index volatility series. The conclusion has a certain reference value for understanding the properties of long memory and structural break in volatility series for academic researchers, market participants and policy makers, and for modeling and forecasting future volatility, testing market efficiency, pricing financial assets, constructing quantitative investment strategy and measuring market risk. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png China Finance Review International Emerald Publishing

Long memory or structural break? Empirical evidences from index volatility in stock market

Luo, Yi; Huang, Yirong
China Finance Review International , Volume 9 (3): 14 – Aug 16, 2019
Download PDF
14 pages

Loading next page...
 
/lp/emerald-publishing/long-memory-or-structural-break-empirical-evidences-from-index-88Wb8uQT83
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2044-1398
DOI
10.1108/cfri-11-2017-0222
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to explore whether stock index volatility series exhibit real long memory.Design/methodology/approachThe authors employ sequential procedure to test structural break in volatility series, and use DFA and 2ELW to estimate long memory parameter for the whole samples and subsamples, and further apply adaptive FIGARCH (AFIGARCH) to describe long memory and structural break.FindingsThe empirical results show that stock index volatility series are characterized by long memory and structural break, and therefore it is appropriate to use AFIGARCH to model stock index volatility process.Originality/valueThis study empirically investigates the properties of long memory and structural break in stock index volatility series. The conclusion has a certain reference value for understanding the properties of long memory and structural break in volatility series for academic researchers, market participants and policy makers, and for modeling and forecasting future volatility, testing market efficiency, pricing financial assets, constructing quantitative investment strategy and measuring market risk.

Journal

China Finance Review International – Emerald Publishing

Published: Aug 16, 2019

Keywords: Stock market; Volatility; Long memory; Structural break; AFIGARCH

References

  • Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models
    Arouri, M.E.H.; Lahiani, A.; Lévy, A.; Nguyen, D.K.
  • Estimating and testing linear models with multiple structural changes
    Bai, J.; Perron, P.
  • Computation and analysis of multiple structural change models
    Bai, J.; Perron, P.
  • Modeling long memory and structural breaks in conditional variances: an adaptive FIGARCH approach
    Baillie, R.T.; Morana, C.
  • Fractionally integrated generalized autoregressive conditional heteroskedasticity
    Baillie, R.T.; Bollerslev, T.; Mikkelsen, H.O.
  • The detection and estimation of long memory in stochastic volatility
    Breidt, F.J.; Crato, N.; de cLima, P.
  • True or spurious long memory in volatility: further evidence on the energy futures markets
    Charfeddine, L.
  • Breaks or long memory behavior: an empirical investigation
    Charfeddine, L.; Guégan, D.
  • Long memory in log-range series: do structural breaks matter
    Chatzikonstanti, V.; Venetis, I.A.
  • Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M model
    Christensen, B.J.; Nielson, M.Ø.; Zhu, J.
  • Long memory and regime switching
    Diebold, F.X.; Inoue, A.
  • The estimation and application of long memory time series models
    Geweke, J.; Porter-Hudak, S.
  • On the power of R/S-type tests under contiguous and semi-long memory alternatives
    Giraitis, L.; Kokoszka, P.; Leipus, R.; Teyssière, G.
  • An introduction to long memory time series and fractionally differencing
    Granger, C.W.J.; Joyeux, R.
  • Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns
    Granger, C.W.J.; Hyung, N.
  • Long-term storage capacity of reservoirs
    Hurst, H.E.
  • Improving GARCH volatility forecasts with regime-switching GARCH
    Klassen, F.
  • Robustness of the rescaled range R/S in the measurement of noncyclic long-run statistical dependence
    Mandelbrot, B.B.; Wallis, J.R.
  • Forecasting stock market volatility with regime-switching GARCH models
    Marcucci, J.
  • Long memory or structural breaks: Some evidence for African stock markets
    Ngene, G.; Tah, K.A.; Darrat, A.F.
  • Persistence in crude oil spot and futures prices
    Ozdemir, Z.A.; Gokmenoglu, K.; Ekinci, C.
  • Quantification of scaling exponents and crossover phenonmena in nonstationary heartbeat time series
    Peng, C.K.; Havlin, S.; Staneley, H.E.; Goldberger, A.L.
  • Mosaic organization of DNA nucleotides
    Peng, C.K.; Buldyrev, S.V.; Havlin, S.; Simons, M.; Stanley, H.E.; Goldberger, A.L.
  • Long-memory and level shifts in the volatility of stock market return indices
    Perron, P.; Qu, Z.
  • Gaussian semiparametric estimation of long range dependence
    Robinson, P.M.
  • Exact local Whittle estimation of fractional integration with unknown mean and time trend
    Shimotsu, K.
  • Exact local Whittle estimation of fractional integration
    Shimotsu, K.; Phillips, P.C.B.
  • Switching volatility in private international equity markets
    Susmel, R.
  • Real or spurious long memory characteristics of volatility: empirical evidence from an emerging market
    Yalama, A.; Celik, S.
  • Persistence of volatility in futures markets
    Chen, Z.; Daigler, R.T.; Parhizgari, A.M.
  • Effect of trends on detrended fluctuation analysis
    Hu, K.; ChIvanov, P.; Chen, Z.; Carpera, P.; Stanley, H.E.