The volatility model of the ASEAN Stock Indexes
-
DOIhttp://dx.doi.org/10.21511/imfi.16(1).2019.18
-
Article InfoVolume 16 2019, Issue #1, pp. 226-238
- Cited by
- 1085 Views
-
181 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This research study examines the characteristics of the Association of Southeast Asian Nations (ASEAN) volatility of stock indexes. The following models are used in this research: Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH), Glosten Jaganathan Runkle Generalized Autoregressive Conditional Heteroscedasticity (GJR-GARCH), and Multifractal Model of Asset Return (MMAR). The research also used the data from the ASEAN country members’ (the Philippines, Indonesia, Malaysia, Singapore, and Thailand) stock indexes for the period from January 2002 until 31 January 2016 to determine the suitable model.
Meanwhile, the results of the MMAR parameter showed that the returns of the countries have a characteristic called long-term memory. The authors found that the scaling exponents are associated with the characteristics of the specific markets including the ASEAN member countries and can be used to differentiate markets in their stage of development.
Finally, the simulated data are compared with the original data by scaling function where most of the stock markets of the selected ASEAN countries have long-term memory with the scaling behavior of information asymmetry. Some of the countries such as the Philippines and Indonesia have their own alternative models using GARCH and EGARCH due to the possibility of leverage. Generally, MMAR is the best model for use in ASEAN market, because this model considered Hurst exponent as a parameter of long-term memory that indicates persistent behavior.
- Keywords
-
JEL Classification (Paper profile tab)G15, G32, G4
-
References28
-
Tables9
-
Figures4
-
- Figure 1. The return index of (a) the Philippines, (b) Indonesia, (c) Malaysia, (d) Singapore, and (e) Thailand (January 1, 2002 – January 31, 2016)
- Figure 2. The scaling function of the nonlinear return sequence that shows an existing series of returns is multifractal
- Figure 3. Partition function each country parallel with the horizontal axis on range two
- Figure 4. The path of standard deviation shows the MMAR model has the smallest value compared to other models
-
- Table 1. Gross Domestic Product of ASEAN member countries
- Table 2. The model return series estimation results
- Table 3. Mono-fractal H for origin series
- Table 4. Return series estimation results using GARCH model
- Table 5. Return series estimation results using EGARCH model
- Table 6. Order q of returning series
- Table 7. Return series estimation results using multifractal model
- Table 8. Standard deviation from difference between original and simulated
- Table 9. Scaling function
-
- Alexander, C. (2001). Market models: a guide to financial data analysis. John Wiley & Sons, Chichester, UK.
- Alexander, C. (2008). Market risk analysis. Volume IV, Valueat-risk models. John Wiley & Sons, Chichester, UK.
- Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3-30.
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
- Campbell, J. Y., & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of financial Economics, 31(3), 281-318.
- Day, T. E., & Lewis, C. M. (1992). Stock market volatility and the information content of stock index options. Journal of Econometrics, 52(1-2), 267-287.
- Dedi, L., & Yavas, B. F. (2016). Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies. Cogent Economics & Finance, 4(1).
- Dennis, J. E., & Schnabel, R. B. (1996). Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Society for Industrial and Applied Mathematics.
- Di Matteo, T., Aste, T., & Dacorogna, M. M. (2005). Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development. Journal of Banking & Finance, 29(4), 827-851.
- Engle, R. F., & Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews, 5(1), 1-50.
- Fama, E. F. (1990). Stock Returns, Expected Returns, and Real Activity. The Journal of Finance, 45(4), 1089-1108.
- Fillol, J. (2003). Multifractality: theory and evidence an application to the French stock market. Economics Bulletin, 3(31), 1-12.
- 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. The Journal of Finance, 48(5), 1779-1801.
- Goldstein, A. A. (1967). Constructive real analysis – CERN Document Server. New York.
- Günay, S. (2016). Performance of the multifractal model of asset returns (MMAR): evidence from emerging stock markets. International Journal of Financial Studies, 4(2), 11.
- Hurst, H. E. (1956). The problem of long-term storage in reservoirs. Hydrological Science Journal, 1(3), 13-27.
- Jamdee, S., & Los, C. A. (2005). Multifractal modeling of the US treasury term structure and fed funds rate. SSRN Electronic Journal.
- Kim, B.-S., Kim, H.-S., & Min, S.-H. (2014). Hurst’s memory for chaotic, tree ring, and SOI series. Applied Mathematics, 5(1), 175-195.
- Kirchler, M., & Huber, J. (2007). Fat tails and volatility clustering in experimental asset markets. Journal of Economic Dynamics and Control, 31(6), 1844-1874.
- Liu, H.-C., & Hung, J.-C. (2010). Forecasting S&P-100 stock index volatility: the role of volatility asymmetry and distributional assumption in GARCH models. Expert Systems with Applications, 37(7), 4928-4934.
- Mandelbrot, B. B., Fisher, A. J., & Calvet, L. E. (1997). A multifractal model of asset returns (Cowles Foundation Discussion No. 1164, Sauder School of Business Working Paper).
- Matos, J. A. O., Gama, S. M. A., Ruskin, H. J., Al Sharkasi, A., & Crane, M. (2008). Time and scale Hurst exponent analysis for financial markets. Physica A: Statistical Mechanics and Its Applications, 387(15), 3910-3915.
- Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: a new approach. Econometrica, 59(2), 347-370.
- Peters, E. E. (1994). Fractal market analysis: applying chaos theory to investment and economics. John Wiley & Sons.
- Satchell, S., & Knight, J. (2011). Forecasting volatility in the financial markets (Butterworth-Heinemann, 3rd ed.). Elsevier.
- Thomas, C. M. (2002). Chaos theory versus the efficient market hypothesis in financial markets. University of Tennessee.
- Toggins, W. N. (2008). New conometric modelling research. New York: Nova Science Publishers.
- Tsay, R. S. (2005). Analysis of financial time series. John Wiley & Sons.