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Dr. Subrata Roy is currently working as Assistant Professor, Department of Commerce at Rabindra Mahavidyalaya Affiliated to the University of Burdwan, Champadanga, Hooghly, West Bengal, India. He can be reached at subrata1_roy@yahoo.com
This study deals with the various asymmetric effects and market efficiency of the traditional and sustainable indices based on daily returns during the period from December 1998 to March 2015. Here, Dow Jones Sustainability Index US [socially responsible investment (SRI) index] and Dow Jones World US (conventional index) indices are considered. The well-known volatility forecasting measures like autoregressive conditional heteroskedasticity (ARCH), Generalised Autoregressive Conditional Heteroskedasticity, Exponential Generalised Autoregressive Conditional Heteroskedasticity and threshold ARCH (TARCH) are applied to attain the objectives. ARCH measure confirms about volatility clustering in the indices. Significant asymmetric shocks and persistence of conditional volatilities are present in the daily returns of the indices. According to the EGARCH measure, the return of the DJSI US is free from the leverage effect, whereas in TARCH measure, leverage effect exists in both the indices. Here, TARCH measure is the best forecasting measure for the DJSI US index because it provides lowest root mean squared error, mean absolute error and mean absolute percent error. But in case of DJ World, EGARCH measure is superior. Finally, the returns of the SRI and conventional indices follow the characteristics of random walk hypothesis that means the indices are informationally efficient at their weak forms, and no one can predict the stock price movements and cannot earn abnormal profits by applying technical analysis.
SRI, DJSI, ARCH, GARCH, EGARCH, TARCH, Market efficiency
JEL Classification Codes: G01, G11, G14, M14
Akgiray, V. (1989). Conditional heteroscedasticity in time series of stock returns. Journal of Business, Vol. 62, pp. 55–80.
Ameur, H.B. and Senanedsch, J. (2014). Socially responsible investments: an international empirical study of time-varying risk premiums, Vol. 30, pp. 1513–1524.
Baillie, R.T. (1997). Time dependent conditional heteroscedasticity. Manuscript, Workshop of Time Series Analysis, Arrabida, Portugal.
Black, F. (1976). Studies of stock price volatility changes’, Proceedings of the 1976 Business Meeting of the Business and Economcs Statistics Section, American Statistical Association, Business and Economic Statistics Section, pp. 177–181.
Bollerslev, T. (1986a). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, Vol. 31, pp. 307–327.
Bollerslev, T. (1986b). A conditional heteroscedasticity time series model for speculative prices and rates of returns. Review of Economics and Statistics, Vol. 69, pp. 542–547
Bollerslev, T., Chou, R.Y. and Kroner, K.F. (1992). ARCH modelling in finance. Journal of Econometrics, Vol. 52, pp. 5–59.
Bollerslev, T., Engle, R.F. and Nelson, D.B. (1994). ARCH models’, in Handbook of Econometrics, Vol. 4, eds. R. F. Engle and D. McFadden, Amsterdam: North-Holland.
Box, G.E.P. and Jenkins, G. (1970). Time series analysis, forecasting and control. Holden-Day, San Francisco.
Brandt, W.M. and Jones, S.C. (2006). Volatility forecasting with range-based EGARCH models. Journal of Business & Economic Statistics, Vol. 24, pp. 470–486.
Brock, W., Dechert, W.D. and Scheinkman, J.A. (1987). A test for independence based on correlation dimension. Working paper, University of Wisconsin at Madison, University of Houston and University of Chicago.
Brock, W., Dechert, W.D., Scheinkman, J.A. and Baron, L.B. (1996). A test for independence based on correlation dimension. Economic Reviews, Vol. 15, pp. 197-235.
Chen, Y.W. and Lian, K.K. (2005). A comparison of forecasting models for ASEAN equity markets. Sunway Academic Journal, Vol. 2, pp. 1–12.
Chou, R.Y. (1998). Volatility persistence and stock valuations: some empirical evidence using GARCH. Journal of Applied Econometrics, Vol. 3, pp. 279–294.
Christie, A.A. (1982). The stochastic behaviour of common stock variances: value, leverage and interest rate effect. Journal of Financial Economics, Vol. 10, pp. 407–432.
Conrad, K. and Juttner, D.J. (1973). Recent behaviour of stock market prices in Germany and the random walk hypothesis. Kyklos, Vol. 26, pp. 576–599.
Consolandi, C., Jaiswal-Dale, A., Poggiani, E. and Vercelli, A. (2008). Global standards and ethical stock indexes: the case of the Dow Jones sustainability Stoxx index. Journal of Business Ethics, Vol. 87, pp. 185–197.
Cooper, J.C.B. (1982). World stock markets: some random walk tests. Applied Economics, Vol. 14, pp. 515-531
Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, Vol. 50, pp. 987–1008.
Engle, R.F. and Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews, Vol. 5, pp. 1–50.
Engle, R.F. and Ng, V.K. (1993). Measuring and testing the impact of news on volatility. Journal of Finance, Vol. 48, pp. 1022–1082.
Engle, R.F., Focardi S.M. and Fabozzi, F.J. (2007). ARCH/GARCH Models in Applied Financial Econometrics. JWPR026-Fabozzi c114-NP, pp. 1–14.
Engle, R.F., Lilien, D.M. and Robins, R.P. (1987). Estimating time varying risk premia in the term structure: the ARCH-M model. Econometrica, Vol. 55, pp. 391–407.
Fama, E.F. (1965a). The behaviour of stock market prices. Journal of Business, Vol. 38, pp. 34–105.
Fama, E.F. (1965b). Tomorrow on the New York Stock Exchange. Journal of Business, Vol. 38, pp. 285–299.
Fama, E.F. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, Vol. 25, pp. 383–417.
Franses, P.H. (1998). Time series models for business and economic forecasting. Cambridge University Press.
Frennberg, P. and Hansson, B. (1993). Testing the random walk hypothesis on Swedish stock prices 1919–1990. Journal of Banking and Finance, Vol. 17, pp. 175-191.
Garz, H., Volk, C. and Gilles, M. (2002). More Gain than Pain – SRI: Sustainability Pays Off. West LB Panmure, http://www.westlbpanmure.com/sri/pdf/sri_nov2002.pdf.
Gazda, V. and Vyrost, T. (2003). Application of GARCH models in forecasting the volatility of the Slovak share index (SAX). BIATEC, Vol. 11, pp. 17–20.
Glosten, L.R., Jagannathan, R. and Runkle, D.E. (1993). On the relation between the expected value and the volatility of the national excess return on stocks. Journal of Finance, Vol. 48, pp. 1779–1801.
Godfrey, M., Granger, C.W.J. and Morgenstern, O. (1964). The random walk hypothesis of stock market behaviour. Kyklos, Vol. 17, pp. 1–30.
Goudarzi, H. and Ramamarayanan, C.S. (2011). Modelling asymmetric volatility in the Indian Stock Market. International Journal of Management, Vol. 6, pp. 221–231.
Granger, C.W.J. and Morgenstern, O. (1963). Spectral analysis of New York Stock Market prices. Kyklos, Vol. 16, pp. 1–27.
Kendall, M.G. (1953). The analysis of economic time series, part I: prices. Journal of the Royal Statistical Society, Vol. 96, pp. 11–25.
Kurtz, L. and DiBartolomeo, D. (1996). Socially screened portfolios: an attribution analysis of relative performance. Journal of Investing, Vol. 5, pp. 35–41.
Kurtz, L. and DiBartolomeo, D. (1999). Managing Risk Exposures of Socially Screened Portfolios. Northfield Information Services, Boston. www.northinfo.com.
Lo, A.W. and MacKinlay, A.C. (1987). Stock market prices do not follow random walks: evidence from a simple specification test. Review of Financial Studies, Vol. 1, pp. 41–66.
Longin, M.F. (1997). The threshold effect in expected volatility: a model based on asymmetric information. The Review of Financial Studies, Vol. 10, pp. 837–869.
Mackinnon, J.G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, Vol. 11, pp. 601–618.
Managi, S., Okimoto, T. and Matsuda, A. (2012). Do socially responsible investment indexes outperform conventional indexes? Applied Financial Economics, Vol. 22, pp. 1511–1527.
Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, Vol. 36, pp. 394–419.
Mun, W.H., Long, S.B., Sundaram, L., Long, S.B. and Yin, S.O. (2008). Leverage effect and market efficiency of Kuala Lumpur composite index. International Journal of Business and Management, Vol. 3940, pp. 138– 144.
Nelson, D.B. (1990). Stationarity and persistence in the GARCH(1,1) model. Econometric Theory, Vol. 59, pp. 318–334.
Nelson, D.B. (1991). Conditional heteroscedasticity in asset returns: a new approach. Econometrica, Vol. 59, pp. 347–370.
Panas, E. (1990). The behaviour of Athens stock prices. Applied Economics, Vol. 22, pp. 1715-1727.
Phillips, P.C.B. and Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, Vol. 75, pp. 335–346.
Poon, S.H. (2005). A Practical Guide to Forecasting Financial Market Volatility. John Wiley and Sons, West Sussex.
Poon, S.H. and Granger, C. (2003). Forecasting volatility in financial markets: a review. Journal of Economic Literature, Vol. 41, pp. 478–539.
Poterba, J.M. and Summers, L.H. (1988). Mean reversion in stock prices: evidence and implications. NBER Working Paper No. 2343. Cambridge, Massachusetts.
Schroeder, M. (2005). The performance of socially responsible investments: investment funds and indexes. Financial Markets and Portfolio Management, Vol. 18, pp. 122–142.
Schwert, G.W. (1989). Why does stock market volatility change overtime? The Journal of Finance, Vol. 44, pp. 1115-1153.
Sharma, J.L. and Kennedy, R.E. (1977). A comparative analysis of stock price behaviour on the Bombay, London and New York Stock Exchanges. Journal of Quantitative Analysis, Vol. 12, pp. 391–413.
Statman, M. (2000). Socially responsible mutual funds. Financial Analysts Journal, Vol. 56, pp. 73–82.
Tse, Y.K. and Tung, S.H. (1992). Forecasting volatility in the Singapore stock market. Asia Pacific Journal of Management, Vol. 9, pp. 1–13.
Zakoian, J.M. (1994). Threshold heteroscedasticity models. Journal of Economic Dynamics and Control, Vol. 18, pp. 931–944
