Long Range Dependence in the Indian Stock Market: Evidence of Fractional Integration, Non-Linearities and Breaks

被引:0
作者
Gil-Alana L.A. [1 ]
Tripathy T. [2 ]
机构
[1] University of Navarra, Pamplona
[2] XLRI-Xavier School of Management, Jamshedpur
关键词
Efficiency; India; Long memory; Stock market;
D O I
10.1007/s40953-016-0029-4
中图分类号
学科分类号
摘要
This paper deals with the analysis of the Indian stock market prices using long range dependence techniques. In particular, we employ a variety of fractionally integrated models, which are very general in the sense that it allows us to incorporate structural breaks and non-linear structures. Our results indicate that the series corresponding to the NSE index is nonstationary and highly persistent, with an order of integration close to or above 1. The volatility, measured in terms of the squared returns indicates that the series is long memory, with an order of integration in the interval (0, 0.5). The results finally support the existence of a mean shift in the data at about January 2008, with the order of integration being around 1. Thus the Efficient Market Hypothesis (EMH) may be satisfied in the Indian stock market once a break is taken into account. However, the existence of short run dynamics suggests a degree of predictability in its behaviour. © 2016, The Indian Econometric Society.
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页码:199 / 215
页数:16
相关论文
共 48 条
[1]  
Arteche J., Gaussian semiparametric estimation in long memory in stochastic volatility and signal plus noise models, Journal of Econometrics, 119, pp. 131-154, (2004)
[2]  
Baillie R.T., Han Y.W., Myers R.J., Song J., Long memory models for daily and high frequency commodity future returns, Journal of Future Markets, 27, pp. 643-668, (2007)
[3]  
Barkoulas J.T., Baum C.F., Long term dependence in stock returns, Economics Letters, 53, pp. 253-259, (1996)
[4]  
Barkoulas J.T., Baum C.F., Travlos N., Long memory in the Greek stock market, Applied Financial Economics, 10, pp. 177-184, (2000)
[5]  
Beran J., Maximum likelihood estimation of the differencing parameter for invertible short and long memory autoregressive integrated moving average models, Journal of the Royal Statistical Society, 57, pp. 659-672, (1995)
[6]  
Bloomfield P., An exponential model in the spectrum of a scalar time series, Biometrika, 60, pp. 217-226, (1973)
[7]  
Bollerslev T., Mikkelsen H.O., Modeling and pricing long memory in stock market volatility, Journal of Econometrics, 73, pp. 151-184, (1996)
[8]  
Breidt F., Crato N., de Lima P., Modeling persistent volatility of asset returns, Computational Inteligence for Financial Engineering, 23, pp. 266-272, (1997)
[9]  
Breidt F., Crato N., de Lima P., The detection and estimation of long memory in stochastic volatility, Journal of Econometrics, 83, pp. 325-348, (1998)
[10]  
Campbellperron J.Y.P., Pitfalls and opportunities: What macroeconomists should know about unit roots, NBER Macroeconomic Annual, pp. 1141-1201, (1991)