Abstract
AbstractThis study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.
Publisher
Springer Science and Business Media LLC
Subject
Management of Technology and Innovation,Finance
Reference74 articles.
1. Aye GC, Gil-Alana LA, Gupta R, Wohar ME (2017) The efficiency of the art market: evidence from variance ratio tests, linear and nonlinear fractional integration approaches. Int Rev Econ Finance 51(C):283–294
2. Barnes ML, Ma S (2001) Market Efciency or Not? The Behaviour of China’s Stock Prices in Response to the Announcement of Bonus Issues 2001. https://ro.uow.edu.au/commpapers/475
3. Box GEP, Jenkins GM (1970) Time series analysis: forecasting and control. Holden-Day, San Francisco
4. Belaire-Franch J, Contreras D (2004) Ranks and Signs-Based Multiple Variance Ratio Tests. Spanish-Italian Meeting on Financial Mathematics, Cuenca, November 2003, Vol. 7, pp 40–79
5. Challa ML, Malepati V, Kolusu SNR (2018) Forecasting risk using autoregressive integrated moving average approach: evidence from S&P BSE Sensex. Financial Innovation 4:24. https://doi.org/10.1186/s40854-018-0107-z
Cited by
27 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献