Publication Type:

Conference Paper

Source:

Proceedings - 8th International Conference on Computer Information Systems & Industrial Management Applications, IEEE (2009)

URL:

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5393807&tag=1

Keywords:

ARFIMA-FIGARCH, autoregressive moving average processes, Autoregressive processes, Computer science, Consumer electronics, Data engineering, Econometrics, Economic forecasting, financial market volatility, GARCH models, hybrid modeling, Indian Stock data, Long memory, Predictive models, pricing, real world financial time series, S&P CNX NIFTY, Signal analysis, Stochastic processes, stock markets, stock return prediction, Time series, Time series analysis

Abstract:

Modeling of real world financial time series such as stock returns are very difficult, because of their inherent characteristics. ARIMA and GARCH models are frequently used in such cases. It is proven of late that, the traditional models may not produce the best results. Lot of recent literature says the successes of hybrid models. The modeling and forecasting ability of ARFIMA-FIGARCH model is investigated in this study. It is believed that data such as stock returns exhibit a pattern of long memory and both short term and long term influences are observed. Empirical investigation has been made on closing stock prices of S&P CNX NIFTY. The obtained statistical result shows the modeling power of ARFIMA-FIGARCH. The performance of this model is compared with traditional Box and Jenkins ARIMA models. It is proven that, by combining several components or models, one can account for long range dependence found in financial market volatility. The results obtained illustrate the need for hybrid modeling.

Cite this Research Publication

Dr. Bhagavathi Sivakumar P. and P., M. V., “Modeling and predicting stock returns using the ARFIMA-FIGARCH”, in Proceedings - 8th International Conference on Computer Information Systems & Industrial Management Applications, 2009.

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