NDLI: Estimation & forecasting of volatility using ARIMA, ARFIMA and Neural Network based techniques

Content Provider	IEEE Xplore Digital Library
Author	Kumar, P.H. Patil, S.B.
Copyright Year	2015
Description	Author affiliation: Dept. of CSE, VTURRC, Belgaum, India (Kumar, P.H.) \|\| Dept. of CSE, BTLIT, Bangalore, India (Patil, S.B.)
Abstract	Volatility is used to indicate the stock market movement; in general terms can be defined as the risk associated with stocks. Volatility is measured as standard deviation and variance of Closing Prices. Forecasting volatility has been a prime issue in financial market and lots of researchers are working on it since more than a decade. The main goal of this paper is to forecast volatility with a high accuracy. The volatility is calculated using traditional volatility calculation techniques called volatility estimators. The volatility is calculated using Close, Garman klass, Parkinson, Roger and Yang estimating methods. Time series forecasting techniques ARIMA, ARFIMA and a feed forward Neural Network based techniques are used for forecasting volatility. The results of all the three techniques are compared to find an accurate estimation and forecasting technique. The best forecasting technique is shortlisted by comparing the error results of all the forecasting techniques with error measuring parameters such as ME, RMSE, MAE, MPE, MAPE, MASE and ACF1. Garman klass estimator with Arima technique as the forecasting methods yields more accurate volatility forecasts for next 10 days.
Starting Page	992
Ending Page	997
File Size	160884
Page Count	6
File Format	PDF
e-ISBN	9781479980475
DOI	10.1109/IADCC.2015.7154853
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-12
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Neural networks Time series analysis Measurement uncertainty Volatility Arima Estimation Volatility Estimators Forecasting Autoregressive processes Arfima and Neural Networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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