S-Transform Based Analysis for Stock Market Volatility Estimation
R. Seethalakshmi1, R Rahul2, C. Vijayabanu3

1Dr.R.Seethalakshmi, APIII, School of Arts, Science & Humanities, SASTRA Deemed University, Thanjavur, India. 
2Rahul R., Student, School of Computing, SASTRA Deemed University, Thanjavur, India.
3Dr. Vijayabanu C, Associate Professor, School of Management, SASTRA Deemed University, Thanjavur, India.

Manuscript received on 23 August 2019. | Revised Manuscript received on 04 September 2019. | Manuscript published on 30 September 2019. | PP: 3669-3675 | Volume-8 Issue-11, September 2019. | Retrieval Number: K19670981119/2019©BEIESP | DOI: 10.35940/ijitee.K1967.0981119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Financial Time series analysis (FTSA) is concerned with theory and practice of asset valuation over time. Generally, FTSA is useful for forecasting the asset volatility. This paper proposes the discrete S-Transform technique driven by Gaussian kernel for the estimation of volatility in FTSA. S-Transform is found to be a better tool in finding the time frequency resolution so as to predict and estimate the risk and returns of financial market. S-Transform prediction on two different bench mark data sets namely, Standard & Poor(S&P) 500 and Dow Jones Industrial Average(DJIA) index clearly indicates its superiority for the prediction of short and long-term trends in stock markets.
Keywords: Volatility, S-Transform, Gaussia kernel.
Scope of the Article: Marketing and Social Sciences.