Modelling and Analysis of Volatility in Time Series Data

  • Siddarth Somarajan
  • Monica Shankar
  • Tanmay Sharma
  • R. JeyanthiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)


The comprehension of volatility is a crucial concept in analysing data. It is of greater importance for financial data since it furnishes key aspects such as return on investments and helps with effective hedging. The unpredictable nature of volatility causes heteroskedasticity which leads to difficulty in modelling. Consequently, time series models are desirable to predict volatility. An illustration of the same has been shown through an example of fitting time series models on the volatility of a listing from the National Stock Exchange (NSE). This paper also attempts to treat heteroskedasticity using Box-Cox transformations to achieve equal error variances prior to the modelling.


Volatility Time series models Heteroskedasticity Box-Cox transformations 


  1. 1.
    Enders, W.: Applied Econometric Time Series, 2nd edn. Wiley, Hoboken, New Jersey (2010)Google Scholar
  2. 2.
    Kim, J., Park, Y.J., Ryu, D.: Testing CEV stochastic volatility models using implied volatility index data. Phys. A Stat. Mech. Appl. 499, 224–232 (2018)CrossRefGoogle Scholar
  3. 3.
    Bala, L., Premaratne, G.: Stock market volatility: examining North America, Europe and Asia. SSRN Electron. J. Department of Economics, National University of Singapore (2004)Google Scholar
  4. 4.
    Sarkar, N.: Arch model with Box–Cox transformed dependent variable. Stat. Probab. Lett. Indian Statistical Institute, Economic Research Unit (2000)Google Scholar
  5. 5.
    AL-Najjar, D.: Modelling and estimation of volatility using ARCH/GARCH models in Jordan’s stock market. Asian J. Financ Account. 8(1) (2016). ISSN 1946-052XCrossRefGoogle Scholar
  6. 6.
    Nelson Jr., H.L., Granger, C.W.J.: Experience with using the Box-Cox transformation when forecasting economic time series. J. Econ. (1979)Google Scholar
  7. 7.
    Nikita, B., Balasubramanian, P., Yermal, L.: Impact of key macroeconomic variables of India and USA on movement of the Indian Stock return in case of S&P CNX Nifty. In: International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 330–333, 18 Oct 2017. Article number 8073536Google Scholar
  8. 8.
    Gamit, P., Leua, A., Tandel V.: Modeling of sugar prices volatility in India using autoregressive conditional heteroskedasticity models. Indian J. Econ. Dev. 14(1a) (2018)CrossRefGoogle Scholar
  9. 9.
    Hiremath, N., Naveen Kumar, S., Surya Narayanan, N.S., Jeyanthi, R.: A study of dealing serially correlated data in GED techniques. In: IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES, 31 Oct 2017. Article number 8091338Google Scholar
  10. 10.
    Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of UK inflation. Econometrica 50, 987–1008 (1982)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bollerslev, T.: Generalised autoregressive conditional heteroskedasticity. J. Econom. 31, 307–327 (1986)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Li, X., Zhang, W.: Research on the Efficiency of Chinese Stock Index Future Market Based on the Test of GARCH Model. Management & Engineering, School of Business, University of Jinan (2017)Google Scholar
  13. 13.
    Jiratumpradub, N., Chavanasporn, N.: Forecasting option price by GARCH model. In: 8th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia (2016)Google Scholar
  14. 14.
    Maddala, G.S.: Introduction to Econometrics, 3rd edn. Wiley (2003)Google Scholar
  15. 15.
    Bharathi, A., Natarajan, A.M.: Cancer classification of bioinformatics data using ANOVA. Int. J. Comput. Theory Eng. 2(3) (2010)Google Scholar
  16. 16.
    Friedman, J.P.: Dictionary of Business and Economics Terms. 5th edn. Barron’s Educational Series (2012)Google Scholar
  17. 17.
  18. 18.
    Thornton, T.D.: Least-squares regression cautions about correlation and regression. Lecture notes.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Siddarth Somarajan
    • 1
  • Monica Shankar
    • 1
  • Tanmay Sharma
    • 1
  • R. Jeyanthi
    • 1
    Email author
  1. 1.Department of Electronics and Communication Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluruIndia

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