Stochastic volatility in mean model for capturing the conditional variance in volatile time series data

RAVINDRA SINGH SHEKHAWAT; K N SINGH; AJAY KUMAR; KRISHNA PADA SARKAR; RIPI DONI; BISHAL GURUNG

doi:10.56093/ijas.v88i10.84258

Stochastic volatility in mean model for capturing the conditional variance in volatile time series data

Authors

RAVINDRA SINGH SHEKHAWAT Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012
K N SINGH Principal Scientist and Head, Division of Forecasting and Agriculture Systems modelling, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012
AJAY KUMAR Research Associate, NAHEP KAB-II, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012
KRISHNA PADA SARKAR Research Scholar, ICAR-Indian Agricultural Research Institute, New Delhi 110 012
RIPI DONI Research Scholar, ICAR-Indian Agricultural Research Institute, New Delhi 110 012
BISHAL GURUNG Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012

DOI:

https://doi.org/10.56093/ijas.v88i10.84258

Keywords:

Particle filter, Stochastic volatility in mean model, Time-series, Volatility

Downloads

Download data is not yet available.

References

Bauwens L, Hafner C M and Laurent S. 2012. Handbook of Volatility Models and their Applications. Wiley, USA. DOI: https://doi.org/10.1002/9781118272039

Bollerslev Tim. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31(3): 307–27. DOI: https://doi.org/10.1016/0304-4076(86)90063-1

Box G, Jenkins G M and Reinsel G. 2008. Time Series Analysis: Forecasting and Control, 4 edn, p 746. John Wiley & Sons, Hoboken, New Jersey. DOI: https://doi.org/10.1002/9781118619193

Deo M., Devanadhen, K. and Srinivasan, K. 2008. An empirical analysis of implied volatility in Indian options market. International Research Journal of Finance and Economics 18: 108–26.

Engle R F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4): 987–1007. DOI: https://doi.org/10.2307/1912773

Fan J and Yao Q. 2003. Nonlinear Time Series: Nonparametric and parametric Methods. Springer, New York.

Jordi C and Josep P. 2012 Maximum likelihood approach for several stochastic volatility models. Journal of Statistical Mechanics: Theory and Experiment 8: 8–16. DOI: https://doi.org/10.1088/1742-5468/2012/08/P08016

Koopman S J and Hol Uspensky E. 2002. The stochastic volatility in mean model: empirical evidence from international stock markets. Journal of Applied Econometrics 17: 667–89. DOI: https://doi.org/10.1002/jae.652

Taylor S J. 1994. Modelling stochastic volatility: A review and comprehensive study. Mathematical Finance 4: 183–204. DOI: https://doi.org/10.1111/j.1467-9965.1994.tb00057.x

Downloads

Published

2018-10-24

Issue

Vol. 88 No. 10 (2018)

Section

Short-Communication

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The copyright of the articles published in The Indian Journal of Agricultural Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.

How to Cite

SHEKHAWAT, R. S., SINGH, K. N., KUMAR, A., SARKAR, K. P., DONI, R., & GURUNG, B. (2018). Stochastic volatility in mean model for capturing the conditional variance in volatile time series data. The Indian Journal of Agricultural Sciences, 88(10), 1644-1647. https://doi.org/10.56093/ijas.v88i10.84258