Estimating Stock Market Volatility Using Exponential Garch Model with Skewed Student- T Distribution

Syamraj KP; Regina Sibi Cleetus

doi:10.59640/cbr.v14i2.98-104

Syamraj KP Research Scholar (Full Time), Post Graduate & Research Department of Commerce, Mar Ivanios (Autonomous) College, Affiliated to University of Kerala
Regina Sibi Cleetus Research Guide & Assistant Professor, Post Graduate & Research Department of Commerce, Mar Ivanios (Autonomous) College, Affiliated to University of Kerala

DOI: https://doi.org/10.59640/cbr.v14i2.98-104

Keywords: GARCH, EGARCH, Student t distribution, skewed student distribution

Abstract

The aim of the study is to empirically investigate the performance of the EGARCH (1, 1) volatility model with the normal, skew-normal, and student t and skewed student t distributions on the NSE Nifty Fifty Index. Ten years of daily closing rates over the period of January 2010 to December 2020, for a total of 2730 observations, have been analyzed. According to the information criterion, this study has found that the EGARCH (1, 1) model under skewed student t distribution is a better fit than other distribution models.

References

A, G. (2022, january 05). rugarch: Univariate GARCH models. Retrieved from cran.r-project.org: https://cran.r-project.org/web/packages/rugarch/index.html

Alberg, D. S. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201-1208.

Bhowmik, D. (2013). Stock market volatility: An evaluation. International Journal of Scientific and Research Publications, 3(10), 1-17.

Daly, K. (2008). Financial volatility: Issues and measuring techniques. Physica A: statistical mechanics and its applications, 387(11), 2377-2393.

Daly, K. (2011). An overview of the determinants of financial volatility: An explanation of measuring techniques. Modern Applied Science, 5(5), 46.

Duffie, D. (2010). Presidential address: Asset price dynamics with slow moving capital. The Journal of finance, 65(4), 1237-1267.

Engle, R. F. (1987). Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica: journal of the Econometric Society, 391-407.

Figlewski, S. &. (2000). Is the’Leverage Effect’a leverage effect? Available at SSRN 256109.

Gupta, S., & Mittal, P. (2015). Base Erosion and Profit Shifting: The New Framework of International Taxation. Journal of Business Management and Information Systems, 2(2), 108–114.

Hao Liu, Z. Z. (2009). The Volatility of the Index of Shanghai Stock Market Research Based on ARCH and Its Extended Forms. Discrete Dynamics in Nature and Society, 9.

Harvey, A. R. (1992). Unobserved component time series models with ARCH disturbances. Journal of Econometrics, 52(1-2), 129-157.

Li, R. &. (2020). A review of Student’st distribution and its generalizations. Empirical Economics, 58(3), 1461-1490.

Nelson,D.B.(1991).Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.

Mittal, P. (2017). Time Series Analysis Using ARCH Models: A Case Analysis of Australian Stock Index. VEETHIKA-An International Interdisciplinary Research Journal, 3(1), 74–80. https://doi.org/10.48001/veethika.2017.03.01.007

Officer, R. R. (1973). The variability of the market factor of the New York Stock Exchange. the Journal of Business, 46(3), 434-453.

Treleaven, P. G. (2013). Algorithmic trading review. Communications of the ACM, 56(11), 76-85.

West, S. G. (2012). Model fit and model selection in structural equation modeling. Handbook of structural equation modeling, 1, 209-231.

Xie, F. C. (2021). Bayesian estimation for stochastic volatility model with jumps, leverage effect and generalized hyperbolic skew Student’s t-distribution. Communications in Statistics-Simulation and Computation, 1-18.