Author:
Dixit Jitendra Kumar,Agrawal Vivek
Abstract
Purpose
Volatility is a permanent behavior of the stock market around the globe. The presence of the volatility in the stock price makes it possible to earn abnormal profits by risk seeking investors and creates hesitancy among risk averse investors as high volatility means high return with high risk. Investors always consider market volatility before making any investment decisions. Random fluctuations are termed as volatility of stock market. Volatility in financial markets is reflected because of uncertainty in the price and return, unexpected events and non-constant variance that can be measured through the generalized autoregressive conditional heteroscedasticity family models and that will give an insight for investment decision-making.
Design/methodology/approach
Daily data of the closing value of Bombay Stock Exchange (BSE) (Sensex) and National Stock Exchange (NSE) (Nifty) from April 1, 2011 to March 31, 2017 is collected through the web-portal of BSE (www.bseindia.com) and NSE (www.nseindia.com) for the analysis purpose.
Findings
The outcome of the study suggested that P-GARCH model is most suitable to predict and forecast the stock market volatility for both the markets.
Research limitations/implications
Future research can be extended to other stock market segments and sectoral indices to explore and forecast the volatility to establish a trade-off between risk and return.
Originality/value
The results of previous studies available are not conducive to this research, and very limited scholarly work is available in the Indian context, so required to be re-explored to identify the appropriate model to predict market volatility.
Subject
Business and International Management,Management of Technology and Innovation
Reference55 articles.
1. Capital structure and financial risk: evidence from foreign debt use in East Asia;The Journal of Finance,2001
2. Computation and analysis of multiple structural change models;Journal of Applied Econometrics,2003
3. Generalised autoregressive conditional heteroscedasticity;Journal of Econometrics,1986
4. Glossary to ARCH (GARCH),2008
5. ARCH modelling in finance;Journal of Econometrics,1992
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献