Abstract
Volatility is the degree of variation in the stock price over time. The stock price is volatile due to many factors, such as demand, supply, economic policy, and company earnings. Investing in a volatile market is riskier for stock traders. Most of the existing work considered Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) models to capture volatility, but this model fails to capture when the volatility is very high. This paper aims to estimate the stock price volatility using the Markov regime-switching GARCH (MSGARCH) and SETAR model. The model selection was carried out using the Akaike-Informations-Criteria (AIC) and Bayesian-Information Criteria (BIC) metric. The performance of the model is evaluated using the Root mean square error (RMSE) and mean absolute percentage error (MAPE) metric. We have found that volatility estimation using the MSGARCH model performed better than the SETAR model. The experiments considered the Indian stock market data.
Highlights
Estimating stock price volatility is a difficult task due to nonlinear patterns in data.Early interpretation of stock price volatility helps the traders to make more profits
We have found that volatility estimation using the Markov regime-switching GARCH (MSGARCH) model performed better than the Self-Exciting Threshold Autoregressive (SETAR) model
We have considered two MSGARCH models to estimates the volatility in stock price
Summary
Estimating stock price volatility is a difficult task due to nonlinear patterns in data. Interpretation of stock price volatility helps the traders to make more profits. Volatility in finance is a statistic to measure the rate of change in the stock price over time, and it is calculated using standard deviations. The volatility statistics help the investor to estimates the risk in the stock or stock index. When the volatility is very high, it is riskier to invest. Identification of volatility in the market is essential as far stock market is concerned
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