Volatility is a statistical measure of profit distribution for a given market or stock index. In most cases, the higher the volatility, the greater the risk. Volatility can be measured by standard deviations or variances between returns of the same stock or market index. In the stock market, when stock prices rise or fall by more than 1% over an extended period of time, it is called an "unstable" market. In the US stock market, people use the VIX index to capture market volatility (VIX was created by the Chicago Board Stock Exchange as a measure to assess the expected 30-day volatility of the US stock market derived from the real-time list price of the S & S index. P 500). This is really a measure of investors and traders to gamble on the future which will either follow the direction of the market or individual stocks. The higher the VIX index implies a risky market because of the high volatility.

In the financial market, modeling and forecasting volatility are essential for investors. For example, investors need to analyze the risks of holding an asset or a portfolio, besides, the expected confidence interval can change over time, so more accurate intervals can be obtained by modeling the variance of errors. On the other hand, more efficient estimation tools can be obtained if the heterogeneity in errors is handled correctly. Autoregressive Conditional Heteroskedasticity (ARCH) models are specifically designed to model and predict conditional variance. The variance of the dependent variable is modeled as a function by the past values of the dependent variable and the independent or exogenous variables. ARCH models were introduced by Engle (1982) and were generalized as GARCH or (generalized ARCH) by Bollerslev (1986) and Taylor (2008). These models are widely used in various econometric studies, especially in financial time series analysis. Since developing GARCH models, some extensions and variants have been proposed. These variants can be divided into two groups: symmetric and asymmetric GARCH models.

This study uses GARCH (1,1), GARCH-M (1,1), EGARCH (1,1) and TGARCH (1,1) models to measure stock price fluctuations on the Ho Chi Minh stock exchange (HSX). The research results are useful reference information to help investors in forecasting the expected profit rate of the HSX, and also the risks along with market fluctuations in order to take appropriate adjust to the portfolios.

The paper includes five parts: Part 1 introduces research issues; Part 2 presents a theoretical overview and a research model; Part 3 presents research data and methods; Part 4 presents the results of empirical research; the final section summarizes the findings and implications.