VOLATILITY IN STOCK RETURN SERIES OF VIETNAM STOCK MARKET

: This paper studies the features of the stock return volatility using GARCH models and the presence of structural breaks in return variance of VNIndex in the Vietnam stock market by using the iterated cumulative sums of squares (ICSS) algorithm. Using a long-span data, GARCH and GARCH in mean (GARCH-M) models seems to be effective in describing daily stock returns’ features. About structural breaks, when applying ICSS to standardized residuals filtered from GARCH (1, 1) model, the number of volatility shifts significantly decreases in comparison with the raw return series. Events corresponding to those breaks and altering the volatility pattern of stock return are found to be country-specific. Not any shifts are found during global crisis period. Further evidence also reveals that when sudden shifts are taken into account in the GARCH models, volatility persistence remarkably reduces and that the conditional variance of stock return is much affected by past trend of observed shocks and variance. Our results have important implications regarding advising investors on decisions concerning pricing equity, portfolio investment and management, hedging and forecasting. Moreover, it is also helpful for policy-makers in making and promulgating the financial policies.


INTRODUCTION
Volatility is a fundamental concept in the discipline of finance. Considerable volatilities have been found in the past few years in mature and emerging financial markets worldwide. As factors, persistence term should be considered.
The persistence in volatility is a key ingredient for accurately predicting how events will affect volatility in stock returns and partially determines stock prices. Poterba and Summers (1986) showed that the extent to which stockreturn volatility affects stock prices (through a time-varying risk premium) depends critically on the permanence of stocks to variance.
Hence, the degree to which conditional variance is persistent or permanent in daily stock-return data is an important economic issue.
ARCH models proposed by Engle and Bollerslev (1982) and generalized by Bollerslev (1986) and Taylor (1986) have been proved to be sufficient in capturing properties of time-varying stock return volatility as well as volatility persistence. Literature has found many evidences in supporting the capability of GARCH models in volatility estimation (Akgiray (1989) and Pagan, Adrian R. et al. (1989)) rather than other non-GARCH models.
Since the introduction of simple GARCH models, a huge number of extensions and alternative specifications such as GARCH in mean (GARCH-M), Threshold GARCH (Glosten, Jagannathan et al. (1993)), has been proposed in attempt to better capture the characteristics of return series. Meanwhile, a procedure based on an iterated cumulative sums of squares (ICSS) by Inclan and Tiao (1994) is commonly used to detect number of (significant) sudden changes in variance in time series, as well as to estimate the time points and magnitude of each detected sudden changes in the variance.
While studies on stock markets in mature and emerging markets are widely available, so far not many researches have focused on Vietnam.
Although being set up much later than many countries in the world, Vietnam stock market has been growing rapidly. Therefore, main objective of this paper is to investigate and to

Events related to regime changes
Many papers concerned about whether global or local events were more important in making major shifts in variance of stock return and whether these events tended to be social,

Differences in periods before and after economic recession?
Of all events studied by some authors, impacts of crises on volatility changes of stock return has still remained a large concern of many investors and researchers. Fernandez  However, Inclan and Tiao (1994) claimed that using the D k function to find out the multiple break points simultaneously may be questionable due to the "masking effect".
Therefore, "an iterative scheme based on successive application of D k to pieces of the series, dividing consecutively after a possible change point is found", is suggested.
In addition, according to Inclan and Tiao (1994), this algorithm can be also included as part of the residual diagnostics for practitioners fitting time series models. Through simulation results, it is showed that when the ICSS algorithm is applied to residuals of autoregressive processes, similar results to those obtained when applying the ICSS algorithm to sequences of independent observation are found.

Combination of GARCH model and sudden changes
Lamoureux and Latrapes (1990)

Data
The data employed in this study comprise  Table 1 shows the descriptive statistics for daily stock market returns with daily returns computed as R t = ln(P t / P t-1 ), where P t is the daily price at time t and P t-1 is the daily price at time t-1.  DeGennaro (1990), Poon and Taylor (1992) and recently Emenike (2010).

Identification of break points and detection of related events
In the following part, we will detect and compare breakpoints in both raw and filtered return series to make clear the overstatement of ICSS when applying to raw data. In addition, events related to sudden changes in filtered data are also analyzed.

Breakpoints in raw returns
In this part, ICSS algorithm is applied to raw returns series. Total 23 breakpoints are found and presented in Table 3.   (2006). The breakpoints are depicted in Figure   3.

Combined GARCH model after including dummies
Three dummies with one constant variable (C) will be used to represent four phases divided by the breakpoints. The modified model is presented as below: