Stock market volatility in indian

Stock market volatility in indian

Author: Krups Date: 28.05.2017

A SumoBrain Solutions Company. Search Expert Search Quick Search. SEARCH RESEARCH MPEP 2. Volatility of Indian stock market: The purpose of this paper is to apply the GARCH-class models to two major stock exchanges of Indian stock markets. The study includes main indices of Bombay Stock Exchange SENSEX and that of National stock exchange NIFTY. GARCH-class models have been applied to analyze the characteristics of the volatility of Indian stock market. That means the investors in those markets are not grown well and they will be heavily influenced by information good or bad very easily.

Garch, E-Garch, Tarch, Stock Market Volatility. Oct-Dec, Source Volume: Introduction The study of volatility is always a serious concern for analysts and researchers because high degree of volatility can affect the smooth functioning of any stock market.

stock market volatility in indian

It may also affect the economic growth and development of the economy through its effect on investor's confidence and risk taking ability. The researchers worldwide have attempted to identify the major factors affecting the level of volatility in the stock markets. The available theoretical and empirical literature suggests that the main source of volatility in any stock market is the arrival of new information or news.

Many of them attempted to establish pragmatic relation between stock return volatility and trading volume, the number of dealings, the bid ask spread, or market liquidity, in general. As an effect, a whole new area of Finance, "market microstructure," has been developed by these theories and the models.

The challenge of this field, though, is that the prerequisite of the method is still improvised. Stock return volatility is conventional and asymmetric in its retort to past negative price shocks compared to past positive price shocks, but what and even how many basic factors drive volatility over time is not clear. Stock market volatility also has many of adverse implications. Emerging economy like India is confronting the challenges of high volatility in abundant fronts together with volatility of its stock markets.

The review of existing literature suggests that stock market volatility may have an impact on economic growth and development Levine and Zervos, and Arestis et al and business environment Zuliu, An increase in stock market volatility can be interpreted as an increase in risk level of equity investment and therefore a transfer of funds from equity to less risky assets classes i. This shift can result to an increase in cost of capital to firms and hence new firms might abide this effect as investors will turn to purchase of stock in superior, well known firms.

Whereas there is a general agreement on what constitutes stock market volatility and, to a smaller extent, on how to quantify it, there is far less conventionality on the reasons of changes in stock market volatility.

A number of researchers investigated the causes of volatility in the arrival of new, unexpected information that affect expected returns on a stock Engle and Mcfadden, Thus, changes in market volatility would just reproduce changes in the domestic or global economic environment.

Others maintain that volatility is caused largely by changes in trading volume, practices or tends, which in turn are resolute by factors such as changes in macroeconomic policies, shifts in investor's risk appetite and growing uncertainty.

Conditional Heteroscedasticity ARCH became a very popular method in the modeling of stock market volatility.

Indian Stock Market – Stock Market News, Latest Share Market News from India

As comparison to traditional time series models, ARCH models allowed the conditional variances to change during time as functions of precedent errors. First approach was to improve the univariate ARCH model with a different requirement of the variance function. One development was introduced by Bollerslev where the Generalized Autoregressive Conditional Heteroscedasticity GARCH method was presented.

Then after, the Integrated GARCH IGARCH Engle and Bollerslev and the exponential GARCH EGARCH Nelson were significant one wherever re-specification of variance equation was considered. Nevertheless, the extent of empirical research on stock return volatility in emerging markets like India was not plentiful. While Roy and Karmakar focused on the measurement of the average level of sample standard deviation to investigate whether volatility has gone up, Goyal used conditional volatility estimates, as recommended by Schwert , to spot the trend in volatility.

He also analyzed the impact of carry forward system on the intensity of volatility. This study is an attempt to develop models to elucidate the volatility of the stock of the major indices of India. To this end, the study includes two main indices of Indian stock markets.

The data consists of indices of Bombay Stock Exchange SENSEX and that of National stock exchange NIFTY. This study uses the Autoregressive Conditional Heteroskedasticity ARCH models and its extension, the Generalized ARCH, EGARCH and TARCH models was used to find out the presence of the stock market volatility on Indian stock market.

The objective is to model the phenomena of volatility clustering and persistence of shock using asymmetric GARCH family of models.

Volatility in indian stock market and foreign institutional investor

This period of study is selected because Indian stock market has witnessed a tremendous growth and development in the sampled period. There are two major stock exchanges in India: Bombay Stock Exchange BSE and National Stock Exchange NSE.

The sample population of the study consists of the daily returns of the two most prominent domestic indices, viz. The data was collected from official websites of respective stock exchanges. Daily closing prices of the two indices were considered for the period of study. These market indices were fairly representative of the various industry sectors.

The daily stock prices were converted to daily returns. Logarithmic difference of prices of two successive periods was used to determine the rate of return. The study has calculated the natural log of the daily return [Y. The econometric software package Eviews 5. Arch and Garch Models Conventional econometric models assume a constant one-period forecast variance.

To simplify this implausible assumption, Robert Engle presented a set of methods called autoregressive conditional heteroscedasticity ARCH.

These are zero mean, serially uncorrelated methods with non constant variance conditional on the past. A practical generalization of this model is the GARCH parameterization introduced by Bollerslev This model is also a weighted average of past squared residuals, but it has waning weights that by no means go entirely to zero.

The set of equation 1 -- 3 represent the original GARCH model. Because GARCH p,q is an annex of ARCH model, it has all the properties of the original ARCH model. And because in GARCH model the conditional variance is not only the linear function of the square of the lagged residuals, it is also a linear function of the lagged conditional variances, GARCH model is more precise than the original ARCH model and it is easier to compute. The most commonly used GARCH model is GARCH 1,1 model.

The 1,1 in parentheses is a standard notation in which the first number refers to how many autoregressive lags, or ARCH terms, come into view in the equation, as the second number refers to how many moving average lags are specified, which here is frequently called the number of GARCH terms. Occasionally models with more than one lag are needed to find better variance forecasts. GARCH 1,1 is the most extensively used GARCH model because it is correctness and ease. Although GARCH model is very helpful in the predicting of volatility and asset pricing, there are still many problems GARCH model cannot clarify.

The main difficulty is that standard GARCH models presume that positive and negative error conditions have a symmetric effect on the volatility. In other terms, good and bad news have the similar impact on the volatility in this model. In real life this hypothesis is often desecrated, in particular by stock returns, in that the volatility increases more often after a flow of bad news than after good news. According to the challenges in the standard GARCH model, a number of parameterized extensions of the standard GARCH model have been recommended in recent times.

E-Garch Model Exponential GARCH EGARCH model was first developed by Nelson in The main purpose of EGARCH model is to explain the asymmetrical response of the market under the positive and negative shocks. EGARCH model in the study has been represented by equation 4 -- 5. When [phi] [not equal to] 0, the impact of information are asymmetry and when [phi] [not equal to] 0, there is an important leverage effect.

If one compared the above equations with the premises of the conventional GARCH model, one can see that there are no constraints for the parameters. This is one of the biggest benefits of EGARCH model as compared to the standard GARCH model. In GARCH-M GARCH-in-mean t h is added in the right hand side of equation 1 and hence is given by set of equation 6 -- 7.

It has the conditional variance given by equation 8. When the stock prices increase, [[psi]. If 0 [psi] we say that there is leverage effect. The return measures were both in positive and negative area. More fluctuations be tending to cluster together and were alienated by periods of relative calm. This was in agreement with Fama's observation of "volatility clustering".

Volatility in the Stock Market in India and Foreign Institutional Investors: A Study of the Post-Election Crash on JSTOR

From the time series graph of the returns for both markets, it is analyzed that high volatilities are followed by high volatilities and low volatilities are followed by low volatilities. That means both time series have important time varying variances. Additionally, it is appropriate to put conditional variance into the function to clarify the impact of risk on the returns.

Hence, GARCH class model is the excellent tool for the study. In addition, relatively large excess kurtosis recommended that the underlying data was leptokurtic heavily tailed and sharp peaked.

The Jarque--Bera statistic is calculated to test the null hypothesis of normality rejected the normality assumption. Both the indices appeared to have significant strong autocorrelations in one-day lag returns. In addition, the autocorrelation in the squared daily returns suggested incidence of clustering.

The results ruled out the independence assumption for the time series of given data set. Stationary of the return series were tested by conducting both Dickey-Fuller and Phillip-Peron tests. The results of both the tests confirmed that the series is stationary at first difference Table 2.

Before ARCH-GARCH is used in the study to approximation the model, the study is required to test whether the data has ARCH effect. The most commonly used method is Lagrange Multiplier test LM. That means the residuals have high order ARCH effect. The present work used GARCH, GARCH-M, TARCH, TARCH-M, EGARCH and EGARCH-M to estimate the data. Following is the table with the results estimated from different models.

From this table, one can select the best model for the further forecasting of stock market volatility. From Table 3, one can see that for both markets EGARCH 1,1 -M has the lowest RSS and the relative high adjusted 2 R. That means, EGARCH 1,1 -M is superior to other models in the estimation. From the standard of AIC and SC, we can see that EGARCH 1,1 has the lowest value. That means EGARCH 1,1 is also a relative good model for the estimation. In addition, when the study use GARCH 1,1 to estimate the data, it is found that the 1 a and 1 e for both markets are 0.

They are very close to 1. This demonstrates that there is high durability of the volatilities in both markets. That means if there is an expected shock in these markets, the sharp movements will not die out in the short run. That is a sign for high risk. At the same time, the study found that the summation of the parameters is less than 1, which indicates that the GARCH process for the stock return is wide-sense stationary.

When the study used TARCH 1,1 to estimate the model, it is found that the estimate of [psi]s are greater than 0 for both stock exchanges. When the study used EGARCH 1,1 , it is found the estimates of [psi]s are less than 0 for both markets. Then one can conclude that there are leverage effects in both markets.

stock market volatility in indian

That is to say the volatilities caused by negative shocks are greater than that caused by positive shocks. This is in consistent with most of the existing literature.

Volatility of Indian stock market: an emperical evidence.

The study also used the estimated EGARCH 1,1 to predict the volatilities for BSE and NSE. In figure 3 and 4, one can see that the model did a great job. Also one can see that these two exchanges are highly correlated and there is a considerable synchronization in their movements.

This is not unexpected because these two stock exchanges are the principal stock exchanges of India and they are regulated by the government. Also the maximum volume more on NSE as comparison to BSE is traded on these stock exchanges only. The research found that both EGARCH 1,1 and EGARCH 1,1 -M did good jobs in fitting the process for both exchanges. Because 1 a and 1 e are 0.

The study also demonstrated that there are leverage effects in the markets. This can easily be seen in current turmoil in Indian stock market. The study also found that the volatility of the Indian stock market exhibited features similar to those found earlier in many of the global stock markets, viz. It was found that asymmetrical GARCH models do better than the ordinary least square OLS models and the Vanilla GARCH models.

Perseverance of shock could be explained the time dependent risk premium. If it is found that the shock was of short term in nature, then the investor would be reluctant from making any modification in their discounting factor while calculating the present discounted value of the stock and therefore its price.

References Bollerslev T, R F Engle and D B Nelson , "ARCH Models in R. Levine, R and S. Zervos , "Stock Market Development and Long-Run Growth", World Bank Economic Review, Vol. A new Approach", Economertica, Vol. Pattanaik S and B Chatterjee. An Empirical Puzzle", Reserve Bank of India Occasional Papers, Vol.

Roy M K and M Karmakar. Descriptive statistics of NIFTY and SENSEX returns SENSEX Return NIFTY Return Mean 0. Unit root test results Variables Augmented Dickey Fuller ADF Phillips At Levels Intercept Intercept No Intercept Model A Model B Model C Model A Sensex Return MacKinnon Critical values at level: Estimates using various models Market Model RSS A-[R. Development of plastic cards market:

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