This paper studied the effects of good and bad news on volatility in the Indian stock markets using asymmetric ARCH models during the global financial crisis of 2008-09. The BSE500 stock index was used as a proxy to the Indian stock market to study the asymmetric volatility over 10 year's period. Two commonly used asymmetric volatility models i.e. EGARCH and TGARCH models were used. The BSE500 returns series found to react to the good and bad news asymmetrically. The presence of the leverage effect would imply that the negative innovation (news) has a greater impact on volatility than a positive innovation (news). This stylized fact indicates that the sign of the innovation has a significant influence on the volatility of returns and the arrival of bad news in the market would result in the volatility to increase more than good news. Therefore, we conclude that, bad news in the Indian stock market increases volatility more than good news.
Abstract-The weak form of market efficiency assumes that prediction of asset returns based on historical information's is not possible. Nevertheless, a great number of studies show that asset returns exhibit significant autocorrelation between observations widely separated in time. This is one of the stylized facts of financial markets which is known as long memory. The presence of long memory can be defined in term of persistence of autocorrelation. This paper studies the presence of long memory property in the Indian stock market. Using data from BSE500 stock index, this study found evidence of long memory property in the Indian stock market as seen in developed stock markets and some other emerging markets. It is found that the FIEGARCH (1, d, 1) is the best fit model and it outperforms other ARCH-type models in modelling volatility in the Indian stock market.
Capital inflows play a substantial role in developing countries. It used to increase accumulation and rate of investments to create conditions for more intensive economic growth. Capital inflows are necessary for macroeconomic stability as capital inflows affect a wide range of macro economic variables such as exchange rates, interest rates, foreign exchange reserves, domestic monetary conditions as well as saving and investments. Capital inflows, however, are not without risk. The main risk posed by large and volatile capital inflows is that they may resulted in crisis and destabilize macroeconomic management. Given the role of FII flews and its associated risks, the main purpose of this paper was to investigate the cointegration and causality between the Indian stock market and foreign institutional investment (FII) In India during world financial turmoil of 2008. The cointegration and causal relationship using Engle-Granger (1987), Johansen (1991, 1995a) and Granger (1969) methodologies were investigated .The study found that BSE500 stock index and FII series are cointegrated and causality between them is bilateral
The international financial markets turmoil, which started around mid-2007, has depreciated substantially since August 2008. The financial market crisis has led to the collapse of major financial institutions. Nevertheless, crashes and/or crises are not devoted to only developed markets and developing countries including India, are not excluded from this rule and it may face such a condition. The sharp decline of Sensex price index from its closing peak of 20 873 on January 8, 2008, to less than 10 000 by October 17, 2008, in line with similar large declines in other major stock markets is good reminders of this fact. Volatility as a measure of risk plays an important role in many financial decisions in such a situations. The main purpose of this study is to examine the volatility of the Indian stock markets and its related stylized facts using ARCH models. The BSE500 stock index was used to study the volatility in the Indian stock market over a 10 years period. Two commonly used symmetric volatility models, ARCH and GARCH were estimated and the fitted model of the data, selected using the model selection criterion such as SBIC and AIC. The adequacy of selected model tested using ARCH-LM test and LB statistics. The study concludes that GARCH (1, 1) model explains volatility of the Indian stock markets and its stylized facts including volatility clustering, fat tails and mean reverting satisfactorily
Since 1979, Iran has faced with unilateral and multilateral harsh sanctions due to its nuclear energy program. These sanctions have resulted in significant problem to both sanctioned and sanctioning parties. Given the fact that sanctions have had significant impacts on Iran’s economy and since Iran stock market is the barometer of its economy, it is assumed that sanctions affect the Iranian stock market as well. To test this hypothesis, this study studied the Iranian stock market volatility during harsh sanctions using ARCH models. The study found that, despite all sanctions, not only Iran’s stock market shows major stylized facts of any stock market’s volatility i.e. volatility clustering, fat tails and mean reversion but also it shows no irregularity which could be attributed to effect of sanctions. This finding was consistent with Iranian stock market regulators claiming Iranian stock market growth and the U.S. Congressional Research Service report 2013. Therefore, based on findings, this study concluded that Iranian stock market has not affected by sanctions.
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