Author: Srinivasan Palamalai Publisher: ISBN: Category : Languages : en Pages :
Book Description
Volatility forecasting is an important area of research in financial markets and immense effort has been expended in improving volatility models since better forecasts translate themselves into better pricing of options and better risk management. In this direction, the present paper attempts to model and forecast the volatility (conditional variance) of the S&P CNX Nifty index returns of Indian stock market, using daily data for the period from January 1, 1996 to January 29, 2010. The forecasting models that are considered in this study range from the simple GARCH(1, 1) model to relatively complex GARCH models, including the Exponential GARCH(1, 1) and Threshold GARCH(1, 1) models. Based on out-of-sample forecasts and a majority of evaluation measures, the results show that the asymmetric GARCH models do perform better in forecasting conditional variance of the Nifty returns rather than the symmetric GARCH model, confirming the presence of leverage effect. The findings are consistent with those of Banerjee and Sarkar (2006) that relatively asymmetric GARCH models are superior in forecasting the conditional variance of Indian stock market returns rather than the parsimonious symmetric GARCH models.
Author: Shabarisha N. Publisher: ISBN: Category : Languages : en Pages :
Book Description
Return is the major attribute of an investment asset which can be construed as a random variable, and the 'variability in return' can be interpreted as volatility. Forecasting volatility and modeling it are the most prolific areas for research. This paper empirically investigates the conditional variance (volatility) pattern in Indian stock market based on financial time series data that consists of daily closing prices of CNX Nifty 50 market index for 10 years from April 2006 to March 2016. For the purpose of estimating conditional variance (volatility) in the daily returns of the index, Autoregressive Conditional Heteroskedasticity (ARCH) models are employed. Both symmetric and asymmetric models are used to capture stylized facts about CNX Nifty 50 market index returns such as volatility clustering and leverage effect. The findings of the study show that the asymmetric models are a better fit than symmetric models, confirming the presence of volatility clustering and leverage effect.
Author: Greg N. Gregoriou Publisher: CRC Press ISBN: 1420099558 Category : Business & Economics Languages : en Pages : 654
Book Description
Up-to-Date Research Sheds New Light on This Area Taking into account the ongoing worldwide financial crisis, Stock Market Volatility provides insight to better understand volatility in various stock markets. This timely volume is one of the first to draw on a range of international authorities who offer their expertise on market volatility in devel
Author: Sasikanta Tripathy Publisher: ISBN: Category : Languages : en Pages :
Book Description
Modeling and forecasting the volatility of stock markets has been one of the major topics in financial econometrics in recent years. Based on the daily closing value of 23 years data, an average of 5,605 observations, for both Sensex and Shanghai Stock Exchange Composite Index, this paper makes an attempt to fit appropriate GARCH model to estimate the conditional market volatility for both Bombay Stock Exchange (BSE) and Shanghai Stock Exchange (SSE), respectively. The empirical results demonstrate that there are significant ARCH effects in both the stock markets, and it is appropriate to use the GARCH model to estimate the process.
Author: Moustafa Ahmed AbdElaal Publisher: ISBN: Category : Languages : en Pages : 18
Book Description
This study investigates the performance of five models for forecasting the Egyptian stock market return volatility. We used the period from 1 January, 1998 until 31 December, 2009 as an in-sample period. We used also the next 30 days after the in-sample period to be our out-of-sample period. The competing models are: EWMA, ARCH, GARCH, GJR, and EGARCH. We examined also the ARCH effect to test the validity of using GARCH family to predict the volatility of market indices. The empirical results show that EGARCH is the best model between the examined models according to the usual evaluating statistical metrics (RMSN, MAE, and MAPE). When we used Diebold and Mariano (DM) test to examine the significance of the difference between errors of volatility forecasting models, we found no significance difference between the errors of competing models. The results also reject the null hypothesis of homoscedastic normal process for both EGX30 and CIBC100 indices.
Author: Srinivasan Kaliyaperumal Publisher: GRIN Verlag ISBN: 3668659958 Category : Business & Economics Languages : en Pages : 144
Book Description
Project Report from the year 2010 in the subject Business economics - Investment and Finance, , course: Ph. D, language: English, abstract: Every modern economy is based on a sound financial system and acts as a monetary channel for productive purpose with effecting economic growth. It encourages saving habit by throwing open and plethora of instrument avenues suiting to the individuals requirements, mobilizing savings from households and other segments and allocating savings into productive usage such as trade, commerce, manufacture etc. Thus a financial system can also be understood as institutional arrangements, through which financial surpluses are mobilized from the units generating surplus income and transferring them to the others in need of them. In nutshell, financial market, financial assets, financial services and financial institutions constitute the financial system. The activities include exchange and holding of financial assets or instruments of different kinds of financial institutions, banks and other intermediaries of the market. Financial markets provide channels for allocation of savings to investment and provide variety of assets to savers in various forms in which the investors can park their funds. At the same time, financial market is one that integral part of the financial system which makes significant contribution to the countries’ economic development. It establishes a link between the demand and supply of long-term capital funds. The economic strength of a country depends squarely on the state of financial market, apart from the productive potential of the country. The efficient allocation of fund by the capital market depends on the state of capital market. All the countries therefore focus more on the functioning of the capital market. Indian financial market has faced many challenges in the process of effecting more efficient allocation and mobilization of capital. It has attained a remarkable degree of growth in the last decade and in continuing to achieve the same in current decade also. Opening up of the economy and adoption of the liberalized economic policies have driven our economy more towards the free market. Over the last few years, financial markets, more specifically the security market were experiencing a lot of structural and regulatory changes. The major constituents of financial market are money market and the capital market catering to the type of capital requirements.
Author: Bart Frijns Publisher: ISBN: Category : Languages : en Pages : 23
Book Description
The current paper proposes a conditional volatility model with time varying coefficients based on a multinomial switching mechanism. By giving more weight to either the persistence or shock term in a GARCH model, conditional on their relative ability to forecast a benchmark volatility measure, the switching reinforces the persistent nature of the GARCH model. Estimation of this volatility targeting or VT-GARCH model for Dow 30 stocks indicates that the switching model is able to outperform a number of relevant GARCH setups, both in- and out-of-sample, also without any informational advantages.
Author: Gurmeet Singh Publisher: ISBN: Category : Languages : en Pages : 24
Book Description
In this paper, an attempt has been made to model the volatility of NIFTY index of National Stock Exchange (NSE) and forecast the NIFTY stock returns for short term by using daily data ranging from January, 2000, to December, 2014, which comprises 3736 data points for the analysis by using Box-Jenkins or ARIMA model. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. It is shown that ARCH family models outperform the conventional OLS models. ADF test and unit root testing is done to know the stationarity of the series, later the AR(p) and MA(q) orders are identified with the help of minimum information criterion as suggested by Hannan-Rissanen. As per the analysis, ARIMA (1,0,1) model was found to be the best fit to forecast the volatility of NIFTY stock returns. The model can be used by the investors to forecast the short run NIFTY stock returns and for making more profitable and less risky investments decision.
Author: Gurmeet Singh Publisher: ISBN: Category : Languages : en Pages : 22
Book Description
This paper investigates the volatility dynamics of stock market in India by using daily data of the NIFTY index of NSE from Jan 2000 to Dec 2014. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. Various volatility estimators and diagnostic tests indicate volatility clustering, i.e., shocks to the volatility process persist and the response to news arrival is asymmetrical, meaning that the impact of good and bad news is not the same. It is shown that ARCH family models outperform the conventional OLS models. We find that, the TARCH model is better fit, when we compare the GARCH, EGARCH and TARCH models, on the basis of AIC and SC criteria. Moreover, in the GARCH model, ARCH and GARCH effects remain significant, which highlights the inefficiency in the market. In addition, EGARCH and TARCH models indicate the presence of leverage effect and positive impact of volatility on returns.
Author: Sumathi D. Publisher: ISBN: Category : Languages : en Pages : 9
Book Description
Economic status of India is greatly imitated by the introduction of new economic policy in 1991. The Indian Capital Market has perceived a marvelous progression. There was an outburst of investor interest during the nineties and an equity cult emerged in the country. Foreign Exchange Regulations Act is one such legislation in this direction. An important recent development has been the entry of Foreign Institutional Investors as participants in the primary and secondary markets for industrial securities. In the past several years, investments in developing countries have increased remarkably. Among the developing countries, India has received considerable capital inflows in recent years. We apply the GARCH (1, 1) (General Autoregressive Conditional Heteroscedasticity) framework to on selected representative stock indices. The findings reveal that the GARCH (1, 1) model successfully captures nonlinearity and existence of volatility. The analysis suggests indicates a long persistence of volatility in Indian stock market especially National Stock Exchange (NSE) of India. The preliminary analysis of data set suggests that volatility in the Indian stock market is time varying in nature, persist to form clusters and has a long memory process. These findings of the data characteristics have been consistent with previous studies of Indian markets and justify the application of GARCH type models. The detailed analysis shows that the TGARCH (1,1) model outperforms in estimating, predicting and forecasting the stock market volatility.
Author: Srinivasan Palamalai
Author: Shabarisha N.
Author: Greg N. Gregoriou
Author: Sasikanta Tripathy
Author: Moustafa Ahmed AbdElaal
Author: Srinivasan Kaliyaperumal
Author: Bart Frijns
Author: Gurmeet Singh
Author: Gurmeet Singh
Author: Sumathi D.