scholarly journals MEASURING VOLATILITY OF NIFTY50 AND SENSEX UNDER DIFFERENT ERROR DISTRIBUTION METHODS OF E-GARCH FOR THE PERIOD BETWEEN 2011 TO 2016.

2021 ◽  
Vol 58 (2) ◽  
pp. 6586-6592
Author(s):  
Dr. Vijayakumari Joseph, Ms. A.Amali Vinupriyadharshini
Keyword(s):  
Stock Market ◽  
Garch Model ◽  
Error Distribution ◽  
Distribution Method ◽  
Arch Models ◽  
Asymmetric Effects ◽  
The Right

Volatility has always been a part and parcel of stock market. Understanding the volatility is very difficult though measuring it is not impossible. Choosing the right method to meausre the volatility is very crucial and important to get the reliable and accurate results. This study aims at measuring volatility of Nifty50 and Sensex under different error distribution methods of E-GARCH model. E-GARCH is one of the reliable ARCH models that measures persistent volatility and asymmetric effects. This paper bring out the best suited model for Nifty50 and Sensex in measuring the volatility under different error distribution method of E-GARCH model.

scholarly journals Modelling Stock Market Volatility: Evidence from Vietnam

2017 ◽  
Vol 33 (3) ◽  
Author(s):  
Ngo Van Toan ◽  
Ho Thuy Tien ◽  
Ho Thu Hoai
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Information Criterion ◽  
Conditional Variance ◽  
Series Data ◽  
Garch Models ◽  
Stock Market Volatility ◽  
Asymmetric Volatility ◽  
Asymmetric Effects ◽  
The Right

This study empirically investigates the volatility pattern of Vietnam stock market based on time series data which consists of daily closing prices of VN-Index during the period 2005-2016. The analysis has been done using both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. Based on Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) criteria, the study proves that GARCH (1,1) and EGARCH (1,1) are the most appropriate model to measure the symmetric and asymmetric volatility of VN-Index respectively. The study also provides evidence of the existence of asymmetric effects (leverage) via the parameters of the EGARCH (1,1) model that show that negative shocks have significant effects on conditional variance (fluctuation). Meanwhile, in the TGARCH (1,1) model, the findingss are not as expected. This study also provides investors with a tool to forecast the rate of return of the stock market. At the same time, the findings will help investors determine the profitability and volatility of the market so that they can make the right decisions on holding the securities.

scholarly journals Investigation of Feller-Buncher Performance Using Weibull Distribution

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 284
Author(s):  
Ebru Bilici
Keyword(s):  
Weibull Distribution ◽  
Shape Parameter ◽  
B Value ◽  
Productivity Analysis ◽  
Volume Data ◽  
Estimation Model ◽  
Distribution Method ◽  
Timber Volume ◽  
C Value ◽  
The Right

With the advancement of technology in forestry, the utilization of advanced machines in forest operations has been increasing in the last decades. Due to their high operating costs, it is crucial to select the right machinery, which is mostly done by using productivity analysis. In this study, a productivity estimation model was developed in order to determine the timber volume cut per unit time for a feller-buncher. The Weibull distribution method was used to develop the productivity model. In the study, the model of the theoretical (estimated) volume distributions obtained with the Weibull probability density function was generated. It was found that the c value was 1.96 and the b value was 0.58 (i.e., b is the scale parameter, and c is the shape parameter). The model indicated that the frequency of the volume data had moved away from 0 as the shape parameter of the Weibull distribution increased. Thus, it was revealed that the shape parameter gives preliminary information about the distribution of the volume frequency. The consistency of the measured timber volume with the estimated timber volume strongly indicated that this approach can be effectively used by decision makers as a key tool to predict the productivity of a feller-buncher used in harvesting operations.

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

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