Modeling S&P Bombay Stock Exchange BANKEX Index Volatility Patterns Using GARCH Model

2019 ◽  
pp. 259-268
Keyword(s):  
Time Series ◽  
Banking Sector ◽  
Stock Exchange ◽  
Time Lag ◽  
Garch Model ◽  
Recent Past ◽  
Volatility Clustering ◽  
Asymmetric Volatility ◽  
Empirical Results ◽  
Asymmetric Garch

The main objective of this chapter is to estimate volatility patterns in the case of S&P Bombay Stock Exchange (BSE) BANKEX index in India. In recent past, the Indian banking sector was one of the fastest-growing industries and all major banks have been included in S&P BANKEX index as index benchmark constituent companies. The financial econometric framework is based on asymmetric GARCH (1, 1) model which is performed in order to capture asymmetric volatility clustering and leptokurtosis. Data time lag is considered from the first transaction day of January 2002 to last transaction day of June 2014. The empirical results revealed the existence of volatility shocks in the selected time series and also volatility clustering. The volatility impact has generated highly positive clockwise and impacted actual stocks. Moreover, the empirical findings reveal that the BANKEX index grown over 17 times in 12 years and volatility returns have been found present in listed stocks.

scholarly journals RISK-RETURN TRADEOFFS IN INDIAN STOCK MARKET: EVIDENCE FROM GARCH MODEL APPROACH

2021 ◽  
pp. 556-566
Author(s):  
Riteshbhai Patel
Keyword(s):  
High Risk ◽  
Stock Market ◽  
Garch Model ◽  
Sample Period ◽  
Asymmetric Volatility ◽  
Empirical Results ◽  
Daily Return ◽  
Risk Return ◽  
Indian Stock Market ◽  
Model Approach

The objective is to examine the risk-return tradeoff in the Indian stock market. The sample period of study is from January 4, 2000 to December 31, 2020. The empirical results shows existence of risk-return tradeoff in the BSE. A positive risk-return tradeoff is found for monthly & annual return series. The market has weak risk-return relationship in daily return series. The CGARCH (1,1) captures the asymmetric volatility effect for all the different frequency based returns. The study has implications for the investors. The riskreturn relationship is stronger and significant in longer duration of investment. The market gives higher return when there is a high risk.

Estimation of Persistence and Predictability of Volatility in The Indian Banking Sector

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Dr. Vandana Dangi
Keyword(s):  
Serial Correlation ◽  
Banking Sector ◽  
Stock Exchange ◽  
Conditional Heteroskedasticity ◽  
Arch Model ◽  
Stock Market Returns ◽  
Volatility Clustering ◽  
Arch Effect ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Augmented Dickey Fuller

The impulsiveness in investment’s price is volatility and its meticulous estimation and forecasting is valuable to investors in the risk management of their portfolio. Earlier volatility of an asset was assumed to be constant. However, the pioneering studies of Mandelbrot, Engle and Bollerslev on the property of stock market returns did not support this assumption. The family of autoregressive conditional heteroskedasticity models were developed to capture time-varying characteristics of volatility. The present treatise attempts to study the presence of autoregressive conditional heteroskedasticity in four Indian banking sector indices viz. BSE Bankex, BSE PSU, CNX bank and CNX PSU. The daily banking sector indices for the period of January 2004 to December 2013 were taken from the online database maintained by the Bombay Stock Exchange and the National Stock Exchange. The data of four indices was studied for stationarity, serial correlation in the returns and serial correlation in the squares of returns with the help of Augmented Dickey–Fuller test, Box-Jenkins methodology and autoregressive conditional heteroscedasticity models respectively. The results of ACF, PACF and Ljung–Box Q test indicates that there is a tendency of the periods of high and low volatility to cluster in the Indian banking sector. All the four banking sector indices display the presence of ARCH effect indicating the presence of volatility clustering. Engle's ARCH test (i.e Lagrange multiplier test) and Breush-Godfrey-Pagan test and ARCH model confirmed the high persistence and predictability of volatility in the Indian banking sector.

scholarly journals Aplikasi GARCH dalam Mengatasi Volatilitas Pada Data Keuangan

2017 ◽  
Vol 7 (2) ◽  
pp. 107
Author(s):  
, Hartati ◽  
Imelda Saluza
Keyword(s):  
Time Series ◽  
Garch Model ◽  
Practical Experience ◽  
Financial Data ◽  
Demand And Supply ◽  
Volatility Clustering ◽  
Wide Range ◽  
One Year ◽  
The Garch Model

The financial market is a place or means convergence between demand and supply of a wide range of financial instruments Long-term (over one year). Activities that occur in the financial markets in the long term will form a series of data is often called a time series that contains a set of information from time to time. Practical experience shows that many time series exhibit their periods with great volatility. The greater the volatility, the greater the chance to experience a gain or loss. Important properties are often owned by the data time series in finance, especially to return data that the probability distribution of returns are fat tails (tail fat) and volatility clustering or often referred to as a case heteroskedastisitas. Not all models are able to capture the nature of heteroscedasticity, one of the models that are able to do is Generalized Autoregressive Heteroskedasticity Condition (GARCH). So the purpose of this study was to determine the GARCH model in dealing with the volatility that occurred in the financial data. The results showed that the GARCH model is best suited to see volatility in the financial data.

scholarly journals SKEW AND IMPLIED LEVERAGE EFFECT: SMILE DYNAMICS REVISITED

2015 ◽  
Vol 18 (04) ◽  
pp. 1550022 ◽  
Author(s):  
VINCENT VARGAS ◽  
TUNG-LAM DAO ◽  
JEAN-PHILIPPE BOUCHAUD
Keyword(s):  
Linear Models ◽  
Garch Model ◽  
Leverage Effect ◽  
Option Markets ◽  
Empirical Results ◽  
Price Series ◽  
Non Linear ◽  
Asymmetric Garch ◽  
Linear Effects ◽  
Using Data

We revisit the "Smile Dynamics" problem, which consists in relating the implied leverage (i.e. the correlation of the at-the-money volatility with the returns of the underlying) and the skew of the option smile. The ratio between these two quantities, called "Skew-Stickiness Ratio" (SSR) by Bergomi (2009), saturates to the value 2 for linear models in the limit of small maturities, and converges to 1 for long maturities. We show that for more general, non-linear models (such as the asymmetric GARCH model), Bergomi's result must be modified, and can be larger than 2 for small maturities. The discrepancy comes from the fact that the volatility skew is, in general, different from the skewness of the underlying. We compare our theory with empirical results, using data both from option markets and from the underlying price series, for the S&P 500 and the DAX. We find, among other things, that although both the implied leverage and the skew appear to be too strong on option markets, their ratio is well explained by the theory. We observe that the SSR indeed becomes larger than 2 for small maturities, signalling the presence of non-linear effects.

scholarly journals Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models

2007 ◽  
Vol 12 (2) ◽  
pp. 115-149
Author(s):  
G.R. Pasha ◽  
Tahira Qasim ◽  
Muhammad Aslam
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Garch Models ◽  
Financial Time ◽  
Daily Data ◽  
Asymmetric Garch ◽  
Gjr Model ◽  
Forecasting Volatility ◽  
Tail Distributions

In this paper we compare the performance of different GARCH models such as GARCH, EGARCH, GJR and APARCH models, to characterize and forecast financial time series volatility in Pakistan. The comparison is carried out by comparing symmetric and asymmetric GARCH models with normal and fat-tailed distributions for the innovations, over short and long forecast horizons. The forecasts are evaluated according to a set of statistical loss functions. Daily data on the Karachi Stock Exchange (KSE) 100 index are analyzed. The empirical results demonstrate that the use of asymmetry in the GARCH models and the assumption of fat-tail distributions for the innovations improve the volatility forecasts. Overall, EGARCH fits the best while the GJR model, with both normal and non-normal innovations, seems to provide superior forecasting ability over short and long horizons.

The Assessment of Time Series for an Entire Air Quality Control District in Southern Taiwan Using GARCH Model

2018 ◽  
Vol 7 (3.19) ◽  
pp. 119 ◽  
Author(s):  
Shu Lung Kuo ◽  
Ching Lin Ho
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Air Quality ◽  
Air Pollutants ◽  
Garch Model ◽  
Multiple Time ◽  
Asymmetric Volatility ◽  
Air Quality Control ◽  
The Garch Model ◽  
T Distribution

The General Autoregressive Conditional Heteroskedastic (GARCH) model and 10 ordinary air quality monitoring stations in the entire air quality control district in Kaohsiung-Pingtung were used in this study. First, the factor analysis results within multivariate statistics were employed to select the main factor that affects air pollution, namely, the photochemical pollution factor. The characteristics of the GARCH model were discussed in terms of asymmetric volatility among the three air pollutants (PM10, NO2, and O3) within the factor. In addition, this study also combined the multiple time series model VARMA to explore changes in the time series of the three air pollutants and to discuss their predictability.The results showed that, although the coefficient of the GARCH model was negative when estimating the variance equation, the conditional variance would always be positive after taking the logarithm. The results also suggested that the GARCH model was quite capable of capturing the asymmetric volatility. In other words, if the condition that pollution factors might be subject to seasonal changes or outliers generated by the human contamination is not considered, the GARCH model had very good ability to verify the results and make predictions, regardless of whether it adopted any of the three risk concepts: normal distribution, t-distribution, and generalized error distribution. For example, under the trend of time series temporal and spatial distribution in various pollution concentrations of photochemical factors, the optimal model VARMA(2,0,0)-GARCH(1,1) selected in this study was used to conduct time series predictability after the verification procedure. After capturing the last 50 entries of data on O3 concentrations in the sequence, the results showed that the predictability correlation (r) was 0.812, the predictability of NO2 was 0.783 and the predictability of PM10 was 0.759. It can be learned from the results that under the sequence of the GARCH model with strong asymmetric volatility, the residual values of these three sequences as white noise were quite evident, and there was also a high degree of correlation in predictability.  

scholarly journals Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Hao Chen ◽  
Jianzhong Zhang ◽  
Yubo Tao ◽  
Fenglei Tan
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Asymmetric Effect ◽  
Wind Power Forecasting ◽  
Asymmetric Volatility ◽  
Forecasting Performance ◽  
Characteristics Analysis ◽  
Asymmetric Garch ◽  
Power Forecasting

AbstractWind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.

scholarly journals MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY IN MEAN UNTUK MERAMALKAN VOLATILITAS RETURN SAHAM

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Syarifah Zela Hafizah, Dadan Kusnandar, Shantika Martha
Keyword(s):  
Time Series ◽  
Garch Model ◽  
Conditional Heteroscedasticity ◽  
Volatility Clustering ◽  
Model Time Series ◽  
Autoregressive Conditional Heteroscedasticity

Volatilitas menunjukkan fluktuasi pergerakan harga saham. Semakin tinggi volatilitas maka semakin tinggi pula kemungkinan mengalami keuntungan dan kerugian. Data time series yang sering memiliki volatilitas yang tinggi adalah data keuangan. Data time series di bidang keuangan sering memiliki sifat volatility clustering atau sering disebut sebagai kasus heteroskedastisitas. Pada umumnya, pemodelan data time series harus memenuhi asumsi varian konstan (homoskedastisitas). Untuk mengatasi masalah heteroskedastisitas, model time series yang dapat digunakan adalah ARCH/GARCH. Model GARCH merupakan pengembangan dari model ARCH yang dapat digunakan untuk menggambarkan sifat dinamik volatilitas dari data. Salah satu bentuk pengembangan dari model GARCH adalah Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M). Tujuan dari penelitian ini adalah untuk mengimplementasikan model GARCH-M pada peramalan volatilitas return saham. Data yang digunakan dalam penelitian ini adalah return penutupan harga saham mingguan S&P 500 dari September 2013 sampai Juni 2019. Model terbaik yang digunakan untuk peramalan volatilitas pada return harga saham S&P 500 adalah MA (1) GARCH (1,1)-M.Kata Kunci: saham, volatilitas, GARCH-M

scholarly journals The market concentration and banking industry performance

2015 ◽  
Vol 13 (1) ◽  
pp. 1257-1264 ◽  
Author(s):  
Mohammed Salameh Anasweh
Keyword(s):  
Market Power ◽  
Banking Sector ◽  
Banking Industry ◽  
Stock Exchange ◽  
Power Structure ◽  
Industry Concentration ◽  
Empirical Results ◽  
Market Competitiveness ◽  
Traditional Structure ◽  
Industry Performance

This study examines the structure-profit relationship in the Qatari banking industry. The study sample consists of all the local banks operating in the market (13 banks) listed in Qatar Stock Exchange (QSE) over the 2009-2014 period. The hypotheses related to the market power structure which includes the traditional Structure-Conduct-Performance Hypothesis (SCP), and the traditional Efficiency Hypothesis (EH). The empirical results generally support the (SCP) Hypothesis in Qatari banking industry. Thus, the main implication of these results for the policymakers, of Qatari banking sector, is to expand the ongoing deregulation efforts with the aim of reducing the industry concentration and enhancing the market competitiveness.

Optimal Hedge Ratio and Hedging Effectiveness in Stock Futures Market: Evidence from National Stock Exchange, India

2019 ◽  
Vol 118 (3) ◽  
pp. 137-152
Author(s):  
A. Shanthi ◽  
R. Thamilselvan
Keyword(s):  
Banking Sector ◽  
Stock Exchange ◽  
Research Work ◽  
Futures Market ◽  
Least Square ◽  
Time Varying ◽  
Hedge Ratio ◽  
Hedging Effectiveness ◽  
Out Of Sample ◽  
Optimal Hedge

The major objective of the study is to examine the performance of optimal hedge ratio and hedging effectiveness in stock futures market in National Stock Exchange, India by estimating the following econometric models like Ordinary Least Square (OLS), Vector Error Correction Model (VECM) and time varying Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model by evaluating in sample observation and out of sample observations for the period spanning from 1st January 2011 till 31st March 2018 by accommodating sixteen stock futures retrieved through www.nseindia.com by considering banking sector of Indian economy. The findings of the study indicate both the in sample and out of sample hedging performances suggest the various strategies obtained through the time varying optimal hedge ratio, which minimizes the conditional variance performs better than the employed alterative models for most of the underlying stock futures contracts in select banking sectors in India. Moreover, the study also envisage about the model selection criteria is most important for appropriate hedge ratio through risk averse investors. Finally, the research work is also in line with the previous attempts Myers (1991), Baillie and Myers (1991) and Park and Switzer (1995a, 1995b) made in the US markets

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