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.

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 Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

2005 ◽  
Vol 12 (1) ◽  
pp. 55-66 ◽  
Author(s):  
W. Wang ◽  
P. H. A. J. M Van Gelder ◽  
J. K. Vrijling ◽  
J. Ma
Keyword(s):  
Seasonal Variation ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Conditional Heteroskedasticity ◽  
Daily Streamflow ◽  
Arch Effect ◽  
Monthly Streamflow ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Residual Series

Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.

Estimating Long-Term Volatility on National Stock Exchange of India

2019 ◽  
pp. 229-237
Keyword(s):  
Stock Exchange ◽  
Stock Index ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Large Sample ◽  
Market Capitalization ◽  
Stock Indices ◽  
Autoregressive Conditional Heteroskedasticity

The main objective of this chapter is to provide an elaborate framework on the long-term volatility of the National Stock Exchange of India based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The CNX-100 index is one of the most diversified Indian stock indices which includes over 38 sectors of the economy. This stock index represents about 81.57% of the free-floating market capitalization of stocks listed on the National Stock Exchange (NSE) of India from March 2014. Moreover, this book chapter empirically tested volatility clusters of CNX100 index using a large sample database from October 2007 to July 2014.

scholarly journals Measuring Dynamics of Risk and Performance of Sector Indices on Zagreb Stock Exchange

2015 ◽  
Vol 1 (1-2) ◽  
pp. 27-41
Author(s):  
Tihana Škrinjarić
Keyword(s):  
Portfolio Selection ◽  
Capital Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Conditional Heteroskedasticity ◽  
Market Sector ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Illiquid Market ◽  
And Performance

Abstract Investors are interested in sector diversification on stock markets among other important portfolio topics. This paper looks at five sector indices on Croatian capital market as an example of a small, relatively illiquid market. Sector indices have been constructed at the beginning of 2013 and since then there is a lack of studies, which focus on sector diversification on Zagreb Stock Exchange (ZSE). Thus, the purpose of this paper is to evaluate the recent dynamics of risk and performance of five sector indices on ZSE by employing MGARCH (Multivariate Generalized Autoregressive Conditional Heteroskedasticity) models empirically. Output from the analysis is used to form guidance for investors on Croatian capital market. The results indicate that in the observed period from February 4th 2013 to October 13th 2015 portfolios based on MGARCH methodology outperform other portfolios in terms return and risk. Thus, it is advisable to use this methodology when making portfolio selection.

Modeling volatility on the Karachi Stock Exchange, Pakistan

2016 ◽  
Vol 10 (3) ◽  
pp. 253-275 ◽  
Author(s):  
Shahan Akhtar ◽  
Naimat U. Khan
Keyword(s):  
Financial Crises ◽  
Stock Exchange ◽  
Small Sample ◽  
Leverage Effect ◽  
Data Series ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Modeling Volatility ◽  
The Impact

Purpose The current paper aims to fill a gap in the literature by analyzing the nature of volatility on the Karachi Stock Exchange (KSE) 100 index of the KSE, and develop an understanding as to which model is most suitable for measuring volatility among those used. The study contributes significantly to the literature as, compared with the limited previous studies of Pakistan undertaken in the past, it covers three types of data (i.e. daily, weekly and monthly) for the whole period from the introduction of the KSE 100 index on November 2, 1991 to December 31, 2013. In addition, to analyze the impact of global financial crises upon volatility, the data have been divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods. Design/methodology/approach This study has used an advanced set of volatility models such as autoregressive conditional heteroskedasticity [ARCH (1)], generalized autoregressive conditional heteroskedasticity [GARCH (1, 1)], GARCH in mean [GARCH-M (1, 1)], exponential GARCH [E-GARCH (1, 1)], threshold GARCH [T-GARCH (1, 1)], power GARCH [P-GARCH (1, 1)] and also a simple exponentially weighted moving average (EWMA) model. Findings The results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity and volatility clustering. However, the heteroskedasticity is absent only in the monthly returns making only the EWMA model usable to measure the volatility level in the monthly series. The P-GARCH (1, 1) model proved to be a better model for modeling volatility in the case of daily returns, while the GARCH (1, 1) model proved to be the most appropriate for weekly data based on the Schwarz information criterion (SIC) and log likelihood (LL) functionality. The study shows high persistence of volatility, a mean reverting process and an absence of a risk premium in the KSE market with an insignificant leverage effect only in the case of weekly returns. However, a significant leverage effect is reported regarding the daily series of the KSE 100 index. In addition, to analyze the impact of global financial crises upon volatility, the findings show that the subperiods demonstrated a slightly low volatility and the global economic crisis did not cause a rise in volatility levels. Originality/value Previously, the literature about volatility modeling in Pakistan’s markets has been limited to a few models of relatively small sample size. The current thesis has attempted to overcome these limitations and used diverse models for three types of data series (daily, weekly and monthly). In addition, the Pakistani economy has been beset by turmoil throughout its history, experiencing a range of shocks from the mild to the extreme. This paper has measured the impact of those shocks upon the volatility levels of the KSE.

scholarly journals Impact Of Financial Crisis On Hedging Effectiveness Of Futures Contracts: Evidence From The National Stock Exchange Of India

2015 ◽  
Vol 10 (2) ◽  
pp. 69-88 ◽  
Author(s):  
Kapil Gupta ◽  
Mandeep Kaur
Keyword(s):  
Financial Crisis ◽  
Stock Exchange ◽  
Moving Average ◽  
Futures Contracts ◽  
Conditional Heteroskedasticity ◽  
Vector Autoregressive ◽  
Hedging Effectiveness ◽  
Autoregressive Conditional Heteroskedasticity ◽  
The 2008 Financial Crisis ◽  
The Impact

Abstract The present study examines the impact of the 2008 financial crisis on the hedging effectiveness of three index futures contracts traded on the National Stock Exchange of India for near, next and far month contracts over the sample period of January 2000 – June 2014. The hedge ratios were calculated using eight methods; Naive hedging, Ederington’s Model, Autoregressive Integrated Moving Average, Vector Autoregressive, Vector Error Correction Methodology, Generalized Autoregressive Conditional Heteroskedasticity, Exponential Generalized Autoregressive Conditional Heteroscedasticity and Threshold Generalized Autoregressive Conditional Heteroskedasticity. The study finds an improvement in hedging effectiveness during the post-crisis period, which implies that during the high-volatility period hedging effectiveness also improves. It was also found that near month futures contracts are a more effective tool for hedging as compared to next and far month contracts, which imply that liquidity is a more important determinant of hedging effectiveness than hedge horizons. The study also finds that a time-invariant hedge ratio is more efficient than time-variant hedging. Therefore, knowledge of sophisticated econometrical tools does not help to improve hedge effectiveness.

scholarly journals Spillover effects in the financial year cycle for Indian markets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Parul Bhatia
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Spillover Effects ◽  
Ordinary Least Squares ◽  
Leverage Effect ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Year Effect ◽  
Autoregressive Conditional Heteroskedasticity

PurposeThe stock market anomalies have been studied across the globe with intermingled results for individual markets. The present study has investigated the financial year effect for Indian stock markets by testing month-of-the-year-effect anomalies.Design/methodology/approachThe oldest stock exchange's index returns (Bombay Stock Exchange [BSE]) have been tested using ordinary least squares (OLS) and autoregressive conditional heteroskedasticity in mean (ARCH-M) models with Student's t and Student's t-fixed distributions for the period between 1991 and 2019. The Glosten, Jagannathan and Runkle-generalised autoregressive conditional heteroskedasticity (GJR-GARCH) model has been further used to find out existence of the leverage effect in returns.FindingsThe findings indicated no evidence for anomalies in the Indian stock market which may be used by investors for making unusual returns. However, the volatility in returns has shown weak but significant results due to the financial year impact. The leverage effect has not been found in the financial year cycle change over. The Indian market may be said to be moving towards a state of efficiency, leaving no scope for investors to gauge bizarre profits.Research limitations/implicationsThe study has incorporated the Indian context for testing anomalies during the start and end of the financial year cycle. The model may be extended further to developed and developing nations’ markets for testing efficiency in their stock markets during the same cycle.Originality/valueThe paper may be the first of its kind to test for the financial year effect on standalone basis for Indian markets. The paper also adds to the existing literature on testing events’ effect.

Estimation of Volatility in BSE-SENSEX by Fitting GARCH (1,1) Model

2020 ◽  
pp. 469-472
Keyword(s):  
High Risk ◽  
Empirical Analysis ◽  
Stock Exchange ◽  
Financial Data ◽  
Conditional Heteroskedasticity ◽  
Additional Insight ◽  
Daily Data ◽  
Augmented Dickey Fuller ◽  
Adf Test ◽  
The Empirical Analysis

This paper aimed to investigate the volatility of the Bombay Stock Exchange of India aka BSE Sensex based on the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. The study empirically tested the volatility of SENSEX using daily data for 10 years, between 2009 and 2019. The empirical analysis is based on closing prices of index-daily observations (4833) and provided additional insight regarding volatility patterns. Augmented Dickey-Fuller (ADF) test was used to test for stationarity and GARCH (1, 1) model was employed to estimate volatility. The results of the ADF test revealed that financial data was stationary. Results of the GARCH (1, 1) model stated that there existed persistent and robust volatility. This implied that the index experienced frequent small gains and few significant losses which would lead to high risk and in turn the chance of higher return. To investigate volatility shocks, GARCH methodology, which is an advanced econometric method preferred to depict actual effects was employed.

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