Modelling the Impact of Global Financial Crisis on the Indian Stock Market through GARCH Models

Mathur, Sunil; Chotia, Varun; Rao, Narendar V.

doi:10.1177/2319510x16650056

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…Bangladesh, China, India, Malaysia, the Philippine, and South Korea from January 2002 to December 2016 divided into three sub-periods such as pre-crisis, crisis, and post crisis time intervals using GARCH model, pairwise Granger causality tests, and generalized VAR (vector auto regression) models. The empirical results indicate that selected Asian emerging stock markets interaction is lower before the global financial crisis period and volatility spillover reached the highest level in the most turbulent period of . Mathur et al (2016 investigated the effects generated by the global financial crisis on the Indian Stock Market (BSE) based on GARCH models for the period 2001-2012 and the results of the empirical study suggest high volatility for the period 2007-2009.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating Abnormal Volatility Transmission Patterns Between Emerging and Developed Stock Markets: A Case Study

Spulbăr

Trivedi²,

Birău

2020

Journal of Business Economics and Management

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The main aim of this paper is to investigate volatility spillover effects, the impact of past volatility on present market movements, the reaction to positive and negative news, among selected financial markets. The sample stock markets are geographically dispersed on different continents, respectively North America, Europe and Asia. We also investigate whether selected emerging stock markets capture the volatility patterns of developed stock markets located in the same region. The empirical analysis is focused on seven developed stock market indices, i.e. IBEX35 (Spain), DJIA (USA), FTSE100 (UK), TSX Composite (Canada), NIKKEI225 (Japan), DAX (Germany), CAC40 (France) and five emerging stock market indices, i.e. BET (Romania), WIG20 (Poland), BSE (India), SSE Composite (China) and BUX (Hungary) from January 2000 to June 2018. The econometric framework includes symmetric and asymmetric GARCH models i.e. EGARCH and GJR which are performed in order to capture asymmetric volatility clustering, interdependence, correlations, financial integration and leptokurtosis. Symmetric and asymmetric GARCH models revealed that all selected financial markets are highly volatile, including the presence of leverage effect. The stock markets in Hungary, USA, Germany, India and Canada exhibit high positive volatility after global financial crisis.

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Section: Literature Reviewmentioning

confidence: 99%

Investigating Abnormal Volatility Transmission Patterns Between Emerging and Developed Stock Markets: A Case Study

Spulbăr

Trivedi²,

Birău

2020

Journal of Business Economics and Management

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“…The motivation for undertaking this exercise is two folds. First, although much has been documented on the volatility of stock prices elsewhere in the world, relatively little is known in the context of Tanzania (see for example, (Achal, Girish, Ranjit, & Bishal, 2015;Ajaya & Swagatika, 2018;Akhtar & Khan, 2016;Mathur, Chotia, & Rao, 2016)). Existing studies that have attempted to examine volatility within the context of GARCH models in Tanzania have mainly focused on other macroeconomic variables such as inflation (Edward, Eliab, & Estomih, 2004) exchange rate (Carolyn, Betuel, & Pitos, 2018;Epaphra, 2016) tax revenues (Chimilila, 2017).…”

Section: Introductionmentioning

confidence: 99%

Volatility of Stock Prices in Tanzania: Application of Garch Models to Dar Es Salaam Stock Exchange

Kazungu

Mboya²

2021

Asian Journal of Economic Modelling

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We use Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to examine volatility of stock prices for firms listed in the Dar es Salaam Stock Exchange (DSE). In doing so, both symmetric and asymmetric GARCH models are used in this study. The descriptive analysis of the data shows that standard deviation of the series returns is high, indicating a high level of daily fluctuations, and the log value of the mean is close to zero. Our empirical results clearly exhibit evidence of volatility and volatility clustering, a typical feature of financial time series. Moreover, our results indicate that the series are highly leptokurtic, flat tailed and asymmetric consistent with characteristics of financial time series data. Out of all models examined, EGARCH (1,1) and GARCH (1,1) seem to perform plausibly better than others.Contribution/ Originality: This study contributes to the existing literature through the application of both GARCH and the EGARCH models in order to capture both symmetry and asymmetry effects, and determines key characteristics of stock returns at Dar es salaam Stock Exchange.

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“…To capture these high variations over time with regards to risk and volatility, [16] proposed the modification of standard (generalized autoregressive conditional heteroscedastic) GARCH-type model under the assumption that the variance coefficient in the mean equation measures the relative risk aversion. Also, according to [17], the increasing roles played by the risk and uncertainty in financial assets have led to the development of new time series techniques for measuring time variations. One of such techniques is the GARCH-in-Mean (GARCH-M) model.…”

Section: Introductionmentioning

confidence: 99%

“…The major advantage of the GARCH-M model over the standard GARCH-type models is that any misspecification of variance function would not affect the consistency of the estimators of parameters of the mean. Meanwhile, prior studies of [18,17,19,20,21,22,23] and [24] have applied GARCH-M technique to capture varying property of risk aversion and autocorrelation of return series as well as interaction between the mean and variance equations of GARCH-type models. Particularly, this study seeks to improve on the work of [25] that detected and modeled the asymmetric GARCH effects using GARCH-type models under the assumption that the return series is uncorrelated.…”

Section: Introductionmentioning

confidence: 99%

Modeling Heteroscedasticity in the Presence of Serial Correlations in Discrete-time Stochastic Series: A GARCH-in-Mean Approach

Moffat

Akpan

2019

AJPAS

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Background: In modeling heteroscedasticity of returns, it is often assumed that the series are uncorrelated. In practice, such series with small time periods between observations can be observed to contain significant serial correlations, hence the motivation for this research. Aim: The aim of this research is to assess the existence of serial correlations in the return series of Zenith Bank Plc, which is targeted at identifying their effects on the parameter estimates of heteroscedastic models. Materials and Methods: The data were obtained from the Nigerian Stock Exchange spanning from January 3, 2006, to November 24, 2016, having 2690 observations. The hybridized Autoregressive Integrated Moving Average-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH-type) models such as Autoregressive Integrated Moving Average-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH), Autoregressive Integrated Moving Average-Exponential Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-EGARCH) and the Autoregressive Integrated Moving Average-Glosten, Jagannathan and Runkle Generalized Autoregressive Conditional Heteroscedastic (ARIMA-GJRGARCH) under normal and student-t distributions were employed to model the conditional variance while the GARCH-in-Mean-GARCH-type model corresponding to the selected ARIMA-GARCH-type model was applied to appraise the possible existence of serial correlations. Results: The findings of this study showed that heteroscedasticity exists and appeared to be adequately captured by ARIMA(2,1,1)-EGARCH(1,1) model under student-t distribution but failed to account for the presence of serial correlations in the series. Meanwhile, its counterpart, GARCH-in-Mean-EGARCH(1,1) model under student-t distribution sufficiently appraised the existence of serial correlations. Conclusion: One remarkable implication is that the estimates of the parameters of ARIMA-GARCH-type model are likely to be biased when the presence of serial correlations is ignored. Also, the application of GARCH-in-Mean-GARCH-type model possibly provides the feedback mechanism or interaction between the variance and mean equations.

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Modelling the Impact of Global Financial Crisis on the Indian Stock Market through GARCH Models

Cited by 6 publications

References 27 publications

Investigating Abnormal Volatility Transmission Patterns Between Emerging and Developed Stock Markets: A Case Study

Investigating Abnormal Volatility Transmission Patterns Between Emerging and Developed Stock Markets: A Case Study

Volatility of Stock Prices in Tanzania: Application of Garch Models to Dar Es Salaam Stock Exchange

Modeling Heteroscedasticity in the Presence of Serial Correlations in Discrete-time Stochastic Series: A GARCH-in-Mean Approach

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