scholarly journals Investigating financial opportunities for traditional clothing industry in South Asia based on an analysis of internationally diversified portfolio using ARCH and GARCH models

2021 ◽  
Vol 72 (06) ◽  
pp. 645-650
Author(s):  
IMRAN ALI ZULFIQAR ◽  
CRISTI SPULBAR ◽  
ABDULLAH EJAZ ◽  
RAMONA BIRAU ◽  
LUCIAN CLAUDIU ANGHEL ◽  
...  
Keyword(s):  
Stock Market ◽  
South Asia ◽  
Market Integration ◽  
Large Change ◽  
Global Market ◽  
Conditional Variance ◽  
Clothing Industry ◽  
Garch Models ◽  
Small Change ◽  
Diversified Portfolio

This paper investigates the benefits of forming an internationally diversified portfolio in the stock markets of Bangladesh, India and Pakistan using the stock market indices data from April 2013 to March 2020. The portfolio comprises of three stock market indices from Pakistan, India and Bangladesh. The goal is to identify financial opportunities for traditional clothing industry in South Asia. Bangladesh, India and Pakistan are neighbouring countries in South Asia. Tradition, culture and specific ethnic elements influence traditional clothing in the case of the selected country cluster consisting of Bangladesh, India and Pakistan. Our empirical results indicate that internationally diversified portfolio does not reduce the risk due to global market integration in the background. Furthermore, ARCH and GARCH models reveal that large change in conditional variance is followed by large changes in conditional variance whereas small change in conditional variance is followed by small changes in conditional variance.

Through the Lens of Recession 2.0: Diversification Dynamics Between the Leading Asian Stock Markets

2021 ◽  
pp. 097226292098395
Author(s):  
Manu K. S. ◽  
Surekha Nayak ◽  
Rameesha Kalra
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Market Integration ◽  
Developing Economies ◽  
Positive Impact ◽  
Global Market ◽  
Stock Market Integration ◽  
Developed Economies ◽  
Market Participants ◽  
Causality Tests

The focus of this article is to analyse the inter-linkages between eight leading stock markets in Asian continent from the period of July 2011 to February 2018. This period holds relevance as this was the time when Recession 2.0 set in, which adversely affected the developed economies; however, the developing economies withstood the crisis without much of an impact. Co-integration and Granger causality tests were conducted to probe the inter-linkages. Study revealed a positive impact on Asian stock market indices collectively on each of the indexes. The highest number of unidirectional causalities was to KOPSI and NIFTY from rest of the stock indices. Results confirmed that no co-integration relationship existed among the selected indices indicating favourable diversification opportunities. Thus, the study fosters global market participants and policymakers to consider the nitty-gritties of stock market integration so as to benefit from international stock market diversification in the Asian region.

scholarly journals Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

2019 ◽  
pp. 1-25
Author(s):  
David Adugh Kuhe ◽  
Moses Abanyam Chiawa ◽  
Sylvester Chigozie Nwaosu ◽  
Jonathan Atsua Ikughur
Keyword(s):  
Stock Market ◽  
Long Memory ◽  
Asset Returns ◽  
Conditional Variance ◽  
Garch Models ◽  
Level Shifts ◽  
Random Level Shifts ◽  
The Impact ◽  
Random Level ◽  
Volume Of Trade

This study investigated the impact of volatility shock persistence on the conditional variance in the Nigerian stock returns using symmetric and asymmetric higher order GARCH family models in the presence of random level shifts and non-Gaussian errors. The study utilised Bai and Perron methodology to detect structural breakpoints in the conditional variance of daily stock and volume of trade returns in the Nigerian stock market from 2nd January, 1998 to 22nd March, 2017. The study employed symmetric GARCH (3,2) and GARCH (2,1)-M models to estimate volatility of asset returns, symmetric GARCH (2,2) and GARCH (2,1)-M to model volatility of volume of trade returns and asymmetric EGARCH (2,2), TGARCH (3,2) and PGARCH (2,3) models to measure the volatility of asset returns as well as asymmetric EGARCH (2,1), TGARCH (1,3) and PGARCH (3,2) models to estimate volatility of volume of trade returns. These models were optimally selected using information criteria and log likelihood as the best fitting symmetric and asymmetric GARCH models to estimate the conditional volatility of asset and volume of trade returns in the Nigerian stock market with and without structural breaks. Results revealed that when random level shifts were ignored in volatility models, the shocks persistence were very high with long memory and variance explosion. But when the random level shifts were incorporated into the GARCH models, there was a significant reduction in the volatility shocks persistence and long memory. Moreover, volatility half-lives also declined drastically while accounting for these sudden level shifts in variance. The study found asymmetry without leverage effects as well as a positive risk-return tradeoff for both asset and volume of trade returns in the Nigerian stock market. The Nigeria banking reform of 2004, the Global Financial and Economic Crises, as well as other local events in Nigeria, were found to have negative and significant impacts on the Nigerian stock market. The study provided some policy recommendations.

scholarly journals Modeling Stock Market Monthly Returns Volatility Using GARCH Models Under Different Distributions

2020 ◽  
Vol 5 (1) ◽  
pp. 42-50
Author(s):  
Rama Krishna Yelamanchili
Keyword(s):  
Stock Market ◽  
Gaussian Distribution ◽  
Garch Model ◽  
Information Criterion ◽  
Conditional Variance ◽  
Garch Models ◽  
Leverage Effect ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Clustering

This papers aims to uncover stylized facts of monthly stock market returns and identify adequate GARCH model with appropriate distribution density that captures conditional variance in monthly stock market returns. We obtain monthly close values of Bombay Stock Exchange’s (BSE) Sensex over the period January 1991 to December 2019 (348 monthly observations). To model the conditional variance, volatility clustering, asymmetry, and leverage effect we apply four conventional GARCH models under three different distribution densities. We use two information criterions to choose best fit model. Results reveal positive Skewness, weaker excess kurtosis, no autocorrelations in relative returns and log returns. On the other side presence of autocorrelation in squared log returns indicates volatility clustering. All the four GARCH models have better information criterion values under Gaussian distribution compared to t-distribution and Generalized Error Distribution. Furthermore, results indicate that conventional GARCH model is adequate to measure the conditional volatility. GJR-GARCH model under Gaussian distribution exhibit leverage effect but statistically not significant at any standard significance levels. Other asymmetric models do not exhibit leverage effect. Among the 12 models modeled in present paper, GARCH model has superior information criterion values, log likelihood value, and lowest standard error values for all the coefficients in the 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.

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