Volatility Modelling of the BRICS Stock Markets (original) (raw)
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Global Journal of Management and Business Research
Financial markets require an accurate estimate of asset volatility for various purposes such as risk management, decision-making and portfolio selection. Moreover, for risk management, volatility estimation is critical in Value-at-Risk (VaR) calculation models. However, there is still no consensus on a model that performs best in estimating volatility. This study proposes comparing volatility measures based on high-frequency data, such as RV and RRV, with heteroskedastic volatility models that use squared daily returns and daily closing prices. Four GARCH type models were implemented to estimate heteroskedastic volatility for the two most actively traded shares on the Brazilian stock exchange, using skewed generalized t (SGT) distribution and allowing flexibility for modeling the empirical distribution of these asymmetric financial data. Performed tests indicated no differential between the GARCH models and the high-frequency volatility measures used to estimate the VaR, indicating ...
This article aims to highlight a controversial issue of great interest ie the intrinsic structure of emerging capital market behavior. Synthesizing, empirical analysis aims to analyze emerging capital markets volatility. Emerging capital markets establish a separate category in the financial field, with highly dynamic characteristics, especially in times of financial crisis. Emerging capital markets are extremely attractive considering the growth prospects and investment opportunities. However, volatility of returns is significant and represents an undeniable obstacle in attracting investors. Modeling and forecasting volatility of emerging capital markets is still an underexploited area although it has quite interesting research resources. Stock prices volatility can be used as a measure of risk in financial markets, so its importance is even greater in emerging capital markets. A sharp introspection regarding cointegration of emerging stock markets raised significant issues as a direct consequence of international portofolio diversification and financial globalization.
Volatility Behaviour in Emerging Stock Markets – A GARCH Approach
International Journal of Business Analytics and Intelligence, 2016
This study primarily focuses on three aspects - (i) volatility in the emerging stock markets across globe by application of GARCH family models, (ii) study of ARMA structures, and (iii) a comparison of symmetric and asymmetric volatility. In the last decade or so, investors from developed countries are mostly focusing on the emerging economics as their investment opportunities. They associate a good amount of risk premium with these countries as far as the risk and return are concerned with their investments. Investments drawn from developed nations seems to make stock markets of emerging nations more volatile as these investment are exposed to both irrational and rational factors. Hence it’s imperative to understand the volatility behaviour of emerging stock markets over a period of time and also to study the comparative analysis of the volatility behaviours’ across these markets. This draws us to revisit the topic on volatility behaviour considering the emerging markets for this study. In this paper an attempt is being made to estimate the volatility behaviour of stock markets of 10 emerging economics and hence concentrated on India, China, Indonesia, Sri Lanka, Pakistan, Russia, Brazil, South Korea, Mexico, and Hong Kong.
On Historical Volatility in Emerging Markets Using Advanced GARCH Models
Social Science Research Network, 2012
This paper models the volatility present in the historical returns in the stock of the two major national indices of India. Sensitive Index or Sensex related to Bombay Stock Exchange (BSE) and Nifty associated with National Stock Exchange (NSE). The objective is to model the phenomena of volatility clustering and persistence of shock using asymmetric GARCH family of models. Research s h o w e d that EGARCH model successfully models the Sensex (BSE) data whereas it is GJR-GARCH which was able to explain conditional variance in the returns from Nifty (NSE).
Open Journal of Statistics, 2017
The aim of this paper is to use the General Autoregressive Conditional Heteroscedastic (GARCH) type models for the estimation of volatility of the daily returns of the Kenyan stock market: that is Nairobi Securities Exchange (NSE). The conditional variance is estimated using the data from March 2013 to February 2016. We use both symmetric and asymmetric models to capture the most common features of the stock markets like leverage effect and volatility clustering. The results show that the volatility process is highly persistent, thus, giving evidence of the existence of risk premium for the NSE index return series. This in turn supports the positive correlation hypothesis: that is between volatility and expected stock returns. Another fact revealed by the results is that the asymmetric GARCH models provide better fit for NSE than the symmetric models. This proves the presence of leverage effect in the NSE return series.
This study focuses on volatility estimation using asymmetric GARCH family models in financial series of S&P BSE LargeCap index collected from BSE Limited (formerly known as Bombay Stock Exchange) of India. The objective of this paper is to investigate volatility in market, asymmetry in volatility, measure short and long term volatility persistence and impact of news on market. This study considers data from 01:2005 to 05:2020 counting 3818 daily observations and follows GARCH (1, 1), EGARCH (1, 1) and GJR (1, 1). The empirical results indicate the following:1) presence of changing asymmetry in series returns of S&P BSE LargeCap index and evidence of leverage effect, 2) presence of abnormal volatility shocks which indicates high investment risk, 3) estimated impact of news and effect on market and 4) an opportunity for investment and return prospects. Main results and findings include all data statistics outcomes with graphical explanations. Furthermore, detailed result statistics available in full for GARCH and GARCH family models.
Volatility Comparison of the GSE All Share Index Returns using Student t and Normal-GARCH models
Journal of Management and Research, 2016
In a frontier equity market like Ghana, trades can be quiet for long periods increasing the liquidity risk for investors. This notwithstanding, studies into the risk dynamics of the stock market are largely lacking for the Ghana Stock Exchange (GSE). In this paper, we have undertaken to use a dynamic volatility model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) to assess the risk of equity returns in the market. A comparison is made between the student-t and normal GARCH(1,1) models to ascertain a better fit for the market data. Using a daily log of adjusted return series of the Ghana Stock Exchange All Share Index (GSEASI) from January 04, 2011 to December 31, 2013, there is enough evidence that the student-t GARCH(1,1) better describes the volatility dynamics of the market for the period 2011 to 2013.
Modeling Stock Market Volatility Using Univariate GARCH Models: Evidence from Bangladesh
This paper investigates the nature of volatility characteristics of stock returns in the Bangladesh stock markets employing daily all share price index return data of Dhaka Stock Exchange (DSE) and Chittagong Stock Exchange (CSE) respectively. Furthermore, the study explores the adequate volatility model for the stock markets in Bangladesh. Results of the estimated MA(1)-GARCH(1,1) model for DSE and GARCH(1,1) model for CSE reveal that the stock markets of Bangladesh capture volatility clustering, while volatility is moderately persistent in DSE and highly persistent in CSE. Estimated MA(1)-EGARCH(1,1) model shows that effect of bad news on stock market volatility is greater than effect induced by good news in DSE, while EGARCH(1,1) model displays that volatility spill over mechanism is not asymmetric in CSE. Therefore, it is concluded that return series of DSE show evidence of three common events, namely volatility clustering, leptokurtosis and the leverage effect, while return series of CSE contains leptokurtosis, volatility clustering and long memory. Finally, this study explores that MA(1)-GARCH(1,1) is the best model for modeling volatility of Dhaka stock market returns, while GARCH models are inadequate for volatility modeling of CSE returns.
Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH
2021 Asian Conference on Innovation in Technology (ASIANCON)
Volatility clustering is an important characteristic that has a significant effect on the behavior of stock markets. However, designing robust models for accurate prediction of future volatilities of stock prices is a very challenging research problem. We present several volatility models based on generalized autoregressive conditional heteroscedasticity (GARCH) framework for modeling the volatility of ten stocks listed in the national stock exchange (NSE) of India. The stocks are selected from the auto sector and the banking sector of the Indian economy, and they have a significant impact on the sectoral index of their respective sectors in the NSE. The historical stock price records from Jan 1, 2010, to Apr 30, 2021, are scraped from the Yahoo Finance website using the DataReader API of the Pandas module in the Python programming language. The GARCH modules are built and fine-tuned on the training data and then tested on the out-of-sample data to evaluate the performance of the models. The analysis of the results shows that asymmetric GARCH models yield more accurate forecasts on the future volatility of stocks.
Review of Economics and Finance, 2021
This paper models and estimates the volatility of stock indices and foreign exchange rates in BRICS, using univariate GARCH models. The data cover the period 13/05/1999-22/11/2018. The conditional variance is modeled with a GARCH (1,1), IGARCH, EGARCH and GJR-GARCH. The results suggest that the GJR-GARCH model outperforms the other GARCH family models and provides a clear direction on how to critically estimate volatility. Our findings also indicate the existence of persistency in the indices returns and in the forex rates returns. Moreover, both financial assets have a leverage effect, while the impact of financial shocks is asymmetric.