Volatility dynamics and the risk-return relationship in South Africa: A GARCH approach

Dwarika, Nitesha; Moores-Pitt, Peter; Chifurira, Retius

doi:10.21511/imfi.18(2).2021.09

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…In order to model and predict the volatility of the South African stock market, researchers have traditionally used various variants of the popular GARCH model. A comprehensive review of this literature is beyond the scope and objective of this paper, but the interested reader can refer to the works of [33,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], and the references cited therein. In terms of the international literature on modeling and predictability of stock market volatility, see [33,[53][54][55][56][57] for detailed reviews.…”

Section: Brief Discussion Of Stock Return Volatility Literature Of So...mentioning

confidence: 99%

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

Wu,

Karmakar,

Gupta

et al. 2024

Climate

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Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers.

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Section: Brief Discussion Of Stock Return Volatility Literature Of So...mentioning

confidence: 99%

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

Wu,

Karmakar,

Gupta

et al. 2024

Climate

View full text Add to dashboard Cite

show abstract

“…The leverage effect was evident. A research study by Dwarika et al [28] revealed that the South African market has a high level of volatility persistence.…”

Section: Review Of Literaturementioning

confidence: 99%

Risk-Return Trade-off and Volatility Characteristics in the Indian Stock Market

Tamilselvan¹,

Srinivasan²,

Kumar³

et al. 2022

TEM Journal

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The study examines the volatility characteristics of Indian stock markets and their tradeoff between the risk and return. It finds a positive but insignificant association between the risk and returns during the subsample (the pre-COVID and COVID pandemic outbreak) and whole sample periods. The study also shows that the weak form of Indian stock markets is not sustainable. Consistent with the GARCH literature, persistent and asymmetric effects are evidenced, and the magnitude of the negative shocks has a larger immediate impact than the positive shocks. These results would help measure the volatility in the Indian stock markets and provide investors and regulators with necessary information about the market efficiency, persistency (long-memory process) and asymmetric effects.

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“…Realized volatility, as captured by the (log) square root of the sum of squared daily log-returns (following [24]), is considered as an accurate, observable, and unconditional metric of volatility [25], unlike the measures of the same derived from the popular alternative types of GARCH models, that has been primarily used in the South African stock market context to capture volatility (see for example, [26][27][28][29][30][31][32][33][34][35][36], and references cited therein), as well as the stochastic volatility (SV) framework. At this stage, it must be noted, within the GARCH-class of models, volatility analysis in South Africa has been dominated by univariate frameworks, and when multivariate-settings were indeed adopted to capture information of predictors, focus was primarily on domestic variables [37][38][39]. More importantly, we use quantile random forests [40], a flexible datadriven machine learning technique, to derive inferences from our prediction models.…”

Section: Introductionmentioning

confidence: 99%

Drivers of Realized Volatility for Emerging Countries with a Focus on South Africa: Fundamentals versus Sentiment

2023

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We use a quantile machine learning (random forests) approach to analyse the predictive ability of newspapers-based macroeconomic attention indexes (MAIs) on eight major fundamentals of the United States on the realized volatility of a major commodity-exporting emerging stock market, namely South Africa. We compare the performance of the MAIs with the performance of a news sentiment index (NSI) of the US. We find that both fundamentals and sentiment improve predictive performance, but the relative impact of the former is stronger. We document how the impact of fundamentals and sentiment on predictive performance varies across the quantiles of the conditional distribution of realized volatility, and across different prediction horizons. Specifically, fundamentals matter more at the extreme quantiles at short horizons, and at the median in the long-run. In addition, we report several robustness checks (involving sample period and alternative definitions of realized volatility), and indicate that the obtained results for South Africa also tend to carry over to other emerging countries such as, Brazil, China, India, and Russia. Our results have important implications for investors with volatility being an input for portfolio allocation decisions. In addition, with stock market variability also capturing financial uncertainty, its accurate prediction based on US fundamentals and sentiment also has a role in policy design to prevent possible collapse.

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Volatility dynamics and the risk-return relationship in South Africa: A GARCH approach

Cited by 7 publications

References 17 publications

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

Risk-Return Trade-off and Volatility Characteristics in the Indian Stock Market

Drivers of Realized Volatility for Emerging Countries with a Focus on South Africa: Fundamentals versus Sentiment

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