On the flexibility of GARCH-family models with an application to the BRICS stock indices

Afuecheta, Emmanuel; Ruiz, Diego Andrés Pérez; Utazi, C. Edson; Nwosu, C. R.

doi:10.1080/23737484.2017.1298482

Cited by 2 publications

(2 citation statements)

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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

“…Specifically speaking, using a long-horizon prediction model, we evaluate the predictability of monthly stock market realized volatility of South Africa, and the BRIC countries, with newspaper-based macroeconomic attention indexes (MAIs) of the US on eight fundamentals (unemployment, monetary policy, output growth, inflation, housing market, credit ratings, oil, and the US dollar) with comparisons drawn with a newspaper-based measure of US economic sentiment. 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].…”

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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On the flexibility of GARCH-family models with an application to the BRICS stock indices

Cited by 2 publications

References 75 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

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

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