---
title: Volatility and Equity Returns in South Africa
url: https://www.ml-quant.com/papers/repec/ids-afasfa-v-14-y-2024-i-1-p-83-99/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:ids:afasfa:v:14:y:2024:i:1:p:83-99
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.inderscience.com%2Flink.php%3Fid%3D136131%3Bh%3Drepec%3Aids%3Aafasfa%3Av%3A14%3Ay%3A2024%3Ai%3A1%3Ap%3A83-99
featured: 2024-02-07
citations: unknown
topic: Derivatives & Volatility
---


# Volatility and Equity Returns in South Africa

The research explores the connection between the South African volatility index and Johannesburg Stock Exchange listed stock indices, concluding that the TGARCH model is best for modeling volatility and the SAVI has a significant positive relationship with all selected indices.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.inderscience.com%2Flink.php%3Fid%3D136131%3Bh%3Drepec%3Aids%3Aafasfa%3Av%3A14%3Ay%3A2024%3Ai%3A1%3Ap%3A83-99
- Identifier: RePEc:ids:afasfa:v:14:y:2024:i:1:p:83-99
- Released: 2024-02-07
- First featured: Quant Letter No. 36 (2024-02-07): https://www.ml-quant.com/issues/2024-02-07/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Risk Revisited](https://www.ml-quant.com/papers/ssrn/4825844/): The study identifies recency, cluster, and sign as three factors shaping investors' risk perceptions of a stock, influencing trading volume and future volatility.
- [Rough Volatility: Fact or Artefact?](https://www.ml-quant.com/papers/arxiv/2203.13820/): Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise.
- [Predicting the volatility of major energy commodity prices: The dynamic persistence model](https://www.ml-quant.com/papers/arxiv/2402.01354/): The article proposes a new method for forecasting oil-based volatility that models varying persistence shocks together, improving predictions and surpassing standard models.
- [Option pricing for Barndorff–Nielsen and Shephard model by supervised deep learning](https://www.ml-quant.com/papers/arxiv/2402.00445/): A supervised deep-learning scheme has been developed to accurately compute call option prices for the Barndorff-Nielsen and Shephard model, using an additional input variable from the Black-Scholes formula.
- [TSMixer and Realized Volatility Prediction](https://www.ml-quant.com/papers/ssrn/4713756/): Stock Volatility Forecasting with Neural Networks: The TSMixer neural network model has proven to be more effective than traditional models in predicting stock market volatility, indicating a possible shift towards simpler models in the future.
- [Inflation Volatility and the Directional Volatility Ratio](https://www.ml-quant.com/papers/ssrn/4724237/): The article presents a new method called the directional volatility ratio for predicting inflation trends, which is more effective than traditional methods.