Browsing by Author "Maphatsoe, Phuthehang"

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    Enhancing realised volatility prediction in emerging markets
    (Stellenbosch : Stellenbosch University, 2023-12) Maphatsoe, Phuthehang; Alfeus, Mesias; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH SUMMARY: This research assignment introduces a comprehensive framework aimed at improving the accuracy of realised volatility forecasts within the context of the South African financial market. The fundamental approach is rooted in the utilisation of high-frequency data and the employment of volatility models that effectively capture the inherent high persistence commonly observed in financial markets. The study is particularly centred on the evaluation of four distinct models: the Heterogeneous AutoRegressive (HAR), Generalised AutoRegressive Conditional Heteroscedasticity (realGARCH), R.ecurrent Conditional Heteroskedasticity (RECH), and the R.ough Fractional Stochastic Volatility (RFSV) models. Furthermore, the study extends these models to incorporate the South African implied volatility (IV), referred to as the South African Volatility Index (SAVI), as an exogenous variable, with the expectation that this augmentation will further refine the accuracy of volatility estimations. These selected models are intentionally designed to capture the intricate dynamics and long-range dependencies that are evident within financial time series, characteristics often overlooked by conventional forecasting methods. The empirical investigation is based on the examination of four key financial indices within the South African market. The findings of this extensive analysis highlight the distinctive performance of each model in terms of capturing long-term volatility patterns. Notably, the HAR model emerges as the most adept at capturing these enduring patterns, while the realGARCH, R.ECH, and RFSV models also display commendable performance, albeit to varying degrees. Furthermore, the inclusion of the SAVI as an exogenous variable is found to enhance the empirical fit and predictive capacity of the models. This enhancement is particularly evident when assessing forecasting accuracy across both one-day and multi-period horizons. These results affirm the effectiveness of the chosen models and provide valuable insights into their suitability for modelling the South African financial market's unique characteristics. In a broader context, this study offers essential insights into realised volatility forecasting within the South African financial market. The practical implications of these findings are substantial, as they provide practitioners and investors with the knowledge required to make well-informed decisions.