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Enhancing realised volatility prediction in emerging markets
Thesis (MCom)--Stellenbosch University, 2023.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2023
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| _version_ | 1867614045603889152 |
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| access_status_str | Open Access |
| author | Maphatsoe, Phuthehang |
| author2 | Alfeus, Mesias |
| author_browse | Alfeus, Mesias Maphatsoe, Phuthehang |
| author_facet | Alfeus, Mesias Maphatsoe, Phuthehang |
| author_sort | Maphatsoe, Phuthehang |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MCom)--Stellenbosch University, 2023. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/128875 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:45:46.810Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/128875 Enhancing realised volatility prediction in emerging markets Maphatsoe, Phuthehang Alfeus, Mesias Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science. Stochastic analysis Financial risk -- Forecasting -- Econometric models Financial risk management -- Econometric models Stock price forecasting -- Mathematical models UCTD Thesis (MCom)--Stellenbosch University, 2023. 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. AFRIKAANSE OPSOMMING: Hierdie navorsingsopdrag stel ’n omvattende raamwerk voor wat gemik is op die verbetering van die akkuraatheid van gerealiseerde volatiliteitsvoorspellings binne die konteks van die Suid-Afrikaanse finansiele mark. Die fundamentele benadering is gewortel in die gebruik van hoe frekwensie data en die gebruik van volatiliteitsmodelle wat die inherente hoe volhoubaarheid effektief vasle wat dikwels in finansiele markte waargeneem word. Die studie is veral gefokus op die evaluering van vier onder- skeie modelle: die Heterogene AutoRegressive (HAR), Veralgemeende AutoRegressive Kondisionele Heteroskedastisiteit (realGARCH), Herhalende Kondisionele Heteroskedastisiteit (RECH), en die Onbehandelde Fraksionele Stogastiese Volatiliteit (RFSV) modelle. Verder brei die studie hierdie modelle uit om die Suid-Afrikaanse geimpliceerde volatiliteit (IV), bekend as die Suid-Afrikaanse Volatiliteitsindeks (SAVI), as ’n eksogene veranderlike in te sluit, met die verwagting dat hierdie uitbreiding die akkuraatheid van volatiliteitsramings verder sal verfyn. Hierdie geselekteerde mod- elle is met opset ontwerp om die ingewikkelde dinamika en lang-termyn afhanklikhede wat duidelik is binne finansiele tydreeks vas te le, eienskappe wat dikwels deur konvensionele voorspellingsme- todes oor die hoof gesien word. Die empiriese ondersoek is gebaseer op die ondersoek van vier sleutelfinansiele indekse binne die Suid-Afrikaanse mark. Die bevindinge van hierdie uitgebreide ontleding beklemtoon die kenmerkende prestasie van elke model in terme van die vaslegging van langtermynvolatiliteitspatrone. Opmerklik is dat die HAR-model na vore kom as die mees bedrewe om hierdie volgehoue patrone vas te le, terwyl die realGARCH, RECH, en RFSV modelle ook lofwaardige prestasies lewer, alhoewel tot wisselende grade. Verder is bevind dat die insluiting van die SAVI as ’n eksogene veranderlike die empiriese pas en voorspellingsvermoe van die modelle verhoog. Hierdie verbetering is veral duidelik wanneer voorspellingsakkuraatheid oor een-dag en multi-periode horisonne beoordeel word. Hierdie resultate bevestig die effektiwiteit van die gekose modelle en bied waardevolle insigte in hul geskiktheid vir die modelering van die unieke eienskappe van die Suid-Afrikaanse finansiele mark. In ’n breer konteks bied hierdie studie noodsaaklike in- sigte in die voorspelling van gerealiseerde volatiliteit binne die Suid-Afrikaanse finansiele mark. Die praktiese implikasies van hierdie bevindinge is betekenisvol, aangesien dit praktisyns en beleggers die kennis verskaf wat nodig is om welingeligte besluite te neem. Masters 2023-11-02T07:52:16Z 2024-01-08T14:15:05Z 2023-11-02T07:52:16Z 2024-01-08T14:15:05Z 2023-12 Thesis https://scholar.sun.ac.za/handle/10019.1/128875 en_ZA Stellenbosch University xiv, 63 pages : illustrations, includes annexures application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Stochastic analysis Financial risk -- Forecasting -- Econometric models Financial risk management -- Econometric models Stock price forecasting -- Mathematical models UCTD Maphatsoe, Phuthehang Enhancing realised volatility prediction in emerging markets |
| title | Enhancing realised volatility prediction in emerging markets |
| title_full | Enhancing realised volatility prediction in emerging markets |
| title_fullStr | Enhancing realised volatility prediction in emerging markets |
| title_full_unstemmed | Enhancing realised volatility prediction in emerging markets |
| title_short | Enhancing realised volatility prediction in emerging markets |
| title_sort | enhancing realised volatility prediction in emerging markets |
| topic | Stochastic analysis Financial risk -- Forecasting -- Econometric models Financial risk management -- Econometric models Stock price forecasting -- Mathematical models UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/128875 |
| work_keys_str_mv | AT maphatsoephuthehang enhancingrealisedvolatilitypredictioninemergingmarkets |