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Forecasting and modelling the VIX using Neural Networks
This study investigates the volatility forecasting ability of neural network models. In particular, we focus on the performance of Multi-layer Perceptron (MLP) and the Long Short Term (LSTM) Neural Networks in predicting the CBOE Volatility Index (VIX). The inputs into these models includes the VIX,...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2023
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| _version_ | 1867613207001038848 |
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| access_status_str | Open Access |
| author | Netshivhambe, Nomonde |
| author2 | Huang, Chun-Sung |
| author_browse | Huang, Chun-Sung Netshivhambe, Nomonde |
| author_facet | Huang, Chun-Sung Netshivhambe, Nomonde |
| author_sort | Netshivhambe, Nomonde |
| collection | Thesis |
| description | This study investigates the volatility forecasting ability of neural network models. In particular, we focus on the performance of Multi-layer Perceptron (MLP) and the Long Short Term (LSTM) Neural Networks in predicting the CBOE Volatility Index (VIX). The inputs into these models includes the VIX, GARCH(1,1) fitted values and various financial and macroeconomic explanatory variables, such as the S&P 500 returns and oil price. In addition, this study segments data into two sub-periods, namely a Calm and Crisis Period in the financial market. The segmentation of the periods caters for the changes in the predictive power of the aforementioned models, given the dierent market conditions. When forecasting the VIX, we show that the best performing model is found in the Calm Period. In addition, we show that the MLP has more predictive power than the LSTM. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37693 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:27.580Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37693 Forecasting and modelling the VIX using Neural Networks Netshivhambe, Nomonde Huang, Chun-Sung Data Science This study investigates the volatility forecasting ability of neural network models. In particular, we focus on the performance of Multi-layer Perceptron (MLP) and the Long Short Term (LSTM) Neural Networks in predicting the CBOE Volatility Index (VIX). The inputs into these models includes the VIX, GARCH(1,1) fitted values and various financial and macroeconomic explanatory variables, such as the S&P 500 returns and oil price. In addition, this study segments data into two sub-periods, namely a Calm and Crisis Period in the financial market. The segmentation of the periods caters for the changes in the predictive power of the aforementioned models, given the dierent market conditions. When forecasting the VIX, we show that the best performing model is found in the Calm Period. In addition, we show that the MLP has more predictive power than the LSTM. 2023-04-13T08:08:58Z 2023-04-13T08:08:58Z 2022 2023-04-12T08:31:48Z Master Thesis Masters MSc http://hdl.handle.net/11427/37693 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Data Science Netshivhambe, Nomonde Forecasting and modelling the VIX using Neural Networks |
| thesis_degree_str | Master's |
| title | Forecasting and modelling the VIX using Neural Networks |
| title_full | Forecasting and modelling the VIX using Neural Networks |
| title_fullStr | Forecasting and modelling the VIX using Neural Networks |
| title_full_unstemmed | Forecasting and modelling the VIX using Neural Networks |
| title_short | Forecasting and modelling the VIX using Neural Networks |
| title_sort | forecasting and modelling the vix using neural networks |
| topic | Data Science |
| url | http://hdl.handle.net/11427/37693 |
| work_keys_str_mv | AT netshivhambenomonde forecastingandmodellingthevixusingneuralnetworks |