Modeling and forecasting volatility of index return: an empirical evidence of FTSE 100 Index
Volatility forecasting in an important area of research in financial markets and immense effort expended in improving volatility models, since better forecasts will ultimately lead to more accurate options pricing and better risk management. This thesis attempts at modeling and forecasting the volat...
| Main Author: | |
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| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2013
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| Online Access: | https://eprints.nottingham.ac.uk/26770/ |
| _version_ | 1848793243220705280 |
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| author | ZHU, Lin |
| author_facet | ZHU, Lin |
| author_sort | ZHU, Lin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Volatility forecasting in an important area of research in financial markets and immense effort expended in improving volatility models, since better forecasts will ultimately lead to more accurate options pricing and better risk management. This thesis attempts at modeling and forecasting the volatility of FTSE 100 index return of United Kingdom market, using daily and weekly frequency data range from January 1, 2003 to July 31, 2013. For each return series, the last 60 observations are used as holdout sample for out-of-sample forecast evaluation. The forecasting models that are considered in this study range from the relative simple GARCH (1, 1) model to relatively complex GARCH models, including Exponential GARCH (p, q), Threshold GARCH (p, q) and GJR-GARCH (p, q).
The findings present the inappropriateness of the asymmetric TGARCH and GJR models in modeling FTSE 100 index return volatility. The results based on out-of-sample forecasts provide evidence of superiority of GARCH (1, 1) in forecasting conditional volatility of daily frequency returns while the asymmetric EGARCH (1, 1) does perform better in forecasting volatility of weekly frequency returns. The findings are evidenced by three different error measurements to evaluate the out-of-sample forecasting accuracy. Moreover, this thesis also detects the risk-reward relation in the FTSE 100 index returns by GARCH-M model. The results suggest such relationship is positive but statically insignificant. |
| first_indexed | 2025-11-14T18:57:12Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-26770 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:57:12Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-267702017-10-19T13:38:47Z https://eprints.nottingham.ac.uk/26770/ Modeling and forecasting volatility of index return: an empirical evidence of FTSE 100 Index ZHU, Lin Volatility forecasting in an important area of research in financial markets and immense effort expended in improving volatility models, since better forecasts will ultimately lead to more accurate options pricing and better risk management. This thesis attempts at modeling and forecasting the volatility of FTSE 100 index return of United Kingdom market, using daily and weekly frequency data range from January 1, 2003 to July 31, 2013. For each return series, the last 60 observations are used as holdout sample for out-of-sample forecast evaluation. The forecasting models that are considered in this study range from the relative simple GARCH (1, 1) model to relatively complex GARCH models, including Exponential GARCH (p, q), Threshold GARCH (p, q) and GJR-GARCH (p, q). The findings present the inappropriateness of the asymmetric TGARCH and GJR models in modeling FTSE 100 index return volatility. The results based on out-of-sample forecasts provide evidence of superiority of GARCH (1, 1) in forecasting conditional volatility of daily frequency returns while the asymmetric EGARCH (1, 1) does perform better in forecasting volatility of weekly frequency returns. The findings are evidenced by three different error measurements to evaluate the out-of-sample forecasting accuracy. Moreover, this thesis also detects the risk-reward relation in the FTSE 100 index returns by GARCH-M model. The results suggest such relationship is positive but statically insignificant. 2013-09-20 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/26770/1/modeling_and_forecasting_volatility_of_index_return__an_empirical_evidence_of_FTSE_100_Index.pdf ZHU, Lin (2013) Modeling and forecasting volatility of index return: an empirical evidence of FTSE 100 Index. [Dissertation (University of Nottingham only)] (Unpublished) GARCH TGARCH GJR EGARCH modeling volatility forecasting volatility |
| spellingShingle | GARCH TGARCH GJR EGARCH modeling volatility forecasting volatility ZHU, Lin Modeling and forecasting volatility of index return: an empirical evidence of FTSE 100 Index |
| title | Modeling and forecasting volatility of index return: an empirical evidence of FTSE 100 Index |
| title_full | Modeling and forecasting volatility of index return: an empirical evidence of FTSE 100 Index |
| title_fullStr | Modeling and forecasting volatility of index return: an empirical evidence of FTSE 100 Index |
| title_full_unstemmed | Modeling and forecasting volatility of index return: an empirical evidence of FTSE 100 Index |
| title_short | Modeling and forecasting volatility of index return: an empirical evidence of FTSE 100 Index |
| title_sort | modeling and forecasting volatility of index return: an empirical evidence of ftse 100 index |
| topic | GARCH TGARCH GJR EGARCH modeling volatility forecasting volatility |
| url | https://eprints.nottingham.ac.uk/26770/ |