Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm
The heterogeneous autoregressive (HAR) models of high-frequency realized volatility are inspired by the Heterogeneous Market Hypothesis and incorporate daily, weekly and monthly realized volatilities in the volatility dynamics with a (1,5,22) time horizon structure. We build on the HAR models and pr...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/943041 |
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| _version_ | 1832564079077621760 |
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| author | Hui Qu Ping Ji |
| author_facet | Hui Qu Ping Ji |
| author_sort | Hui Qu |
| collection | DOAJ |
| description | The heterogeneous autoregressive (HAR) models of high-frequency realized volatility are inspired by the Heterogeneous Market Hypothesis and incorporate daily, weekly and monthly realized volatilities in the volatility dynamics with a (1,5,22) time horizon structure. We build on the HAR models and propose a new framework, adaptive heterogeneous autoregressive (AHAR) models, whose time horizon structures are optimized by a genetic algorithm. Our models can be applied to markets with different heterogeneous structures, and their time horizon structures can be adjusted adaptively as the market's heterogeneous structure varies. Moving window tests with five-minute returns of the CSI 300 index indicate that the (1,5,22) structure originally proposed for American stock markets is not the best choice for Chinese stock markets, and Chinese stock markets’ heterogeneous structure does vary over time. Using four common loss functions, we find that the AHAR models outperform the corresponding HAR models in most of the forecast windows and thus are reasonable choices for volatility forecasting practices. |
| format | Article |
| id | doaj-art-b5f0493bdd0345479d0f81dedab3401e |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-b5f0493bdd0345479d0f81dedab3401e2025-02-03T01:11:49ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/943041943041Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic AlgorithmHui Qu0Ping Ji1School of Management and Engineering, Nanjing University, Nanjing, Jiangsu 210093, ChinaSchool of Management and Engineering, Nanjing University, Nanjing, Jiangsu 210093, ChinaThe heterogeneous autoregressive (HAR) models of high-frequency realized volatility are inspired by the Heterogeneous Market Hypothesis and incorporate daily, weekly and monthly realized volatilities in the volatility dynamics with a (1,5,22) time horizon structure. We build on the HAR models and propose a new framework, adaptive heterogeneous autoregressive (AHAR) models, whose time horizon structures are optimized by a genetic algorithm. Our models can be applied to markets with different heterogeneous structures, and their time horizon structures can be adjusted adaptively as the market's heterogeneous structure varies. Moving window tests with five-minute returns of the CSI 300 index indicate that the (1,5,22) structure originally proposed for American stock markets is not the best choice for Chinese stock markets, and Chinese stock markets’ heterogeneous structure does vary over time. Using four common loss functions, we find that the AHAR models outperform the corresponding HAR models in most of the forecast windows and thus are reasonable choices for volatility forecasting practices.http://dx.doi.org/10.1155/2014/943041 |
| spellingShingle | Hui Qu Ping Ji Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm Abstract and Applied Analysis |
| title | Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm |
| title_full | Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm |
| title_fullStr | Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm |
| title_full_unstemmed | Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm |
| title_short | Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm |
| title_sort | adaptive heterogeneous autoregressive models of realized volatility based on a genetic algorithm |
| url | http://dx.doi.org/10.1155/2014/943041 |
| work_keys_str_mv | AT huiqu adaptiveheterogeneousautoregressivemodelsofrealizedvolatilitybasedonageneticalgorithm AT pingji adaptiveheterogeneousautoregressivemodelsofrealizedvolatilitybasedonageneticalgorithm |