Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-04
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Qu, Hui& Ji, Ping. 2014. Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1015114
Modern Language Association (MLA)
Qu, Hui& Ji, Ping. Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm. Abstract and Applied Analysis No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1015114
American Medical Association (AMA)
Qu, Hui& Ji, Ping. Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1015114
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1015114