Abstract
This chapter discusses both static and dynamic hedge ratio in detail. In static analysis, we discuss minimum-variance hedge ratio, Sharpe hedge ratio, and optimum mean-variance hedge ratio. In addition, several time series analysis methods such as the multivariate skew-normal distribution method, the autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) methods, the regime-switching GARCH model, and the random coefficient method are used to show how hedge ratio can be estimated.
| Original language | English |
|---|---|
| Title of host publication | Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (In 4 Volumes) |
| Editors | Cheng Few Lee, John C. Lee |
| Place of Publication | London UK |
| Publisher | World Scientific Publishing |
| Chapter | 11 |
| Pages | 431-483 |
| Number of pages | 53 |
| Volume | 1 |
| Edition | 1st |
| ISBN (Electronic) | 9789811202391 |
| ISBN (Print) | 9789811202384 |
| DOIs | |
| Publication status | Published - 2021 |
Keywords
- ARCH method
- Cara utility function
- Co-integration and error assertion method effectiveness
- Garch method
- Hedge ratio
- Maximum mean extended-gini coefficient hedge ratio
- Minimum generalized semi-variance hedge ratio
- Minimum value-at-risk hedge ratio multivariable spew-normal distribution method
- Minimum variance hedge ratio
- Optimum mean meg hedge ratio
- Optimum mean variance hedge ratio
- Random coefficient method
- Regime-switching garch method
- Sharpe hedge ratio
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