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Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates
- Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates
- Choi, Ji-Eun; Shin, Dong Wan
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- JOURNAL OF FORECASTING
- JOURNAL OF FORECASTING vol. 37, no. 6, pp. 691 - 704
- asymmetry; implied volatility; jump; LHARX model; realized volatility; volatility index
- SSCI; SCOPUS
- Document Type
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- For leverage heterogeneous autoregressive (LHAR) models with jumps and other covariates, called LHARX models, multistep forecasts are derived. Some optimal properties of forecasts in terms of conditional volatilities are discussed, which tells us to model conditional volatility for return but not for the LHARX regression error and other covariates. Forecast standard errors are constructed for which we need to model conditional volatilities both for return and for LHAR regression error and other blue covariates. The proposed methods are well illustrated by forecast analysis for the realized volatilities of the US stock price indexes: the S&P 500, the NASDAQ, the DJIA, and the RUSSELL indexes.
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- 자연과학대학 > 통계학전공 > Journal papers
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