The Estimation of Leverage Effect With High-Frequency Data
| Autor: | Per A. Mykland, Christina Dan Wang |
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| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Journal of the American Statistical Association. 109:197-215 |
| ISSN: | 1537-274X 0162-1459 |
| DOI: | 10.1080/01621459.2013.864189 |
| Popis: | The leverage effect has become an extensively studied phenomenon that describes the (usually) negative relation between stock returns and their volatility. Although this characteristic of stock returns is well acknowledged, most studies of the phenomenon are based on cross-sectional calibration with parametric models. On the statistical side, most previous works are conducted over daily or longer return horizons, and few of them have carefully studied its estimation, especially with high-frequency data. However, estimation of the leverage effect is important because sensible inference is possible only when the leverage effect is estimated reliably. In this article, we provide nonparametric estimation for a class of stochastic measures of leverage effect. To construct estimators with good statistical properties, we introduce a new stochastic leverage effect parameter. The estimators and their statistical properties are provided in cases both with and without microstructure noise, under the stochastic volat... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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