| 来源类型 | Working Paper
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| 规范类型 | 报告
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| DOI | 10.3386/t0162
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| 来源ID | Technical Working Paper 0162
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| Asymptotic Filtering Theory for Multivariate ARCH Models |
| Daniel B. Nelson
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| 发表日期 | 1994-08-01
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| 出版年 | 1994
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| 语种 | 英语
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| 摘要 | ARCH models are widely used to estimate conditional variances and covariances in financial time series models. How successfully can ARCH models carry out this estimation when they are misspecified? How can ARCH models be optimally constructed? Nelson and Foster (1994) employed continuous record asymptotics to answer these questions in the univariate case. This paper considers the general multivariate case. Our results allow us, for example, to construct an asymptotically optimal ARCH model for estimating the conditional variance or conditional beta of a stock return given lagged returns on the stock, volume, market returns, implicit volatility from options contracts, and other relevant data. We also allow for time-varying shapes of conditional densities (e.g., `heteroskewticity` and `heterokurticity'). Examples are provided. |
| 主题 | Econometrics
; Estimation Methods
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| URL | https://www.nber.org/papers/t0162
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/562218
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推荐引用方式
GB/T 7714 |
Daniel B. Nelson. Asymptotic Filtering Theory for Multivariate ARCH Models. 1994.
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文件名：
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t0162.pdf
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格式：
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Adobe PDF
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