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来源类型Working Paper
规范类型报告
DOI10.3386/w28569
来源IDWorking Paper 28569
Consistent Local Spectrum (LCM) Inference for Predictive Return Regressions
Torben G. Andersen; Rasmus T. Varneskov
发表日期2021-03-22
出版年2021
语种英语
摘要This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a latent persistent conditional mean, whose memory is difficult to estimate consistently by standard techniques in finite samples. Moreover, the predictors may be endogenous and “imperfect”. In this setting, we develop a consistent Local speCtruM (LCM) estimation procedure, that delivers asymptotic Gaussian inference. Furthermore, we provide a new LCM-based estimator of the conditional mean persistence, that leverages biased regression slopes as well as new LCM-based tests for significance of (a subset of) the predictors, which are valid even without estimating the return persistence. Simulations illustrate the theoretical arguments. Finally, in an empirical application to monthly S&P 500 return predictions, we provide evidence for a fractionally integrated conditional mean component. Moreover, using our new LCM procedure and tools, we document significant predictive power for key state variables such as the default spread and treasury interest rates.
主题Financial Economics ; Portfolio Selection and Asset Pricing ; Financial Markets
URLhttps://www.nber.org/papers/w28569
来源智库National Bureau of Economic Research (United States)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/586243
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GB/T 7714
Torben G. Andersen,Rasmus T. Varneskov. Consistent Local Spectrum (LCM) Inference for Predictive Return Regressions. 2021.
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