Nonparametric predictive inference for stock returns

作者：Baker Rebecca M; Coolen Maturi Tahani^*; Coolen Frank P A

来源：Journal of Applied Statistics, 2017, 44(8): 1333-1349.

DOI：10.1080/02664763.2016.1204429

• Imprecise probability
• lower and upper probability
• nonparametric predictive inference
• pairwise comparison
• stock returns

In finance, inferences about future asset returns are typically quantified with the use of parametric distributions and single-valued probabilities. It is attractive to use less restrictive inferential methods, including nonparametric methods which do not require distributional assumptions about variables, and imprecise probability methods which generalize the classical concept of probability to set-valued quantities. Main attractions include the flexibility of the inferences to adapt to the available data and that the level of imprecision in inferences can reflect the amount of data on which these are based. This paper introduces nonparametric predictive inference (NPI) for stock returns. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. NPI is presented for inference about future stock returns, as a measure for risk and uncertainty, and for pairwise comparison of two stocks based on their future aggregate returns. The proposed NPI methods are illustrated using historical stock market data.

• 出版日期2017