Gaussian and logistic adaptations of smoothed safety first

Haley, M. Ryan

doi:10.1007/s10436-013-0235-4

Cited by 8 publications

(1 citation statement)

References 13 publications

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“…Many methods of estimating such a conditional expectation have been proposed, including kernel regression, orthogonal series estimation, nearest-neighbor estimation, and splines; see, e.g., Fan and Gijbels (1990), Härdle (1990), andSimonoff (1996) for general discussions of nonparametric regression and Haley (2014) for an application of smoothing to finance. Although, in principle, any of these methods could be used, here we use smoothing spline estimates, which are known to perform well when estimating the derivative of a regression function (Wahba 1990).…”

Section: It Follows That (8) Holds Withmentioning

confidence: 99%

A nonparametric approach to measuring the sensitivity of an asset’s return to the market

Severini

2016

Ann Finance

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In the market model the return on an asset is modeled as a linear function of the return on a market index with slope parameter beta. The coefficient beta is often used as a measure of the sensitivity of the asset's return to the market and to measure the component of the variance of the return that is explained by the market. However, both of these interpretations require the additional assumption that the error term in the market model has mean 0 conditional on the return on the market index, an assumption that is often difficult to verify in practice. In this paper, a nonparametric version of the market model is proposed that does not require such an assumption. This nonparametric model replaces the beta coefficient of the market model with a "beta curve" describing the relationship between the asset's return and that of the market locally near a given value of the market return. The proposed model is applied to stock returns, as well as to returns on mutual funds. Corresponding tests of the market model are given and it is shown that the nonparametric model often provides an improvement over the standard parametric market model.

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