Signal regression models for location, scale and shape with an application to stock returns
Brockhaus S, Fuest A, Mayr A, Greven S (2018)
Publication Type: Journal article
Publication year: 2018
Journal
Book Volume: 67
Pages Range: 665-686
Journal Issue: 3
DOI: 10.1111/rssc.12252
Abstract
We discuss scalar-on-function regression models where all parameters of the assumed response distribution can be modelled depending on covariates. We thus combine signal regression models with generalized additive models for location, scale and shape. Our approach is motivated by a time series of stock returns, where it is of interest to model both the expectation and the variance depending on lagged response values and functional liquidity curves. We compare two fundamentally different methods for estimation, a gradient boosting and a penalized-likelihood-based approach, and address practically important points like identifiability and model choice. Estimation by a componentwise gradient boosting algorithm allows for high dimensional data settings and variable selection. Estimation by a penalized-likelihood-based approach has the advantage of directly provided statistical inference.
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Germany (DE)How to cite
APA:
Brockhaus, S., Fuest, A., Mayr, A., & Greven, S. (2018). Signal regression models for location, scale and shape with an application to stock returns. Journal of the Royal Statistical Society Series C-Applied Statistics, 67(3), 665-686. https://doi.org/10.1111/rssc.12252
MLA:
Brockhaus, Sarah, et al. "Signal regression models for location, scale and shape with an application to stock returns." Journal of the Royal Statistical Society Series C-Applied Statistics 67.3 (2018): 665-686.
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