Zobrazeno 1 - 10
of 319
pro vyhledávání: '"JENS PERCH NIELSEN"'
Publikováno v:
Financial Innovation, Vol 10, Iss 1, Pp 1-16 (2024)
Abstract The availability of many variables with predictive power makes their selection in a regression context difficult. This study considers robust and understandable low-dimensional estimators as building blocks to improve overall predictive powe
Externí odkaz:
https://doaj.org/article/958b8a3121934d70b61f5a0fc09e07dc
Autor:
Alan Penman
Publikováno v:
Annals of Actuarial Science. 7:167-168
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::80b63a08415e872779243af5dc5a940b
https://doi.org/10.1017/s1748499512000322
https://doi.org/10.1017/s1748499512000322
Publikováno v:
European Journal of Operational Research. 307:948-962
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec47e9cbaa3203d5aa2f92e0f42f04d8
https://doi.org/10.1016/j.ejor.2022.10.003
https://doi.org/10.1016/j.ejor.2022.10.003
Publikováno v:
Mathematics, Vol 9, Iss 6, p 620 (2021)
The fundamental interest of investors in econometric modeling for excess stock returns usually focuses either on short- or long-term predictions to individually reduce the investment risk. In this paper, we present a new and simple model that contemp
Externí odkaz:
https://doaj.org/article/aa62f92e78af43cab17a1de59dbd526d
Publikováno v:
Mathematics, Vol 8, Iss 6, p 927 (2020)
Long-term return expectations or predictions play an important role in planning purposes and guidance of long-term investors. Five-year stock returns are less volatile around their geometric mean than returns of higher frequency, such as one-year ret
Externí odkaz:
https://doaj.org/article/b93c80684b644622a0ec18666a02957b
Publikováno v:
Risks, Vol 8, Iss 2, p 54 (2020)
It is our pleasure to prologue the special issue on “Machine Learning in Insurance”, which represents a compilation of ten high-quality articles discussing avant-garde developments or introducing new theoretical or practical advances in this fiel
Externí odkaz:
https://doaj.org/article/31558499c19c44eeae9ed7beeb9fb38c
Publikováno v:
Dipòsit Digital de la UB
Universidad de Barcelona
Universidad de Barcelona
We present a method to integrate telematics data in a pay‐how‐you‐drive insurance pricing scheme that penalizes some near‐miss events. We illustrate our method with a sample of drivers for whom information on near‐miss events and claims fre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6680eea7fd6e45394e46260273d7b5af
https://doi.org/10.1111/jori.12340
https://doi.org/10.1111/jori.12340
Publikováno v:
Journal of Econometrics, 221(1), 43-67. Elsevier Science
van den Berg, G J, Janys, L, Mammen, E & Nielsen, J P 2021, ' A general semiparametric approach to inference with marker-dependent hazard rate models ', Journal of Econometrics, vol. 221, no. 1, pp. 43-67 . https://doi.org/10.1016/j.jeconom.2019.05.025
Journal of Econometrics, 221(1), 43-67. Elsevier BV
van den Berg, G J, Mammen, E, Janys, L & Nielsen, J P 2020, ' A General Semiparametric Approach to Inference with Marker-Dependent Hazard Rate Models ', Journal of Econometrics . https://doi.org/10.1016/j.jeconom.2019.05.025
van den Berg, G J, Janys, L, Mammen, E & Nielsen, J P 2021, ' A general semiparametric approach to inference with marker-dependent hazard rate models ', Journal of Econometrics, vol. 221, no. 1, pp. 43-67 . https://doi.org/10.1016/j.jeconom.2019.05.025
Journal of Econometrics, 221(1), 43-67. Elsevier BV
van den Berg, G J, Mammen, E, Janys, L & Nielsen, J P 2020, ' A General Semiparametric Approach to Inference with Marker-Dependent Hazard Rate Models ', Journal of Econometrics . https://doi.org/10.1016/j.jeconom.2019.05.025
We examine a new general class of hazard rate models for survival data, containing a parametric and a nonparametric component. Both can be a mix of a time effect and (possibly time-dependent) marker or covariate effects. A number of well-known models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::864cad6cf91cd02051045068eb141c87
https://doi.org/10.1016/j.jeconom.2019.05.025
https://doi.org/10.1016/j.jeconom.2019.05.025
Publikováno v:
Risks, Vol 7, Iss 4, p 113 (2019)
In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation ra
Externí odkaz:
https://doaj.org/article/07a0f5157e934d27bf9ff6d8c5a3ab02
Publikováno v:
Insurance: Mathematics and Economics. 96:31-52
A very popular forecasting tool in the actuarial sciences is the so-called chain ladder. Mammen et al. (2015) recently introduced in-sample forecasting, a general forecasting technique applicable in many fields which builds on the continuous chain la
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e817c71b1280b8fde76462a11edfbcec
https://doi.org/10.1016/j.insmatheco.2020.10.003
https://doi.org/10.1016/j.insmatheco.2020.10.003