Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ilaria Piatti"'
Publikováno v:
The Review of Financial Studies. 35:3710-3741
This paper proposes an aggregation scheme of subjective bond return expectations based on the historical accuracy of professional interest rate forecasters. We use disaggregated survey data on bond returns and document large disagreement in the cross
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
SSRN Electronic Journal.
The forecasting literature has presented overwhelming evidence that the aggregation of heterogeneous expectations leads to improvements in forecast accuracy; however, outperforming a simple equal weight- ing scheme has proved challenging. This paper
Autor:
Ilaria Piatti, Fabio Trojani
Asymptotic tests over-reject the null of no predictability in present-value models. We develop a nonparametric testing approach in state-space models, implying reliable finite sample inference under weak assumptions on price–dividend ratio and divi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b6e910b92603f6933b4084654d278c5
https://ora.ox.ac.uk/objects/uuid:e6df13ff-784a-4aed-8962-aee6131a3378
https://ora.ox.ac.uk/objects/uuid:e6df13ff-784a-4aed-8962-aee6131a3378
Publikováno v:
SSRN Electronic Journal.
This paper documents large micro-heterogeneity and forecasting skill in the cross-section of survey based bond risk premia. We reject informationally constrained rational expectations but show a learning model distorted by sentiment is consistent wit
Autor:
Ilaria Piatti
Publikováno v:
SSRN Electronic Journal.
This paper investigates the asset pricing implications of investor disagreement about the likelihood of a systematic disaster. I specify a general equilibrium model with multiple trees and heterogeneous beliefs about rare event risk, to understand ho
Autor:
Fabio Trojani, Ilaria Piatti
Publikováno v:
SSRN Electronic Journal.
Conventional tests of present-value models over-reject the null of no predictability. In order to better account for the intrinsic probability of detecting predictive relations by chance alone, we develop a new nonparametric Monte Carlo testing metho