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Daniel R. Smith
Researcher at University of Bristol
Publications - 88
Citations - 3331
Daniel R. Smith is an academic researcher from University of Bristol. The author has contributed to research in topics: Population & Volatility (finance). The author has an hindex of 30, co-authored 88 publications receiving 2781 citations. Previous affiliations of Daniel R. Smith include University of Plymouth & Queensland University of Technology.
Papers
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The level and quality of Value-at-Risk disclosure by commercial banks
TL;DR: In this article, the authors study both the level of value-at-risk (VaR) disclosure and the accuracy of the disclosed VaR figures for a sample of US and international commercial banks.
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Sex equality can explain the unique social structure of hunter-gatherer bands
Mark Dyble,Gul Deniz Salali,Nikhil Chaudhary,Abigail E. Page,Daniel R. Smith,James Thompson,Lucio Vinicius,Ruth Mace,Andrea Bamberg Migliano +8 more
TL;DR: The results suggest that pair-bonding and increased sex egalitarianism in human evolutionary history may have had a transformative effect on human social organization.
Journal ArticleDOI
Cooperation and the evolution of hunter-gatherer storytelling
Daniel R. Smith,Daniel R. Smith,Philip Schlaepfer,Katie Major,Mark Dyble,Abigail E. Page,James Thompson,Nikhil Chaudhary,Gul Deniz Salali,Ruth Mace,Ruth Mace,Leonora Astete,Marilyn Ngales,Lucio Vinicius,Andrea Bamberg Migliano +14 more
TL;DR: Benefits of storytelling in Agta hunter-gatherer communities are shown, as storytellers have higher reproductive success and storytelling is associated with higher cooperation in the group, suggesting one of the adaptive functions of storytelling among hunter gatherers may be to organise cooperation.
Journal ArticleDOI
The Level and Quality of Value-at-Risk Disclosure by Commercial Banks
TL;DR: In this paper, the authors study both the level of Value-at-Risk (VaR) disclosure and the accuracy of the disclosed VaR figures for a sample of US and international commercial banks.
Posted Content
Evaluating Value-at-Risk Models via Quantile Regression
TL;DR: The authors proposed a new backtest that does not rely solely on binary variables, such as whether or not there was an exception, and showed that the new back test provides a sufficient condtion to assess the finite sample performance of a quantile model whereas the existing ones do not.