Mathematical Research Data Initiative
Main page
Recent changes
Random page
Help about MediaWiki
Create a new Item
Create a new Property
Create a new EntitySchema
Merge two items
In other projects
Discussion
View source
View history
Purge
English
Log in

SUB-ADDITIVE RECURSIVE "MATCHING" NOISE AND BIASES IN RISK-WEIGHTED INDEX CALCULATION METHODS IN INCOMPLETE MARKETS WITH PARTIALLY OBSERVABLE MULTI-ATTRIBUTE PREFERENCES

From MaRDI portal
Publication:2864863
Jump to:navigation, search

DOI10.1142/S1793830913500201zbMath1408.91242arXiv2005.01708OpenAlexW3122438878WikidataQ123250098 ScholiaQ123250098MaRDI QIDQ2864863

Michael Nwogugu

Publication date: 26 November 2013

Published in: Discrete Mathematics, Algorithms and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.01708


zbMATH Keywords

complexitydecision analysisevolutionary computationindex theoryrisk managementasset allocation


Mathematics Subject Classification ID

Financial applications of other theories (91G80)




Cites Work

  • Portfolio management without probabilities or statistics
  • Further critique of GARCH/ARMA/VAR/EVT Stochastic-Volatility models and related approaches
  • Recursive smooth ambiguity preferences
  • On the diversity of equity markets
  • The exchangeable multinomial model as an approach to testing deterministic axioms of choice and measurement
  • Comparing downside risk measures for heavy tailed distributions
  • Investment volatility: A critique of standard beta estimation and a simple way forward
  • Rethinking Rigor in Calculus: The Role of the Mean Value Theorem
  • An Economic Index of Riskiness
  • WHY THE RETURN NOTION MATTERS
Retrieved from "https://portal.mardi4nfdi.de/w/index.php?title=Publication:2864863&oldid=15804888"
Tools
What links here
Related changes
Special pages
Printable version
Permanent link
Page information
MaRDI portal item
This page was last edited on 3 February 2024, at 20:25.
Privacy policy
About MaRDI portal
Disclaimers
Imprint
Powered by MediaWiki