Factors on Demand

版本 1.6.0.0 (4.1 MB) 作者: Attilio Meucci
Proper implementation of factor models: bottom-up estimation, top-down attribution
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更新时间 2011/5/9

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Three case studies: random matrix theory for estimation vs. cross-sectional model for attribution; hedging based on full-repricing instead of Black-Scholes deltas; heuristcs for best K attribution/hedging factors out N

To walk through the code and for a thorough description, see
Meucci A., "Factors on Demand",

Latest version of article and code available at http://symmys.com/node/164

引用格式

Attilio Meucci (2024). Factors on Demand (https://www.mathworks.com/matlabcentral/fileexchange/26853-factors-on-demand), MATLAB Central File Exchange. 检索来源 .

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FactorsOnDemand/NoGreekHedging/

FactorsOnDemand/SelectionHeuristics/

FactorsOnDemand/StatisticalVsCrossSectional/

版本 已发布 发行说明
1.6.0.0

updated references
1.5.0.0

Added case study

1.1.0.0

Added one case study, also detailed in the above article
1.0.0.0