Timeline for Linear programming and factor models vs M-V optimization?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Sep 3, 2023 at 19:23 | vote | accept | deblue | ||
| Aug 30, 2023 at 12:57 | comment | added | LattePrincess | Also as a subsequent example, it is easier for PMs to have a forecast on 50ish factor returns than a forecast of 1000+ asset returns. This helps just based on number of forecasts required but also because you are estimating systemic components (factors) instead of systemic and idiosyncratic components (stock returns for example). What this means is you need fewer forecasts and the forecasts are easier to get 'right' (i.e. it is easier to predict how the auto industry as a whole will perform than to predict Tesla's returns). | |
| Aug 30, 2023 at 12:28 | comment | added | LattePrincess | If you have factor exposures for your assets and a factor VCV matrix, you can use these to estimate the asset VCV. I remember reading that this tends to be more stable period-to-period as exposures don't change frequently and factors themselves are generally chosen because they are orthogonal. Here is the math describing the transformation: web.stanford.edu/~wfsharpe/mia/fac/mia_fac3.htm | |
| Aug 30, 2023 at 7:49 | comment | added | deblue | Can you please expand on (2). I have recently experimented with singular value decomposition. Applying SVD on the data and constructing the VCV is the equivalent of MLE estimator of VCV under Gaussian. | |
| Aug 29, 2023 at 17:54 | history | edited | LattePrincess | CC BY-SA 4.0 |
more clarifiaction
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| Aug 29, 2023 at 17:47 | history | edited | LattePrincess | CC BY-SA 4.0 |
clarifiaction
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| S Aug 29, 2023 at 17:36 | review | First answers | |||
| Aug 29, 2023 at 18:23 | |||||
| S Aug 29, 2023 at 17:36 | history | answered | LattePrincess | CC BY-SA 4.0 |