• Media type: E-Book
  • Title: Uncertain Covariance Models and Uncertainty-Penalized Portfolio Optimization
  • Contributor: Shah, Anish [VerfasserIn]
  • imprint: [S.l.]: SSRN, [2021]
  • Extent: 1 Online-Ressource (23 p)
  • Language: English
  • DOI: 10.2139/ssrn.2616109
  • Identifier:
  • Keywords: Covariance ; Estimation error ; Multi-factor models ; Portfolio optimization ; Regularization ; Uncertainty
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 9, 2015 erstellt
  • Description: Covariance appears throughout investment management, e.g., in risk reporting and control, portfolio construction, risk parity, smart beta, algorithmic trading, and hedging. It is usually represented via multi-factor model. The form’s fewer parameters and structure—comovement through sensitivity to common factors, a residual component for uncorrelated variance—soften insufficient and non-stationary data issues. Nevertheless, parameter values remain inferred and not perfectly accurate. Common practice ignores the error and proceeds from point-estimates. This paper retains the error and propagates estimates of parameters’ mean and covariance to their effect at the investment portfolio level. Forecasted portfolio variance changes from a number to a mean and standard deviation, the latter representing uncertainty. Applications include more informative portfolio risk assessment, uncertainty-penalized optimization to counter estimation error and improve realized utility, and uncertainty indifference bands to lower trading costs
  • Access State: Open Access