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Power mapping with dynamical adjustment for improved portfolio optimization

Schaefer, Rudi; Nilsson, Fredrik LU and Guhr, Thomas (2010) In Quantitative Finance 10(1). p.107-119
Abstract
For financial risk management it is of vital interest to have good estimates for the correlations between the stocks. It has been found that the correlations obtained from historical data are covered by a considerable amount of noise, which leads to a substantial error in the estimation of the portfolio risk. A method to suppress this noise is power mapping. It raises the absolute value of each matrix element to a power q while preserving the sign. In this paper we use the Markowitz portfolio optimization as a criterion for the optimal value of q and find a K/T dependence, where K is the portfolio size and T the length of the time series. Both in numerical simulations and for real market data we find that power mapping leads to portfolios... (More)
For financial risk management it is of vital interest to have good estimates for the correlations between the stocks. It has been found that the correlations obtained from historical data are covered by a considerable amount of noise, which leads to a substantial error in the estimation of the portfolio risk. A method to suppress this noise is power mapping. It raises the absolute value of each matrix element to a power q while preserving the sign. In this paper we use the Markowitz portfolio optimization as a criterion for the optimal value of q and find a K/T dependence, where K is the portfolio size and T the length of the time series. Both in numerical simulations and for real market data we find that power mapping leads to portfolios with considerably reduced risk. It compares well with another noise reduction method based on spectral filtering. A combination of both methods yields the best results. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
optimization, Numerical simulation, Monte Carlo methods, markets, Financial, Dynamic models, Portfolio, Econophysics, Correlation structures
in
Quantitative Finance
volume
10
issue
1
pages
107 - 119
publisher
Taylor & Francis
external identifiers
  • wos:000273719700009
  • scopus:74249101831
ISSN
1469-7696
DOI
10.1080/14697680902748498
language
English
LU publication?
yes
id
5b0bb396-b487-4519-b50b-35a5255b0374 (old id 1547178)
date added to LUP
2010-02-24 09:50:29
date last changed
2018-06-24 03:00:53
@article{5b0bb396-b487-4519-b50b-35a5255b0374,
  abstract     = {For financial risk management it is of vital interest to have good estimates for the correlations between the stocks. It has been found that the correlations obtained from historical data are covered by a considerable amount of noise, which leads to a substantial error in the estimation of the portfolio risk. A method to suppress this noise is power mapping. It raises the absolute value of each matrix element to a power q while preserving the sign. In this paper we use the Markowitz portfolio optimization as a criterion for the optimal value of q and find a K/T dependence, where K is the portfolio size and T the length of the time series. Both in numerical simulations and for real market data we find that power mapping leads to portfolios with considerably reduced risk. It compares well with another noise reduction method based on spectral filtering. A combination of both methods yields the best results.},
  author       = {Schaefer, Rudi and Nilsson, Fredrik and Guhr, Thomas},
  issn         = {1469-7696},
  keyword      = {optimization,Numerical simulation,Monte Carlo methods,markets,Financial,Dynamic models,Portfolio,Econophysics,Correlation structures},
  language     = {eng},
  number       = {1},
  pages        = {107--119},
  publisher    = {Taylor & Francis},
  series       = {Quantitative Finance},
  title        = {Power mapping with dynamical adjustment for improved portfolio optimization},
  url          = {http://dx.doi.org/10.1080/14697680902748498},
  volume       = {10},
  year         = {2010},
}