Power mapping with dynamical adjustment for improved portfolio optimization
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1547178
- author
- Schaefer, Rudi ; Nilsson, Fredrik LU and Guhr, Thomas
- organization
- publishing date
- 2010
- 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
- 2016-04-01 09:50:01
- date last changed
- 2022-03-19 06:51:33
@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}},
keywords = {{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}},
doi = {{10.1080/14697680902748498}},
volume = {{10}},
year = {{2010}},
}