Portfolio Value at Risk Based on Independent Components Analysis
AbstractRisk management technology applied to high dimensional portfolios needs simple and fast methods for calculation of Value-at-Risk (VaR). The multivariate normal framework provides a simple off-the-shelf methodology but lacks the heavy tailed distributional properties that are observed in data. A principle component based method (tied closely to the elliptical structure of the distribution) is therefore expected to be unsatisfactory. Here we propose and analyze a technology that is based on Independent Component Analysis (ICA). We study the proposed ICVaR methodology in an extensive simulation study and apply it to a high dimensional portfolio situation. Our analysis yields very accurate VaRs.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2005-060.
Length: 25 pages
Date of creation: Sep 2005
Date of revision:
independent component analysis; Value-at-Risk;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G20 - Financial Economics - - Financial Institutions and Services - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-01-24 (All new papers)
- NEP-CMP-2006-01-24 (Computational Economics)
- NEP-FIN-2006-01-24 (Finance)
- NEP-FMK-2006-01-24 (Financial Markets)
- NEP-RMG-2006-01-24 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Chen, Ying & HÃ¤rdle, Wolfgang & Jeong, Seok-Oh, 2008.
"Nonparametric Risk Management With Generalized Hyperbolic Distributions,"
Journal of the American Statistical Association,
American Statistical Association, vol. 103(483), pages 910-923.
- Ying Chen & Wolfgang Härdle & Seok-Oh Jeong, 2004. "Nonparametric Risk Management with Generalized Hyperbolic Distributions," SFB 649 Discussion Papers SFB649DP2005-001, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany, revised Aug 2005.
- Robert F. Engle & Kevin Sheppard, 2001.
"Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH,"
NBER Working Papers
8554, National Bureau of Economic Research, Inc.
- Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
- Kritski, Oleg & Ulyanova, Marina, 2007. "Assessment of Multivariate Financial Risks of a Stock Share Portfolio," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 8(4), pages 3-17.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (RDC-Team).
If references are entirely missing, you can add them using this form.