The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of MATHEMATICS AND COMPUTERS IN SIMULATION
Transactions ID Number: 19-839
Full Name: Javier Perote
Position: Associate Professor
Age: ON
Sex: Male
Address: University of Salamanca, Campus Miguel de Unamuno, 37007 Salamanca
Country: SPAIN
Tel: +34-923-294400 ext. 3512
Tel prefix:
Fax:
E-mail address: perote@usal.es
Other E-mails: javier.perote@urjc.es
Title of the Paper: A new proposal for computing portfolio value-at-risk for semi-nonparametric distributions
Authors as they appear in the Paper: Trino-Manuel Ñíguez and Javier Perote
Email addresses of all the authors: T.Niguez@westminster.ac.uk,perote@usal.es
Number of paper pages: 8
Abstract: This paper proposes a semi-nonparametric (SNP) methodology for computing portfolio value-at-risk (VaR) that is more accurate than both the traditional Gaussian-assumption-based methods implemented in the software packages used by risk analysts (RiskMetrics), and alternative heavy-tailed distributions that seem to be very rigid to incorporate jumps and asymmetries in the distribution tails (e.g. the Student's t). The outperformance of the SNP distributions lies in the fact that Edgeworth and Gram-Charlier series represent a valid asymptotic approximation of any "regular" probability density function. In fact these expansions involve general and flexible parametric representations capable of featuring the salient empirical regularities of financial data. Furthermore these distributions can be extended to a multivariate context and may be estimated in several steps and thus we propose to estimate portfolio VaR in three steps: Firstly, estimating the conditional varia!
nce and covariance matrix of the portfolio consistently with the multivariate SNP distribution; Secondly, estimating the univariate distribution of the portfolio constrained to the portfolio variance obtained from the previous step; Thirdly, computing the corresponding quantile of the portfolio distribution by implementing straightforward recursive algorithms. We estimate the VaRs obtained with such methodology for different bivariate portfolios of stock indices and interests rates finding a clear underestimation (overestimation) of VaR measures obtained from the traditional Gaussian- (Student's t-) based methods compared to our SNP approach.
Keywords: Edgeworth and Gram-Charlier series, GARCH models, Multivariate densities, Semi-nonparametric distributions, Value-at-Risk
EXTENSION of the file: .doc
Special (Invited) Session: Portfolio VaR for SNP distributions
Organizer of the Session: 202-293
How Did you learn about congress: Esther B. del Brio (edelbrio@usal.es)
IP ADDRESS: 212.128.156.50
