TY - JOUR
T1 - A new approach for using lévy processes for determining high-frequency value-at-risk predictions
AU - Sun, Wei
AU - Rachev, Svetlozar
AU - Fabozzi, Frank J.
PY - 2009/3
Y1 - 2009/3
N2 - A new approach for using Lévy processes to compute value-at-risk (VaR) using high-frequency data is presented in this paper. The approach is a parametric model using an ARMA(1,1)-GARCH(1,1) model where the tail events are modelled using fractional Lévy stable noise and Lévy stable distribution. Using high-frequency data for the German DAX Index, the VaR estimates from this approach are compared to those of a standard nonparametric estimation method that captures the empirical distribution function, and with models where tail events are modelled using Gaussian distribution and fractional Gaussian noise. The results suggest that the proposed parametric approach yields superior predictive performance.
AB - A new approach for using Lévy processes to compute value-at-risk (VaR) using high-frequency data is presented in this paper. The approach is a parametric model using an ARMA(1,1)-GARCH(1,1) model where the tail events are modelled using fractional Lévy stable noise and Lévy stable distribution. Using high-frequency data for the German DAX Index, the VaR estimates from this approach are compared to those of a standard nonparametric estimation method that captures the empirical distribution function, and with models where tail events are modelled using Gaussian distribution and fractional Gaussian noise. The results suggest that the proposed parametric approach yields superior predictive performance.
KW - Fractional Gaussian noise
KW - Fractional Lévy stable noise
KW - High-frequency data
KW - Lévy stable distribution
KW - Value-at-risk
UR - http://www.scopus.com/inward/record.url?scp=61849097252&partnerID=8YFLogxK
U2 - 10.1111/j.1468-036X.2008.00467.x
DO - 10.1111/j.1468-036X.2008.00467.x
M3 - Article
AN - SCOPUS:61849097252
SN - 1354-7798
VL - 15
SP - 340
EP - 361
JO - European Financial Management
JF - European Financial Management
IS - 2
ER -