TY - GEN
T1 - A general framework of importance sampling for value-at-risk and conditional value-at-risk
AU - Sun, Lihua
AU - Hong, L. Jeff
PY - 2009
Y1 - 2009
N2 - Value-at-risk (VaR) and conditional value-at-risk (CVaR) are important risk measures. Importance sampling (IS) is often used to estimate them. We derive the asymptotic representations for IS estimators of VaR and CVaR. Based on these representations, we are able to give simple conditions under which the IS estimators have smaller asymptotic variances than the ordinal estimators. We show that the exponential twisting can yield an IS distribution that satisfies the conditions for both the IS estimators of VaR and CVaR. Therefore, we may be able to estimate VaR and CVaR accurately at the same time.
AB - Value-at-risk (VaR) and conditional value-at-risk (CVaR) are important risk measures. Importance sampling (IS) is often used to estimate them. We derive the asymptotic representations for IS estimators of VaR and CVaR. Based on these representations, we are able to give simple conditions under which the IS estimators have smaller asymptotic variances than the ordinal estimators. We show that the exponential twisting can yield an IS distribution that satisfies the conditions for both the IS estimators of VaR and CVaR. Therefore, we may be able to estimate VaR and CVaR accurately at the same time.
UR - https://www.scopus.com/pages/publications/77951612021
UR - https://www.scopus.com/pages/publications/77951612021#tab=citedBy
U2 - 10.1109/WSC.2009.5429348
DO - 10.1109/WSC.2009.5429348
M3 - Conference contribution
AN - SCOPUS:77951612021
SN - 9781424457700
T3 - Proceedings - Winter Simulation Conference
SP - 415
EP - 422
BT - Proceedings of the 2009 Winter Simulation Conference, WSC 2009
T2 - 2009 Winter Simulation Conference, WSC 2009
Y2 - 13 December 2009 through 16 December 2009
ER -