In this paper, we investigate the performance of parametric ARCH
class models to forecast out-of-sample VaR for two portfolios of
Tehran Stock Exchange (TSE) companies (Market portfolio and a
portfolio of 50 liquid companies), using a number of distributional
assumptions and sample sizes at low and high confidence levels. We
find, first, that leptokurtic distributions are able to produce better oneday-
ahead and 10-day-ahead VaR forecasts; second, the choice of
sample size is important for the accuracy of the forecasts.
. (2008). Forecasting Value-at-Risk Using Conditional Volatility Models: Evidence from Tehran Stock Exchange. (e27748). Financial Research Journal, 10(25), e27748
MLA
. "Forecasting Value-at-Risk Using Conditional Volatility Models: Evidence from Tehran Stock Exchange" .e27748 , Financial Research Journal, 10, 25, 2008, e27748.
HARVARD
. (2008). 'Forecasting Value-at-Risk Using Conditional Volatility Models: Evidence from Tehran Stock Exchange', Financial Research Journal, 10(25), e27748.
CHICAGO
, "Forecasting Value-at-Risk Using Conditional Volatility Models: Evidence from Tehran Stock Exchange," Financial Research Journal, 10 25 (2008): e27748,
VANCOUVER
. Forecasting Value-at-Risk Using Conditional Volatility Models: Evidence from Tehran Stock Exchange. FRJ. 2008;10(25):e27748 (In Persian).
