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. Financial Research Journal, 10(25), -.
MLA
. "Forecasting Value-at-Risk Using Conditional Volatility Models: Evidence from Tehran Stock Exchange", Financial Research Journal, 10, 25, 2008, -.
HARVARD
(2008). 'Forecasting Value-at-Risk Using Conditional Volatility Models: Evidence from Tehran Stock Exchange', Financial Research Journal, 10(25), pp. -.
VANCOUVER
Forecasting Value-at-Risk Using Conditional Volatility Models: Evidence from Tehran Stock Exchange. Financial Research Journal, 2008; 10(25): -.
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