Original scientific paper
https://doi.org/10.1080/1331677X.2022.2136228
Can jumps improve the futures margin level? An empirical study based on an SE-SVCJ-GPD model
Yan Chen
Lei Zhang
Abstract
In addition to the characteristics of leptokurtic fat-tailed distribution,
financial sequences also exhibit typical volatility and jumps.
Moreover, jumps exhibit self-exciting and clustering characteristics
under extreme events. However, studies on dynamic margin levels
often ignore jumps. In this study, we combine the self-exciting
stochastic volatility with correlated jumps (SE-SVCJ) model with a
generalized Pareto distribution (GPD) to measure the optimal
margin level for the stock index futures market. Value at risk (VaR)
is estimated and forecasted using the SE-SVCJ-GPD, SVCJ-GPD,
and generalized autoregressive conditional heteroskedasticity with
GPD (GARCH-GPD) models. SE-SVCJ-GPD can undertake more risks
in the long or short trading position of stock index futures contracts.
Moreover, the backtesting experiment results show that
the SE-SVCJ-GPD model provides a more accurate margin level
forecast than the other methods in both positions. This study’s
findings have practical significance and theoretical value for
assessing the level of risk and taking corresponding risk-prevention
measures.
Keywords
Futures margin level; jump; self-exciting; generalized Pareto distribution; value at risk
Hrčak ID:
306698
URI
Publication date:
30.4.2023.
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