The article defines the practical value of applying multidimensional models of conditional volatility, i.e. the constant conditional correlations (CCC), dynamic conditional correlations (DCC) and asymmetric dynamic conditional correlations (A-DCC) models with regard to series of spot and futures yields in the Brent Crude oil market to calculate optimal hedge ratios. The author studied and compared the effectiveness of computational multidimensional models with dynamic conditional correlations by applying the indicator of hedge efficiency. Using multidimensional models of generalized autoregressive conditional heteroskedasticity (GARCH), the author calculated optimal hedge ratios taking into account the data on a non-stationary covariation matrix for spot and future prices for oil. The AR-EGARCH model describes the price dynamics, the multidimensional GARCH models with dynamic conditional correlations describe volatility and correlation. The author estimates the optimal hedge ratios, which have been calculated based on the competing models, according to the criterion of lowering variance of portfolio and risk in comparison with a non-hedged portfolio. The obtained indicators of variance and efficiency of hedging testify to the fact that the A-DCC model has the best ability to lower a portfolio variance. The author concludes that hedge efficiency increases, if asymmetric characteristics of volatility are considered. In addition, the author proposes using a dynamic ratio of optimal hedge to build a hedging strategy in the oil market on the basis of models of conditional volatility with dynamic conditional correlations. The methodology presented in this article can be adapted to the markets of other assets.
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