I estimated an MGARCH-BEKK model (using the R package BEKK
, i.e. Baba, Engle, Kraft and Kroner; see Engle and Kroner (1995)) on time series of spot and futures prices. The estimated parameters are:
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Estimate Std. Error t value Pr(> | t| )
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mu1.DLog_Base -0.002 0.001 -1.498 0.134
mu2.DLog_B3 0.0003 0.001 0.282 0.778
A011 0.004 0.003 1.047 0.295
A021 0.0004
A022 0.013 0.001 14.475 0
A11 0.008 0.027 0.314 0.754
A21 -0.096 0.089 -1.077 0.282
A12 -0.052 0.088 -0.588 0.557
A22 0.661 0.122 5.395 0.00000
B11 0.967 0.010 96.058 0
B21 0.124
B12 0.073 0.123 0.596 0.551
B22 0.011 0.185 0.058 0.953
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I don't now to calculate the conditional variance and covariance matrix.
[σssσsfσfsσff]=[c110c21c22][c110c21c22]+[a1100a22][ϵ2s,t−1ϵs,t−1ϵf,t−1ϵfs,t−1ϵs,t−1ϵ2f,t−1][a1100a22]
+[b1100b22][σss,t−1σsf,t−1σfs,t−1σff,t−1][b1100b22]
My conditional variance and covariance matrix:
[σssσsfσfsσff]=
+[0.967000.011][σss,t−1σsf,t−1σfs,t−1σff,t−1][0.967000.011]
To calculate the optimal hedge ratio BEKK
:
ht=cov(ΔSt,Δft∣Ωt−1)var(Δft∣Ωt−1)
ΔSt, Δft is the return price spot and future, and Ωt−1 is conditional variance and covariance matrix.
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