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3

Suppose that you are riskless asset with return $r_{ft}$ and a risky asset with return $r_t$ and conditional volatility $\sigma_t(r_t) := \sqrt{V_t(r_t)}$. We build a portfolio using weights $(w_1, w_2) \in \mathbb{R}$, or as you wrote it $w_t := w_{1t}$, $w_{2t} := 1 - w_t$. This portfolio will have a time $t$ return of $r_{pt}$. Its volatility is given by $...


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I have just found out what the code is. Note that theta and delta come from the copula selected for you through the BiCopSelect() command. theta <- selectedCopula$par delta <- selectedCopula$par2 gfBB1 <- gofCopula(BB1Copula(c(theta, delta)), as.matrix(mydata), N = 50)


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Note that Boyle (1988) introduces $\lambda$ because the CRR parameterisation $u=e^{\sigma\sqrt{h}}$ yielded negative probabilities (and probabilities above one) for reasonable parameter values. Instead, he uses $u=e^{\lambda\sigma\sqrt{h}}$, where $\lambda>1$ and $h=\frac{T}{n}$ is the length of one time step. If you perform the limits, $p_u\to0$ and $...


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