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Dynamic nelson siegel excel3/11/2024 The most difficult part of the estimation of NSS model is how to choose \(\lambda_1\) and \(\lambda_2\). Restrictions on \(\lambda_1\) and \(\lambda_2\) For this reason too small \(\lambda_2\) is not appropriate and is needed to be constrained by some reasonable upper and lower bounds. Smaller \(\lambda_2\) fits the yield curve at longer maturities well but it lowers the interpretability of the level factor. In the above figure, I use \(\lambda_1 = 0.0609\) and \(\lambda_2 = 0.01 \), which represent the maximum of curvature loadings are attained at nearly 30-month and 180-month respectively. \(\lambda_1\) and \(\lambda_2\) determine the shapes of slope and two curvature factor loadings as follows. \(\lambda_1\) and \(\lambda_2 \) are the decay parameters. \(\beta_1, \beta_2, \beta_3\, \beta_4\) are coefficient parameters. Here, \(\tau\) is a maturity and \(y(\tau)\) is a continuously compounded spot rate with \(\tau\) maturity. Nelson-Siegel model is a non-linear least square problem with 6 parameters with some inequality constraints. Bayesian Estimation by using rjags R Package.Excel Solver using VBA macro : Nelson-Siegel yield curve fitting.Non-linear Optimization of Nelson-Siegel model using nloptr R package.Non-linear Optimization by using R function.
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