🤖 AI Summary
This study addresses a key limitation of traditional time series scenario generation methods, which often produce interest rate curve paths lacking economic plausibility despite matching historical return distributions. To overcome this, the authors propose a novel framework that integrates parametric term structure models with a semi-parametric bootstrap approach. The method preserves the dynamic structure of the underlying model while resampling residuals and incorporates autoregressive or mean-reverting specifications to enhance temporal coherence of generated paths. Empirical results demonstrate that, in fixed-income applications, the proposed approach significantly outperforms conventional nonparametric techniques by simultaneously reproducing the statistical properties of yield curves accurately and generating scenarios that align with economic reasoning.
📝 Abstract
Generating stochastic trajectories for asset classes is an increasingly relevant task in quantitative finance. Traditional approaches, such as the stationary bootstrap, preserve by construction the empirical distribution of asset-class returns, but do not ensure that each individual simulated path is economically realistic: scenarios may be valid in distribution while single trajectories fail to represent plausible states of the world. To address this limitation, we review semiparametric simulation methodologies that combine a parametric structure, which enforces realistic dynamics, with the resampling of model residuals, which preserves the stochastic component observed in historical data. The issue is particularly acute for interest rates, where direct resampling of rate changes may produce implausible yield-curve evolutions despite correct distributional properties. Our empirical analysis shows the effectiveness of semiparametric bootstrap methods based on autoregressive or mean-reverting specifications. In the fixed-income setting, combining these methods with fully parametric term-structure models yields more coherent and realistic simulations of yield-curve dynamics.