Title: Bond portfolio optimization in turbulent times: A dynamic Nelson-Siegel approach with Wishart stochastic volatility
Authors: Richard Schnorrenberger - Kiel University (Germany) [presenting]
Abstract: Modeling and forecasting the time-varying volatility of bond yields play a prominent role in many finance applications. However, amid periods of financial turmoil, managing interest rate risk on a daily basis is rather a challenging task due to extreme realizations and sudden changes in bond yields that can easily lead to implausible density forecasts. To reduce forecasting uncertainty and account for structural instability in volatile bond markets, the predictive performance of yield curve factor models with time-varying VAR parameters and Wishart stochastic volatility is investigated under a Bayesian MCMC scheme. A bond portfolio optimization and Value-at-Risk forecasting application to daily US Treasury yields also highlight the potential of modeling frameworks with factor Wishart stochastic volatility. The results clearly indicate that the proposed modeling features are economically motivated due to their outperformance in terms of portfolio allocation and risk management during turbulent times including the Great Recession and COVID-19 pandemic.