Time-Varying Volatility in the Dynamic Nelson-Siegel Model

This thesis looks into various extensions of the Dynamic Nelson-Siegel (DNS) model that allow for time-varying volatility. A common shock component with time-varying vari- ance in the measurement equation of the state space framework greatly improves model _t. The inclusion of a second common component gives insight in how general interest rate and stock market volatility are both priced in the yield curve. The total volatility in the two component model is regressed on the VIX and a measure of general interest rate market volatility to show this for di_erent maturities. Furthermore, various GARCH- type processes are considered to account for the time-varying volatility in the yields and in the factors of the DNS model. I study asymmetric volatility extensions and allow for inuences of exogenous macroeconomic and _nancial factors in the GARCH equation. The alternative speci_cations to capture the dynamics of the common volatility turn out to improve model _t in volatile periods, but do not outperform the standard GARCH in stable times. Allowing for time-varying volatility results in predictions that signi_cantly outperform the naive random walk forecast for short maturity yields at medium and long horizons. Parsimoniousness is key when forecasting is concerned and a model with the common shock component in the state equation produces the most accurate predictions.