Analyzing interest rate risk: stochastic volatility in the term structure of government bond yields

We propose a Nelson-Siegel type interest rate term structure model where the underlying yield factors follow autoregressive processes with stochastic volatility. The factor volatilities parsimoniously capture risk inherent to the term structure and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the factor volatilities follow highly persistent processes. We show that slope and curvature risk have explanatory power for bond excess returns and illustrate that the yield and volatility factors are closely related to industrial capacity utilization, inflation, monetary policy and employment growth. JEL Classification: C5, E4, G

Analyzing Interest Rate Risk: Stochastic Volatility in the Term Structure of Government Bond Yields

We propose a Nelson-Siegel type interest rate term structure model where the underlying yield factors follow autoregressive processes with stochastic volatility. The factor volatilities parsimoniously capture risk inherent to the term structure and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the factor volatilities follow highly persistent processes. We show that slope and curvature risk have explanatory power for bond excess returns and illustrate that the yield and volatility factors are closely related to industrial capacity utilization, inflation, monetary policy and employment growth.Term Structure Modelling, Yield Curve Risk, Stochastic Volatility, Factor Models, Macroeconomic Fundamentals

Discrete-Time Stochastic Volatility Models and MCMC-Based Statistical Inference

In this paper, we review the most common specifications of discrete-time stochas- tic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap- proach which is easily implemented in empirical applications and financial practice and can be straightforwardly extended in various directions. We illustrate empirical results based on different SV specifications using returns on stock indices and foreign exchange rates.Stochastic Volatility, Markov Chain Monte Carlo, Metropolis-Hastings al- Jump Processes

Yield Curve Factors, Term Structure Volatility, and Bond Risk Premia

We introduce a Nelson-Siegel type interest rate term structure model with the underlying yield factors following autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term struc- ture and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the yield factors and factor volatilities follow highly persistent processes. Using the extracted factors to explain one-year-ahead bond excess returns we observe that the slope and cur- vature yield factors contain the same explanatory power as the return-forecasting factor recently proposed by Cochrane and Piazzesi (2005). Moreover, we identify slope and curvature risk as important additional determinants of future excess returns. Finally, we illustrate that the yield and volatility factors are closely con- nected to variables reflecting macroeconomic activity, inflation, monetary policy and employment growth. It is shown that the extracted yield curve components have long-term prediction power for macroeconomic fundamentals.Term Structure Modelling; Yield Curve Risk; Stochastic Volatility; Factor Models; Macroeconomic Fundamentals

Yield Curve Factors, Term Structure Volatility, and Bond Risk Premia

We introduce a Nelson-Siegel type interest rate term structure model with the underlying yield factors following autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term struc- ture and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the yield factors and factor volatilities follow highly persistent processes. Using the extracted factors to explain one-year-ahead bond excess returns we observe that the slope and cur- vature yield factors contain the same explanatory power as the return-forecasting factor recently proposed by Cochrane and Piazzesi (2005). Moreover, we identify slope and curvature risk as important additional determinants of future excess returns. Finally, we illustrate that the yield and volatility factors are closely con- nected to variables reflecting macroeconomic activity, inflation, monetary policy and employment growth. It is shown that the extracted yield curve components have long-term prediction power for macroeconomic fundamentals

Discrete-Time Stochastic Volatility Models and MCMC-Based Statistical Inference

In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical applications and financial practice and can be straightforwardly extended in various directions. We illustrate empirical results based on different SV specifications using returns on stock indices and foreign exchange rates