Smooth Transition Vector Autoregressive Research Articles

The asymmetric effects of oil price and monetary policy shocks: A nonlinear VAR approach

In this paper we investigate the asymmetric effects of oil price shocks and monetary policy on macroeconomic activity, using monthly data for the United States, over the period from 1983:1 to 2008:12. In doing so, we use a logistic smooth transition vector autoregression (VAR), as detailed in Terasvirta and Anderson (1992) and Weise (1999), and make a distinction between two oil price volatility regimes (high and low), using the realized oil price volatility as a switching variable. We isolate the effects of oil price and monetary policy shocks and their asymmetry on output growth and, following Koop et al. (1996) and Weise (1999), we employ simulation methods to calculate Generalized Impulse Response Functions (GIRFs) to trace the effects of independent shocks on the conditional means of the variables. Our results suggest that in addition to the price of oil, oil price volatility has an impact on macroeconomic activity and that monetary policy is not only reinforcing the effects of oil price shocks on output, it is also contributing to the asymmetric response of output to oil price shocks.

Forecasting interest rate swap spreads using domestic and international risk factors: evidence from linear and non‐linear models

AbstractThis paper explores the ability of factor models to predict the dynamics of US and UK interest rate swap spreads within a linear and a non‐linear framework. We reject linearity for the US and UK swap spreads in favour of a regime‐switching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. We compare the ability of the STVAR model to predict swap spreads with that of a non‐linear nearest‐neighbours model as well as that of linear AR and VAR models. We find some evidence that the non‐linear models predict better than the linear ones. At short horizons, the nearest‐neighbours (NN) model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions. At long horizons, the STVAR model increases its forecasting ability over the linear models, whereas the NN model does not outperform the rest of the models. Copyright © 2007 John Wiley & Sons, Ltd.

Determinants of Japanese Yen interest rate swap spreads: Evidence from a smooth transition vector autoregressive model

AbstractThis study investigates the determinants of variations in the yield spreads between Japanese yen interest rate swaps and Japan government bonds for a period from 1997 to 2005. A smooth transition vector autoregressive (STVAR) model and generalized impulse response functions are used to analyze the impact of various economic shocks on swap spreads. The volatility based on a GARCH (generalized autoregressive conditional heteroskedasticity) model of the government bond rate is identified as the transition variable that controls the smooth transition from a high volatility regime to a low volatility regime. The break point of the regime shift occurs around the end of the Japanese banking crisis. The impact of economic shocks on swap spreads varies across the maturity of swap spreads as well as regimes. Overall, swap spreads are more responsive to the economic shocks in the high volatility regime. Moreover, a volatility shock has profound effects on shorter maturity spreads, whereas the term structure shock plays an important role in impacting longer maturity spreads. Results of this study also show noticeable differences between the nonlinear and linear impulse response functions. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:82–107, 2008