Are the dynamic linkages between the macroeconomy and asset prices time-varying?

We estimate a number of multivariate regime switching VAR models on a long monthly data set for eight variables that include excess stock and bond returns, the real T-bill yield, predictors used in the finance literature (default spread and the dividend yield), and three macroeconomic variables (inflation, real industrial production growth, and a measure of real money growth). Heteroskedasticity may be accounted for by making the covariance matrix a function of the regime. We find evidence of four regimes and of time-varying covariances. We provide evidence that the best in-sample fit is provided by a four state model in which the VAR(1) component fails to be regime-dependent. We interpret this as evidence that the dynamic linkages between financial markets and the macroeconomy have been stable over time. We show that the four-state model can be helpful in forecasting applications and to provide one-step ahead predicted Sharpe ratios.Macroeconomics ; Asset pricing

Option Pricing under Stochastic Volatility and Trading Volume

This paper presents a pricing formula for European options derived from a model in which changes in the underlying price and trading volumes are jointly determined by exogenous events. This specification makes increments to the volatility depend on the current level of volatility and news and thereby accounts for the observed persistence in volatility. Moreover, it makes volatility an observable variable. The model accounts well for time varying volatility smiles and term structures, and that out-of-sample price forecasts for a sample of call options are superior to the benchmark ad hoc procedure of plugging implicit volatilities into the Black-Scholes formula.Option valuation, trading volume, the stochastic volatility and volume (SVV) model

Non-linear predictability in stock and bond returns: when and where is it exploitable?

We systematically examine the comparative predictive performance of a number of alternative linear and non-linear models for stock and bond returns in the G7 countries. Besides Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STAR) regime switching (predictive) regression models, we also estimate univariate models in which conditional heteroskedasticity is captured through GARCH, TARCH and EGARCH models and ARCH-in mean effects appear in the conditional mean. Although we fail to find a consistent winner/out-performer across all countries and asset markets, it turns out that capturing non-linear effects is of extreme importance to improve forecasting performance. U.S. and U.K. asset return data are “special” in the sense that good predictive performance seems to loudly ask for models that capture non linear dynamics, especially of the Markov switching type. Although occasionally also stock and bond return forecasts for other G7 countries appear to benefit from non-linear modeling (especially of TAR and STAR type), data from France, Germany, and Italy express interesting predictive results on the basis of simpler benchmarks. U.S. and U.K. data are also the only two data sets in which we find statistically significant differences between forecasting models. Results appear to be remarkably stable over time, and robust to the specification of the loss function used in statistical evaluations as well as to the methodology employed to perform pairwise comparisons.Group of Seven countries ; Financial markets

Does the macroeconomy predict UK asset returns in an nonlinear fashion? Comprehensive out-of-sample evidence

We perform a comprehensive examination of the recursive, comparative predictive performance of a number of linear and non-linear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STR) regime switching models, and a range of linear specifications in addition to univariate models in which conditional heteroskedasticity is captured by GARCH type specifications and in which predicted volatilities appear in the conditional mean. The results demonstrate that U.K. asset returns require non-linear dynamics be modeled. In particular, the evidence in favour of adopting a Markov switching framework is strong. Our results appear robust to the choice of sample period, changes in the adopted loss function and to the methodology employed to test the null hypothesis of equal predictive accuracy across competing models