Revisiting Shiller’s excess volatility hypothesis

One of the cornerstone of financial anomalies is that there exists money making opportunities. Shiller’s excess volatility theory is re-investigated from the perspective of a trading strategy where the present value is computed using a series of simple econometric models to forecast the present value. The results show that the excess volatility may not be exploited given the data available until time t. However, when learning is introduced empirically, the simple trading strategy may offer profits, but which are likely to disappear once transaction costs are considered.

Co-Movement, Spillovers and Excess Returns in Global Bond Markets

This paper investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in bond yields of seven advanced economies is due to global co-movement, which is mainly attributed to shocks to non-fundamentals. Global fundamentals, especially global inflation, affect yields through a ‘policy channel’ and a ‘risk compensation channel’, but the effects through two channels are offset. This evidence explains the unsatisfactory performance of fundamentals-driven term structure models. Our approach delineates asymmetric spillovers in global bond markets connected to diverging monetary policies. The proposed model is robust as identified factors has significant explanatory power of excess returns. The finding that global inflation uncertainty is useful in explaining realized excess returns does not rule out regime changing as a source of non-fundamental fluctuations.

Revisiting Shiller’s excess volatility hypothesis

One of the cornerstone of financial anomalies is that there exists money making opportunities. Shiller’s excess volatility theory is re-investigated from the perspective of a trading strategy where the present value is computed using a series of simple econometric models to forecast the present value. The results show that the excess volatility may not be exploited given the data available until time t. However, when learning is introduced empirically, the simple trading strategy may offer profits, but which are likely to disappear once transaction costs are considered.