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We use 70 years of international data from the major bond markets to examine bond return predictability through in-sample and out-of-sample tests. Our results reveal economically strong and statistically significant bond return predictability. This finding is robust over markets and time periods, including 30 years of out-of-sample data, prolonged periods of rising or falling rates, and a dataset of nine additional countries. Furthermore, the results are not explained by market or macroeconomic risks, nor can they be easily attributed to transaction costs or other investment frictions. These results reveal predictable dynamics in government bond returns relevant for academics and practitioners.

We use 70 years of international data from the major bond markets to examine bond return predictability through in-sample and out-of-sample tests. Our results reveal economically strong and statistically significant bond return predictability. This finding is robust over markets and time periods, including 30 years of out-of-sample data, prolonged periods of rising or falling rates, and a dataset of nine additional countries. Furthermore, the results are not explained by market or macroeconomic risks, nor can they be easily attributed to transaction costs or other investment frictions. These results reveal predictable dynamics in government bond returns relevant for academics and practitioners.

This paper is closed access until 23 November 2020. ; This paper aims to provide empirical evidence to the theoretical claim that rare disaster risks affect government bond market movements. Using a nonparametric quantiles-based methodology, we show that rare disaster-risks affect only volatility, but not returns, of 10-year government bond of the United States over the monthly period of 1918:01 to 2013:12. In addition, the predictability of volatility holds for the majority of the conditional distribution of the volatility, with the exception of the extreme ends. Moreover, in general, similar results are also obtained for long-term government bonds of an alternative developed country (UK) and an emerging market (South Africa).

AbstractThis paper aims to provide empirical evidence to the theoretical claim that rare disaster risks affect government bond market movements. Using a nonparametric quantiles‐based methodology, we show that rare disaster‐risks affect only volatility, but not returns, of 10‐year government bond of the United States over the monthly period of 1918:01 to 2013:12. In addition, the predictability of volatility holds for the majority of the conditional distribution of the volatility, with the exception of the extreme ends. Moreover, in general, similar results are also obtained for long‐term government bonds of an alternative developed country (UK) and an emerging market (South Africa).

We conduct in-sample and out-of-sample forecasting using the new approach of combining explanatory variables through complete subset regressions (CSR). We predict bond CAPM betas and bond returns conditioning on various macro-fi…nance variables. We explore differences across long-term government bonds, investment grade corporate bonds, and high-yield corporate bonds. The CSR method performs well in predicting bond betas, especially in-sample, and, mainly high-yield bond betas when the focus is out-of-sample. Bond returns are less predictable than bond betas.

ABSTRACTThis paper uses two recently developed tests to identify neglected nonlinearity in the relationship between excess returns on four asset classes and several economic and financial variables. Having found some evidence of possible nonlinearity, it was then investigated whether the predictive power of these variables could be enhanced by using neural network models instead of linear regression or GARCH models.Some evidence of nonlinearity in the relationships between the explanatory variables and large stocks and corporate bonds was found. It was also found that the GARCH models are conditionally efficient with respect to neural network models, but the neural network models outperform GARCH models if financial performance measures are used. In resonance with the results reported for the tests for neglected nonlinearity, it was found that the neural network forecasts are conditionally efficient with respect to linear regression models for large stocks and corporate bonds, whereas the evidence is not statistically significant for small stocks and intermediate‐term government bonds. This difference persists even when financial performance measures for individual asset classes are used for comparison.

"We examine empirically how the maturity structure of government debt affects bond yields and excess returns. Our analysis is based on a theoretical model of preferred habitat in which clienteles with strong preferences for specific maturities trade with arbitrageurs. Consistent with the model, we find that (i) the supply of long- relative to short-term bonds is positively related to the term spread, (ii) supply predicts positively long-term bonds' excess returns even after controlling for the term spread and the Cochrane-Piazzesi factor, (iii) the effects of supply are stronger for longer maturities, and (iv) following periods when arbitrageurs have lost money, both supply and the term spread are stronger predictors of excess returns"--National Bureau of Economic Research web site