Using a unique data set of individual professional forecasts, we document disagreement about the future path of monetary policy, particularly at longer horizons. The stark differences in short rate forecasts imply strong disagreement about the risk-return trade-off of longer-term bonds. Longerhorizon short rate disagreement co-moves with term premiums. We estimate an affine term structure model in which investors hold heterogeneous beliefs about the long-run level of rates. Our model fits U.S. Treasury yields and the short rate paths predicted by different groups of professional forecasters very well. About one-third of the variation in term premiums is driven by short rate disagreement.
In this paper we model and predict the term structure of US interest rates in a data-rich and unstable environment. The dynamic Nelson-Siegel factor model is extended to allow the model dimension and the parameters to change over time, in order to account for both model uncertainty and sudden structural changes, in one setting. The proposed specification performs better than several alternatives, since it incorporates additional macrofinance information during hard times, while it allows for more parsimonious models to be relevant during normal periods. A dynamic variance decomposition measure constructed from our model shows that parameter uncertainty and model uncertainty regarding different choices of predictors explain a large proportion of the predictive variance of bond yields.
In this paper we study the exchange rate predictability across a range of investment horizons by proposing a generalized (term structure) model to capture the risk premium component of exchange rates with a broad set of variables meanwhile handle both parameter and model uncertainty. We demonstrate the existence of time-varying term-structural effect and model disagreement effect of exchange rate predictors as well as the projections of predictive information over the term structure. We further utilize the time-variation in the probability weighting to identify the scapegoat drivers of customer order flows. Our findings suggest that heterogeneous agents learn to forecast exchange rates and switch trading rules over time, resulting in the dynamic country-specific and global exposures of exchange rates to shortrun non-fundamental risk and long-run business cycle risk. Hedging pressure and liquidity are identified to contain predictive information that is common to a range of forecasting horizons. Policy-related predictors are important for short-run forecasts up to 3 months while crash risk indicators matter for long-run forecasts from 9 months to 12 months. We further comprehensively evaluate both statistical and economic significance of the model allowing for a full spectrum of currency investment management, and find that the model generates substantial performance fees of 6.5% per annum. The outperformance is mainly due to (i) the relaxing of restrictions imposed on structural parameters via model generalization, and (ii) the use of factor structure to extract common useful information from noisy data and reduce estimation errors.
This paper explains the co-movement of global yield curve dynamics using a Bayesian hierarchical factor model augmented with macro fundamentals. Our novel modeling approach reveals the relative importance of global shocks through two transmission channels: the policy and risk channels. Global inflation is the most important traditional macro fundamentals for international yields and operates through a policy channel. Economic uncertainty and sentiment are also important in driving global yield co-movements, through a risk channel.
This paper explains the co-movement of global yield curve dynamics using a Bayesian hierarchical factor model augmented with macro fundamentals. Our novel modeling approach reveals the relative importance of global shocks through two transmission channels: the policy and risk channels. Global inflation is the most important traditional macro fundamentals for international yields and operates through a policy channel. Economic uncertainty and sentiment are also important in driving global yield co-movements, through a risk channel.
Many collections of documents, manuscripts, and works of art on paper are prone to degradation due to a complex interplay of extrinsic and intrinsic factors. The aim of this study was to examine the simultaneous effect of multiple degradation agents on selected non-model types of paper in natural environments, and the relative effect of environmental parameters (heat, humidity, light and pollution) compared to material parameters (pH, fibre composition and presence of additives). An exposure experiment was set up to investigate visual and chemical changes of 12 different types of paper in real time in different environmental conditions over a 1.5-year period at 11 sites across Europe and North Africa, sheltered from UV light and precipitation. Suitable environmental monitoring equipment, such as data loggers and gas samplers, and analytical methods to characterise sample degradation, specifically spectrocolorimetry and capillary viscometry, were used to estimate alterations in visual appearance and degree of cellulose polymerisation, which are the most important properties of paper in the heritage context. Multiple linear regression and principal component regression were used to interpret the large volume of data and calculate a set of dose-response functions. The results of this study not only suggested that most of the considered variables are of significance in relation to changes in colour and in average molecular weight, but also revealed a number of meaningful interactions between these variables. Based on the assessment of the relative contributions of environmental and material-related variables to the natural ageing processes of paper, the dose-response functions proposed in this study enable prioritisation of degradation factors in environmental management of paper-based collections and in historic paper degradation studies; however, further work is required to increase accuracy and understanding of the chemistry of degradation.
This paper identifies five factors that can capture 95% of the variance across 39 US dollar exchange rates based on the principal component method. A time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model is used to analyze the determinants of movements in these exchange rates, revealing that their impact on global oil prices and the People's Republic of China's growth has increased significantly since 2008. In particular, the variance of US dollar exchange rates has mainly been driven by these two shocks in recent years. The impact of monetary policy shocks on the currency pairs is comparatively small.
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