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This study aims to better understand the effects of speeches as a valuable communication tool for central banks. It extends the analysis of the effects of public speeches on jumps to determine whether individual speakers matter partly because of their name, position or institution.

This study detects intraday jumps using a robust-to-jump volatility estimator that accounts for deterministic seasonality. As a result, this study removes spurious jumps that occur when volatility is high and consider the relatively small jumps that occur when volatility is low. After identifying jumps, this study examines their reactions to senior official speeches and macroeconomic news surrounding the US and European Union (EU) financial crises.

Despite having the most influential individual speakers, this study finds that the impact of the Federal Reserve (Fed) and European Central Bank (ECB) is mitigated because the two institutions have a relatively small impact on currency jumps. This finding shows that the speaker’s name is more important than his or her institution affiliation. While the Federal Reserve Bank President and Chief Executive, as well as ECB board members, significantly reduce jump sizes, particularly during the EU crisis period, both the Fed Chairman and the ECB President increase the magnitude of the jump in both the US crisis and noncrisis periods, contributing to market instability.

The implications of the results include international portfolio management, currency derivatives pricing and hedging, risk management and market efficiency.

The findings contribute to a better understanding of the effects of senior official speech attributes on currency jumps in various economic states. The results raise questions about the speaker’s name, institution and position’s effectiveness in calming markets and reducing uncertainty.

Forecasting the future movement of yield curves contains valuable information for both academic and practical issues such as bonding pricing, portfolio management, and government policies. The purpose of this paper is to develop a dynamic factor approach that can provide more precise and consistent forecasting results under various yield curve dynamics.

The paper develops a unified dynamic factor model based on Diebold and Li (2006) and Nelson and Siegel (1987) three-factor model to forecast the future movement yield curves. The authors apply the state-space model and the Kalman filter to estimate parameters and extract factors from the US yield curve data.

The authors compare both in-sample and out-of-sample performance of the dynamic approach with various existing models in the literature, and find that the dynamic factor model produces the best in-sample fit, and it dominates existing models in medium- and long-horizon yield curve forecasting performance.

The authors find that the dynamic factor model and the Kalman filter technique should be used with caution when forecasting short maturity yields on a short time horizon, in which the Kalman filter is prone to trade off out-of-sample robustness to maintain its in-sample efficiency.

Bond analysts and portfolio managers can use the dynamic approach to do a more accurate forecast of yield curve movements.

The enhanced forecasting approach also equips the government with a valuable tool in setting macroeconomic policies.

The dynamic factor approach is original in capturing the level, slope, and curvature of yield curves in that the decay rate is set as a free parameter to be estimated from yield curve data, instead of setting it to be a fixed rate as in the existing literature. The difference range of estimated decay rate provides richer yield curve dynamics and is the key to stronger forecasting performance.