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This chapter examines the impacts of scheduled announcements of 14 widely followed macroeconomic news on the corporate bond market from July 2002 to June 2017 and documents several new findings. First, good (bad) macroeconomic news tends to have a negative (positive) effect on IG bond returns and a positive (negative) effect on high-yield (HY) bond returns. Second, nonfarm payroll (NFP) appears to be the “King of announcements” for the corporate bond market. Third, while information about revisions of prior releases is incorporated into bond prices on announcement days, future revisions fail to be priced in. Fourth, the news information is thoroughly and quickly reflected in bond prices on the announcement day. Finally, corporate bond volatility increases on announcement days, whereas the Zero Lower Bound (ZLB) policy has little effect on conditional volatility.

This study examines the response of Australian interest rate swap spreads to the arrival of macroeconomic news information during the economic expansion and contraction periods. We find that the impact of news announcements on swap spread change differs and largely depends on the state of the economy. The unexpected inflation rate is the only news released that has significant impact on swap spreads across all maturities during contractions and remains the important news announcement throughout the business cycles, while the unanticipated unemployment rate tends to be more relevant to 10-year swap and the unanticipated change in money supply tends to be more relevant to 4- and 7-year swaps during expansions. We also find shocks from these news surprises appear to have significant impact on the conditional volatility of the swap spread change during both economic phases. The macroeconomic shocks in general are negatively related to the conditional volatility of the swap spread change, suggesting that the newsworthy announcements tend to reduce uncertainty on the news announcement days in the swap market during expansion and contraction periods.

We highlight two mechanisms of limited attention for expert information intermediaries, i.e., analysts, and the effects of such limited attention on the market price discovery process. We approach analysts' limited attention from the perspective of day-to-day arrival of information and processing of tasks. We examine the attention-limiting role of competing tasks (number of earnings announcements and forecasts for portfolio firms) and distracting events (number of earnings announcements for non-portfolio firms) in analysts' forecast accuracy and the effects of such, on the subsequent price discovery process. Our results show that competing tasks worsen analysts' forecast accuracy, and competing task induced limited attention delays the market price adjustment process. On the other hand, distracting events can improve analysts' forecast accuracy and accelerate market price adjustments when such events relate to analysts' portfolio firms through industry memberships.

This chapter investigates the impact of policy interest rate news from the U.S. Federal Reserve (Fed) and the European Central Bank (ECB) on stock returns and volatilities of U.S. NYSE and German DAX listed commercial banks. We find that Fed news has the most influence on both U.S. and German listed bank stocks and an unexpected policy rate increase (decrease) lowers (raises) returns and raises volatility in the majority of cases. On the other hand, ECB news generally increases bank stock volatility in the United States but has little impact within its own domestic banking industry. While our results for the U.S. listed banks confirm that their stock prices are more responsive in bad economic times and also during periods of monetary tightening, we find disparities for German banks suggesting that U.S. and European banking industries respond heterogeneously to monetary policy news but the Global Financial Crisis increased the sensitivity of all banks to monetary policy news.

We examine the informational roles of trades and time between trades in the domestic and overseas US Treasury markets. A vector autoregressive model is employed to assess the information content of trades and time duration between trades. We find significant impacts of trades and time duration between trades on price changes. Larger trade size induces greater price revision and return volatility, and higher trading intensity is associated with a greater price impact of trades, a faster price adjustment to new information and higher volatility. Higher informed trading and lower liquidity contribute to larger bid–ask spreads off the regular daytime trading period.

This paper compares the out-of-sample forecasting performance of three long-memory volatility models (i.e., fractionally integrated (FI), break and regime switching) against three short-memory models (i.e., GARCH, GJR and volatility component). Using S&P 500 returns, we find that structural break models produced the best out-of-sample forecasts, if future volatility breaks are known. Without knowing the future breaks, GJR models produced the best short-horizon forecasts and FI models dominated for volatility forecasts of 10 days and beyond. The results suggest that S&P 500 volatility is non-stationary at least in some time periods. Controlling for extreme events (e.g., the 1987 crash) significantly improved forecasting performance.

This commentary is a reflective discussion of how to use simple social media tools in college-level writing courses, and contains research elements such as effective examples of what is attainable and possible when incorporating blogs (e.g., Posterous) and Twitter in the college classroom. In order to do this, it uses reflective writing with a focus on failures/successes in past courses, and also incorporates students' own comments on blogging and Twitter. The chapter's findings include the following: The overall ease of use and relative simplicity of certain social media tools make for low barriers of entry for a majority of students. The mobile accessibility of these online communicative technologies should also be of specific appeal. These characteristics should encourage student participation in ways that content management systems like Blackboard do not. The convenience of and allowance for quick and easy sharing of information via blogging and microblogging can also mean that each is often quicker than email for contacting someone. What makes both better than Blackboard concerns how they, when taken together, sustain class discussion, keeping it alive, present, and continuous. If proper affordances are made in terms of framing and timing, social media can make for successful additions to college-level courses. Simple tools allow and encourage students to document and reflect on their own learning in ways that are meaningful and unique as they are.

In this chapter we investigate the response of bond markets to macroeconomic news announcements in the euro area. Specifically, we analyze the impact of (un)expected changes in the interest rate, unemployment rate, consumer confidence index and industrial production index on the returns, volatility and correlations of European government bond markets. Overall, our results suggest that, bond return volatility strongly reacts to news announcements and that the response is asymmetric. However, the influence of macroeconomic news announcements appears insignificant for bond returns. Finally, our results paint a complex picture of the effect of macroeconomic news releases on correlations.