Will a Common European Monetary Policy Have Asymmetric Effects?

We survey the existing work on the cross-country differences in the transmission of European monetary policy. We find that prior work, focusing on macroeconomic data, does not clearly answer the question posed in the title and offer some explanations for the ambiguity. Aside from the inappropriate design of the prior empirical exercises, we point to the need to use microeconomic data to disentangle the potentially confounding effects of differences in the behavior of agents in different countries and the composition of agents across countries. We review the leading theories of monetary non-neutrality to find the structural features of the economy that in principle could alter the transmission mechanism. We provide some evidence that these structural features do differ markedly among the major European economies. We then explore the potential importance of these structural factors drawing on firm-level data from one country, Italy, and we show how the business cycle has differentially affected firms in Italy over the last decade. It appears that the 1992 monetary tightening and 1993 recession were not uniformly felt by Italian firms, but differed along the lines suggested by several of the theories. Several of the dimensions which appear to be important in the Italian experience are dimensions which vary noticeably across European countries, suggesting that further work on firm-level comparisons in other European countries may be valuable.monetary policy transmission asymmetries, firm level data

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Forecasting the equity risk premium: The role of technical indicators

Ministry of Education, Singapore under its Academic Research Funding Tier

Capital and Punishment: Resource Scarcity Increases Endorsement of the Death Penalty

Faced with punishing severe offenders, why do some prefer imprisonment whereas others impose death? Previous research exploring death penalty attitudes has primarily focused on individual and cultural factors. Adopting a functional perspective, we propose that environmental features may also shape our punishment strategies. Individuals are attuned to the availability of resources within their environments. Due to heightened concerns with the costliness of repeated offending, we hypothesize that individuals tend toward elimination-focused punishments during times of perceived scarcity. Using global and United States data sets (studies 1 and 2), we find that indicators of resource scarcity predict the presence of capital punishment. In two experiments (studies 3 and 4), we find that activating concerns about scarcity causes people to increase their endorsement for capital punishment, and this effect is statistically mediated by a reduced willingness to risk repeated offenses. Perceived resource scarcity shapes our punishment preferences, with important policy implications

ARE VOLATILITY EXPECTATIONS CHARACTERIZED BY REGIME SHIFTS? EVIDENCE FROM IMPLIED VOLATILITY INDICES

This paper examines nonlinearities in the dynamics of volatility expectations using benchmarks of implied volatility for the US and Japanese markets. The evidence from Markov regime-switching models suggests that volatility expectations are likely to be governed by regimes featuring a long memory process and significant leverage effects. Market volatility is expected to increase in bear periods and decrease in bull periods. Leverage effects constitute thus an important source of nonlinearities in volatility expectations. There is no evidence of long swings associated with financial crises, which do not have the potential of shifting volatility expectations from one regime to another for long protracted periods.Markov Regime Switching, Implied Volatility Index, Nonlinear Modelling.

Examining the Factors Causing a Drastic Reduction and Subsequent Increase of Roadway Fatalities on United States Highways Between 2005 and 2016

The substantial decline in motor-vehicle fatal crashes over the period of 2008 to 2011 has been subjected to extensive research in the last few years. Starting from the early 1970s, reduced fatalities have been associated with economic downfalls by looking into empirical historical evidence in many countries of the world. Following the perceptible reduction in fatalities in the United States (U.S.) beginning in 2008, which concurred with a major economic recession during the same period, some researchers focused on finding the relative influence of such a hypothesis using statistical modeling. This study sought to serve as an extension of the Project 17-67 by the National Cooperative Highway Research Program (NCHRP) to provide a thorough investigation of the factors influencing fatalities during and after the 2008 recession using an updated dataset. Two Poisson-gamma regression models, considering (MCS) or not considering (MNCS) a varying effect among states, and a log-change regression model were developed under the Project NCHRP 17-67. The primary research objectives were to run the existing models with a state-based dataset from 2001 to 2012 and recalibrate it with an updated dataset to 2016 to check the adequacy of the models in predicting fatalities after the recession or if any additional variable is required. The study further investigated the inconsistent effect of the recession on fatalities by land use type. The modeling results showed remarkable improvements with the updated dataset, where both the MNCS and MCS models could reflect the fluctuations in fatalities over the focus period. The Poisson-gamma models outperformed the log-change model in predicting total, rural, and urban fatalities. The effect analysis revealed that the economic factors contribute as much as 84% to 86% in the reduction and subsequent increase in fatalities during and after the recession. The unemployment rate of 16 to 24 years old, median household income, and the price of gasoline were found to be the most statistically significant parameters in both the models. The goal of this research was to provide a better understanding of the economic variables affecting fatalities, alongside focus on rarely addressed issues like variable effect on rural versus urban environments

Twitter sentiment analysis: applications in healthcare and finance

This research explores the influence of Twitter sentiment on healthcare and finance industries. It assesses how Twitter sentiment and culture measure influence COVID-19 statistics, and it investigates the impact of Twitter sentiment on S&P 1500 stock mispricing. Furthermore, it examines how tweet sentiment predicts major industry returns. The first part examines how Hofstede’s Culture Dimensions (HCD) and Twitter economic uncertainty index (TEU) relate to COVID-19 infection rate and death rate. The results show certain aspects in HCD, such as power distance index (PDI) and masculinity (MAS) both are negatively and significantly associated with the infection rate, while indulgence (IVR) and long-term orientation (LTO) exhibit negative statistical significance to the death rate. TEU based in USA is relevant to COVID-19 death rate in short run (up to 3 months). Some practical strategies are proposed for public health officials to help mitigate COVID-19 spread. The second part bridges a research gap by exploring the relation between aggregated tweet contents and stock market mispricing. In short, tweet features affect future stock mispricing, in different directions and magnitudes. For overvalued stocks, tweet variables including proportion of external links, average number of words, percentage of retweets, likes and replies are negatively associated with mispricing of S&P 1500 stocks. Average number of words possibly reduces mispricing by reducing idiosyncratic volatility, while proportion of external links can mitigate mispricing via channels other than liquidity or idiosyncratic volatility. For undervalued stocks, only average number of words is positively related to mispricing; average number of words affect mispricing via channels other than liquidity or idiosyncratic volatility. Additionally, this study investigates how tweet sentiment from S&P 1500 firms predicts major industry returns by constructing multiple sentiment indices. The robustness tests show highly consistent results, proving such indices can predict the returns from three out of five major industries, including Consumables, High Technology and Healthcare. In general, the sentiment index type and prediction length do not matter much. In conclusion, this research shows tweet sentiment is more than some meaningless noise. Instead, it has beneficial applications in both healthcare and finance fields, such as COVID-19 pandemic prediction and possible investment reference