Labor productivity forecasts based on a Beveridge–Nelson filter: Is there statistical evidence for a slowdown?

In this paper, I assess the evidence for a structural break in labor productivity growth in the years before the Great Recession with the use of out-of-sample forecasting exercises for the years 2010 to 2019 and the recently developed Beveridge–Nelson filter. Models based on a Beveridge–Nelson filter with no structural breaks outperform those allowing for a structural break, and there is statistically significant evidence that they outperform the random walk, though all models were too optimistic about labor productivity growth. Recently developed statistical tests do point to the presence of a structural break before the Great Recession, but uncertainty about the data-generating process for labor productivity growth or the timing and magnitude of the break may be too great to be helpful in forecast preparation.     

Managing organizational entry in China: the roles of newcomer–supervisor exchange,incumbent support,and perceived insider status

The socialization literature has long recognized the important role of perceived insider status in facilitating newcomer adjustment, but knowledge on how to cultivate this desirable psychological state is limited. This study examines newcomer–supervisor exchange as an antecedent of perceived insider status of newcomers, which in turn facilitates their social adjustment and task mastery during organizational entry. We also explore incumbent support as a boundary condition for these effects. We conducted a survey study on 320 new employees and 92 supervisors in China. This study revealed that newcomer–supervisor exchange was related positively to newcomers’ perceived insider status, and perceived insider status mediated the positive relationships of newcomer–supervisor exchange with social adjustment and task mastery. In addition, incumbent support moderated the positive relationship between newcomer–supervisor exchange and perceived insider status, as well as the positive mediated relationships of newcomer–supervisor exchange with social adjustment and task mastery through perceived insider status. Specifically, these relationships were weaker when incumbent support was higher. Theoretical and practical implications were discussed.     

Semistatic hedging and pricing American floating strike lookback options

We price an American floating strike lookback option under the Black–Scholes model with a hypothetic static hedging portfolio (HSHP) composed of nontradable European options. Our approach is more efficient than the tree methods because recalculating the option prices is much quicker. Applying put–call duality to an HSHP yields a tradable semistatic hedging portfolio (SSHP). Numerical results indicate that an SSHP has better hedging performance than a delta-hedged portfolio. Finally, we investigate the model risk for SSHP under a stochastic volatility assumption and find that the model risk is related to the correlation between asset price and volatility.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

Hedging and pricing early-exercise options with complex fourier series expansion

We introduce a new numerical method called the complex Fourier series (CFS) method proposed by Chan (2017) to price options with an early-exercise feature—American, Bermudan and discretely monitored barrier options—under exponential Lévy asset dynamics. This new method allows us to quickly and accurately compute the values of early-exercise options and their Greeks. We also provide an error analysis to demonstrate that, in many cases, we can achieve an exponential convergence rate in the pricing method as long as we choose the correct truncated computational interval. Our numerical analysis indicates that the CFS method is computationally more comparable or favourable than the methods currently available. Finally, the superiority of the CFS method is illustrated with real financial data by considering Standard & Poor’s depositary receipts (SPDR) exchange-traded fund (ETF) on the S&P 500® index options, which are American options traded from November 2017 to February 2018 and from 30 January 2019 to 21 June 2019.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

基于延迟自相关特性的码速率测量技术

通信信号的识别与解调是通信侦察的重要研究内容，在通常情况下，通信信号的很多参数并不知道。本文介绍了一种基于通信信号码延迟自相关特性的码速率测量技术，并给出了码速率测量理论分析及基于硬件的实现技术，仿真和实验结果表明本方案具有较高的测量性能。     

Advertising,Price and Quality in Self–regulating Professions: A Survey

This paper surveys the theoretical and empirical literature on the relationship between advertising, fees and quality in the self–regulating professions. Much of the literature is derived from the perspective of advertising as an information–enhancing device, helping to reduce the information asymmetry between professional and client. This is consistent with the majority of the empirical studies which suggest that advertising tends to have a downward effect on professional fees, with little if any adverse effect on quality. There are, however, important issues of method and measurement which may lessen the force of this conclusion     

Analysing Materials and Energy Flows in an Industrial District using an Enterprise Input–Output Model

Recent approaches to sustainable development leave much room for policies at a local level. In fact, it is becoming evident that targets such as increasing resource productivity, preserving natural cycles, or extending the present level of welfare, are best pursued within the confines of a local area. In particular, environmental changes are best brought about by considering local systems of firms as cornerstones of cooperative strategies and using data on materials and energy use in physical terms. In this paper, an enterprise input–output model is developed for an industrial district, i.e. a local group of firms specialized in the production of a single final output. The model allows for a detailed quantitative analysis of materials and energy flows and the consequent generation of waste and pollution. As a planning tool, the model may be used to evaluate alternative scenarios, such as the possibility of re- using waste taking account of sustainability requirements. An empirical case study applies the model to an industrial district in Southern Italy producing leather sofas.     

Multilateral Comparison of Cost Structures in the Input–Output Tables of Japan,the US and West Germany

This paper presents a multilateral comparison of relative levels and structures of costs of production in Japan, the US and West Germany. The analysis is carried out by using harmonized input–output tables, which are converted at US prices by using adjusted sectoral purchasing power parities. A new accounting methodology is derived from recent developments of index number theory, whereas the chosen multilateral comparison procedure gives results that are invariant with respect to the order of the pairs of countries examined.