Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic

Since the level of markets’ information efficiency is key to profiteering by strategic players, Shocks; such as the COVID-19 pandemic, can play a role in the nature of markets’ information efficiency. The martingale difference and conditional heteroscedasticity tests are used to evaluate the Adaptive form of market efficiency for four (4) major stock market indexes in the top four affected economies during the COVID-19 pandemic (USA, Brazil, India, and Russia). Generally, based on the martingale difference spectral test, there is no evidence of a substantial change in the levels of market efficiency for the US and Brazilian stock markets in the short, medium, and long term. However, in the long term, the Indian stock markets became more information inefficient after the coronavirus outbreak while the Russian stock markets become more information efficient. Intuitively, these affect the forecastability and predictability of these markets’ prices and/or returns. Thereby, informing the strategic and trading actions of stock investors (including arbitrageurs) towards profit optimization, portfolio asset selection, portfolio asset adjustment, etc. Similar policy implications are further discussed.     

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.     

Means‐Tested Long‐Term Care and Family Transfers

One of the pervasive problems with means‐tested public long‐term care programs is their inability to prevent individuals who could afford private long‐term services from taking advantage of public care. They often manage to elude the means‐test net through ‘strategic impoverishment’. We show in a simple model how this problem comes about, how it affects welfare and how it can be mitigated.     

Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.     

食品检测实验室质量监督方法探讨

食品检测实验室中最为重要的就是质量监督管理工作,做好质量监督有利于提高实验室工作人员的质检与管理能力,进而推动有关人员的学习进步,以胜任工作岗位,提高该食品检测实验室的质量检测水平,确保检测数据的有效性与无误性。但现阶段我国的实验室由于缺乏统一的行业标准,导致其质量监督工作水平参差不齐,出现了一系列不利于质量检测的因素,比如质量监督目的不明、质量监督内容不完整、质量监督措施不完善等不足,都会影响该实验室对于食品的检测。因此,本文主要就食品检测实验室的质量监督方法进行探究,从各个方面指出质量监督的技术要点。     

超声波技术在食品检测中的应用

超声波技术因其具备适应性强、操作简单、设备投资低等优势而被广泛应用于食品检测领域。本文介绍了超声波技术检测食品的原理和在食品检测中的应用价值,并详细阐述了该技术在食品检测中的具体应用,为超声波技术更好的应用于食品检测领域提供参考。     

Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models

There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed models where interest is on the variance components, we study the effects of random effects misspecification on random effects inference in linear mixed models, for which there is considerably less literature. Our findings are surprising and contrary to general belief: for point estimation, maximum likelihood estimation of the variance components under misspecification is consistent, although in finite samples, both the bias and mean squared error can be substantial. For inference, we show through theory and simulation that under misspecification, standard likelihood ratio tests of truly non‐zero variance components can suffer from severely inflated type I errors, and confidence intervals for the variance components can exhibit considerable under coverage. Furthermore, neither of these problems vanish asymptotically with increasing the number of clusters or cluster size. These results have major implications for random effects inference, especially if the true random effects distribution is heavier tailed than the normal. Fortunately, simple graphical and goodness‐of‐fit measures of the random effects predictions appear to have reasonable power at detecting misspecification. We apply linear mixed models to a survey of more than 4 000 high school students within 100 schools and analyse how mathematics achievement scores vary with student attributes and across different schools. The application demonstrates the sensitivity of mixed model inference to the true but unknown random effects distribution.     

Commodities and Macroeconomic Factors: Unconditional Volatility Measures

Abstract:

This study focuses on the measurement of spillover effects from macroeconomic factors to commodity volatility. It argues that such measurement is sensitive to volatility computation and to causality testing. To this end, I analyze two commodity data sets-gold and the Continuous Commodity Index (1969-2011), and twenty-four Dow Jones futures indexes (1991-2011)-and various macroeconomic indicators. I conclude that the macroeconomic factors that influence volatility generally depend on the commodity under consideration. I also explore whether commodities of the same class experience volatility shifts around the same dates, and find that this is not the case except for energy commodities.     

炼化工程质量第三方检测实践与探讨

介绍了炼化工程传统的质量控制方法,重点论述了第三方检测在炼化工程质量控制中的运行模式与取得的效果,并提出了一些建议,可供其他炼化工程建设单位借鉴。     

Does institutional quality resolve the Lucas Paradox?

The Lucas Paradox observes that capital flows predominantly to relatively rich countries, contradicting the neoclassical prediction that it should flow to poorer capital-scarce countries. In an influential study, Alfaro, Kalemli-Ozcan, and Volosovych (AKV) argue that cross-country variation in institutional quality can fully explain the Paradox, contending that if institutional quality is included in regression models explaining international capital inflows, a country’s level of economic development is no longer statistically significant. We replicate AKV’s results using their cross-sectional IFS capital flow data. Motivated by the importance of conducting inference in statistically adequate models, we focus on misspecification testing of alternative functional forms of their empirical model of capital flows. We show that their resolution of the Paradox relies on inference in a misspecified model. In models that do not fail basic misspecification tests, even though institutional quality is a significant determinant of capital inflows, a country’s level of economic development also remains a significant predictor. The same conclusions are reached using an extended dataset covering more recent IFS international capital flow data, first-differenced capital stock data and additional controls.