Spillovers and connectedness in foreign exchange markets: The role of trade policy uncertainty.

This paper analyses the directional spillover effects and connectedness for both return and volatility of nine US dollar exchange rates of globally most traded currencies under the influence of trade policy uncertainty. We find two interesting results over the study period ranging from December 1993 to July 2019. First, there exists asymmetric spillovers and connectedness among the considered exchange rates when trade policy uncertainty is present. Second, the volatility spillover is stronger than the return connectedness between exchange rate and trade policy uncertainty. These findings are robust to the presence of economic policy uncertainty effects. Concomitantly, the trade policy uncertainty patterns are also found to be useful for predicting currency market dynamics. Our findings contribute to the debate on the impact of trade policy uncertainty on the global economy and financial sector.

Automated liquid handling robot for rapid lateral flow assay development.

The lateral flow assay (LFA) is one of the most popular technologies on the point-of-care diagnostics market due to its low cost and ease of use, with applications ranging from pregnancy to environmental toxins to infectious disease. While the use of these tests is relatively straightforward, significant development time and effort are required to create tests that are both sensitive and specific. Workflows to guide the LFA development process exist but moving from target selection to an LFA that is ready for field testing can be labor intensive, resource heavy, and time consuming. To reduce the cost and the duration of the LFA development process, we introduce a novel development platform centered on the flexibility, speed, and throughput of an automated robotic liquid handling system. The system comprises LFA-specific hardware and software that enable large optimization experiments with discrete and continuous variables such as antibody pair selection or reagent concentration. Initial validation of the platform was demonstrated during development of a malaria LFA but was readily expanded to encompass development of SARS-CoV-2 and Mycobacterium tuberculosis LFAs. The validity of the platform, where optimization experiments are run directly on LFAs rather than in solution, was based on a direct comparison between the robotic system and a more traditional ELISA-like method. By minimizing hands-on time, maximizing experiment size, and enabling improved reproducibility, the robotic system improved the quality and quantity of LFA assay development efforts.

Trust and the stock market reaction to lockdown and reopening announcements: A cross-country evidence.

We conduct a country-level analysis with a sample of 44 countries to examine whether generalised social trust has an impact on the stock market reaction to government announcements of lockdown and reopening during the COVID-19 pandemic. We first conduct an event study to measure the global stock markets' reaction to government announcements of lockdown and reopening, which is measured by each stock market's cumulative abnormal return. We then employ regression analysis to investigate the relationship between generalised social trust and the stock markets' reaction to government announcements of lockdown and reopening. The results show that government announcement of lockdown had a significant negative influence on most of the stock markets, whereas the magnitude of the stock markets' reaction to government announcement of reopening is relatively marginal, indicating a possible negative bias. Moreover, we find that generalised social trust is positively related to the stock markets' reaction to government announcement of lockdown and negatively related to the stock markets' reaction to government announcement of reopening.

When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

We present a textual analysis that explains how Elon Musk's sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world's wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla's CEO. However, we did not find evidence to support linkage between Musk's sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.

Carbon emissions determinants and forecasting: Evidence from G6 countries.

We examine the explanatory and forecasting power of economic growth, financial development, trade openness and FDI for CO2 emissions in major developed economies within the context of the debate on curbing CO2 emissions Post-Paris Agreement (COP21). Using data from G-6 countries from 1978 to 2014 and employing a set of empirical approaches, we find weak evidence of the Environmental Kuznets Curve, while economic growth, capital market expansion, and trade openness are found to be major drivers of carbon emissions. Carbon emissions are also weakly and negatively affected by stock market capitalization and FDI. Moreover, the forecasting performance is quite good, particularly by augmenting the model with energy consumption and oil prices. With respect to climate commitments, our empirical findings reveal important policy implications.

Feverish sentiment and global equity markets during the COVID-19 pandemic.

This paper proposes a new approach to estimating investor sentiments and their implications for the global financial markets. Contextualising the COVID-19 pandemic, we draw on the six behavioural indicators (media coverage, fake news, panic, sentiment, media hype and infodemic) of the 17 largest economies and data from 1 st January 2020 to 3 rd February 2021. Our key findings, obtained using a time-varying parameter-vector auto-regression (TVP-VAR) model, indicate the total and net connectedness for the new index, entitled 'feverish sentiment'. This index provides us insight into economies that send or receive the sentiment shocks. The construction of the network structures indicates that the United Kingdom, China, the United States and Germany became the epicentres of the sentimental shocks that were transmitted to other economies. Furthermore, we also explore the predictive power of the newly constructed index on stock returns and volatility. It turns out that investor sentiment positively (negatively) predicts the stock volatility (return) at the onset of COVID-19. This is the first study of its kind to assess international feverish sentiments by proposing a novel approach and its impacts on the equity market. Based on empirical findings, the study also offers some policy directions to mitigate the fear and panic during the pandemic.

What Drives Bitcoin? An Approach from Continuous Local Transfer Entropy and Deep Learning Classification Models.

Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins' price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection do not find significative the explanatory power of NASDAQ and Tesla. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to self-regulate and does not need additional drivers to improve the accuracy of the price direction.

Wash-Free, Digital Immunoassay in Polydisperse Droplets.

Immunoassays are important for the detection of proteins to enable disease identification and monitor treatment, but many immunoassays suffer from sensitivity limitations. The development of digital assays has enabled highly sensitive biomarker detection and quantification, but the necessary devices typically require precisely controlled volumes to reduce biases in concentration estimates from compartment size variation. These constraints have led to systems that are often expensive, cumbersome, and challenging to operate, confining many digital assays to centralized laboratories. To overcome these limitations, we have developed a simplified digital immunoassay performed in polydisperse droplets that are prepared without any specialized equipment. This polydisperse digital droplet immunoassay (ddIA) uses proximity ligation to remove the need for wash steps and simplifies the system to a single reagent addition step. Using interleukin-8 (IL-8) as an example analyte, we demonstrated the concept with samples in buffer and diluted whole blood with limits of detection of 0.793 pM and 1.54 pM, respectively. The development of a one-pot, washless assay greatly improves usability compared to traditional immunoassays or digital-based systems that rely heavily on wash steps and can be run with common and readily available laboratory equipment such as a heater and simple fluorescent microscope. We also developed a stochastic model with physically meaningful parameters that can be utilized to optimize the assay and enable quantification without standard curves, after initial characterization of the parameters. Our polydisperse ddIA assay serves as an example of sensitive, lower-cost, and simpler immunoassays suitable for both laboratory and point-of-care applications.

Environmental consequences of population, affluence and technological progress for European countries: A Malthusian view.

This paper examines the short-run and long-run effects of economic, sociological and energy factors on environmental degradation in 28 European countries. In so doing, we employ Panel Vector Autoregressive (PVAR) and Fully Modified OLS (FMOLS) approaches on data from 1990 to 2014 in a STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework. Key empirical results indicate that these factors may contribute to environmental improvement in the short run; however, there are adverse implications in the long-run. Specifically, economic factors including economic growth, trade openness and foreign direct investment cause environmental degradation in the under-analysis economies. The sociological factors as measured by the population growth and the level of urbanization also show a negative impact on the environmental degradation in the short-run but in the long run, both population size and urbanization increase environmental degradation. These findings are in line with the concerns raised by Thomas Robert Malthus in his Essay on the Principle of Population. With regards to the energy factors, it indicates that the renewable energies help the European environment by reducing the level of carbon dioxide emissions whereas the higher energy intensity is an ecological threat. Our results remain robust in the EKC framework.

The impact of carbon emissions on country risk: Evidence from the G7 economies.

This paper empirically investigates the effect of carbon emissions on sovereign risk? To answer this question, we use fixed effects model by using annual data from G7 advanced economies, which includes Canada, France, Germany, Italy, Japan, UK and USA, for the period from 1996 to 2014. We employ a novel extreme value theory to measure sovereign risk. The results indicate that climate change (carbon emissions) are likely to increase sovereign risk significantly. We also expand our analysis to some specific sectors, as some of the sectors emit more carbon than others. Specifically, we take top three polluting sectors namely: transportation, electricity and industry and show that they are more likely to increase the sovereign risk. Our results are robust to change in risk measures, estimation in differences and dynamic version of econometric models. Therefore, we have robust consideration that the carbon emissions significantly explain the sovereign risk.

Managing environmental challenges: Training as a solution to improve employee green performance.

Although environmental training program has become a vital solution to minimize environmental challenges, the existing literature has paid little attention to (1) the connection between environmental training and employee in-role green performance (EIGP), (2) the mediating role of employee environmental commitment in this environmental training-EIGP connection, and (3) the cultural perspective (Western and Local) influences on interrelationships between environmental training, EIGP, and employee environmental commitment. Drawing on the social exchange theory and cultural perspective, a quantitative study based on survey data collected from 301 respondents at hotels in Vietnam was employed to fill these research gaps. Findings reveal that environmental training program is as an important tool to drive EIGP directly, and this environmental training-EIGP relationship is significantly mediated by employee environmental commitment. Interestingly, the study indicates support for our prediction that the mediating role of employee environmental commitment on the environmental training-EIGP link is stronger at hotels managed by Western hospitality companies. However, unexpectedly, cultural influence does not moderate the effect of environmental training on EIGP. Other than theoretical contributions, our study carries important practical implications that can help organizations reduce their carbon footprint. Limitations and further research directions have also been discussed.

Asymmetric correlation and hedging effectiveness of gold & cryptocurrencies: From pre-industrial to the 4th industrial revolution✰.

This paper examines the long-run and short-run asymmetric effects of gold and cryptocurrency returns on the Thai stock market. Employing daily data on gold prices from 2000 to 2019 and on cryptocurrency (Bitcoin) from 2013 to 2019 in a linear and non-linear Autoregressive Distributed Lag (ARDL) framework, we investigate and contrast the hedging effectiveness of gold and bitcoins for equities. This study also evaluates whether hedging potential of gold or cryptocurrency remains equally strong in bearish and bullish conditions of the stock market. Our key findings on stock and gold returns reveal that the effects of gold on the stock market are asymmetric in most of the cases. Negative asymmetry is more likely to occur regardless of stock market conditions. On the other hand, there is very limited evidence showing the meaningful effect of cryptocurrency. The robustness of the ARDL bounds test of co-integration provides evidence for a strong long-run relationship in all cases. Contrary to the existing literature, our results suggest that neither gold nor cryptocurrency acts as a good instrument for hedging in the stock market. Correlations between stock/gold and stock/cryptocurrency pairs are found to be positive in most cases. Our findings imply that adding gold or cryptocurrency to a stock portfolio does not enhance its risk-adjusted return.

Did Congress trade ahead? Considering the reaction of US industries to COVID-19.

During the ongoing COVID-19 pandemic in the US, there has been considerable media attention regarding several US legislators who traded stocks in late January through February 2020. The concern is that these legislators traded in anticipation of COVID-19 having a major impact on the financial markets, while publicly suggesting otherwise. We consider whether these legislator trades were in a time window, and of a nature, that would be consistent with trading ahead of the market. Towards this end, we assess the reactions of US industries to sudden COVID-related news announcements, concomitantly with an analysis of levels of investor attention to COVID. Results suggest that, at an industry-level, for legislator trading to be "ahead of the market" it needed to have been done prior to February 26, and involving the 15 industries we identify as having abnormal returns, especially medical and pharmaceutical products (positive); restaurants, hotels, and motels (negative); as well as services and utilities. These criteria are met by many of the legislator trades. Our results help to both parameterize concerns about this case of legislator trading; as well as provide insight into the reactions and expectations of investors toward COVID-19.

General methods for quantitative interpretation of results of digital variable-volume assays.

In digital assays, devices are typically considered to require precisely controlled volumes since variation in compartment volumes causes biases in concentration estimates. To enable more possibilities in device design, we derived two methods to accurately calculate target concentrations from raw results when the compartment volume may vary and may not follow known parametrically described distributions. The Digital Variable Volume (dvv) method uses volumes of ON compartments (those with positive signals) and the total sample volume, while the Digital Variable Volume Approximation (dvva) method uses the number of ON compartments, the total number of compartments, and a set of separately measured volumes. We verified the trueness of the dvv and dvva methods using simulated assays where volumes followed an empirical distribution (based on measured droplet volumes) and well known distributions with a wide range of standard deviations. We applied both methods to digital PCR experiments with polydisperse volumes, and also derived equations to estimate standard errors and limits of detection. The dvv method allows the compartment volume to follow any distribution in each assay run, the dvva method allows for quantification without in-assay volume measurements, and both methods potentially enable new designs of digital assays.

Role of financial development, economic growth & foreign direct investment in driving climate change: A case of emerging ASEAN.

In the context of remarkable economic growth and financial development in the emerging economies of East Asia, this paper attempts to shed light on the ecological consequences (CO2 emission) of economic growth, foreign direct investment and financial development in the selected ASEAN-5 economies. Drawing on the data from 1982 to 2014, we employed a set of quantitative techniques for panel data analysis which entailed Dynamic Ordinary Least Squares (DOLS) and Fully Modified OLS (FMOLS) approaches. Our findings indicate that financial and economic development, as well as FDI, have a statistically significant long-run co-integrating relationship with environmental degradation (CO2 emissions) in the under analysis economies. It showed that in ASEAN-5 countries, economic growth, financial development and FDI leads to an increase in environmental degradation. The quadratic term for economic growth showed a negative impact on environmental degradation i.e. Environmental Kuznets Curve (EKC). Our key findings manifest and emphasise the importance of appropriate policies for more inclusive economic and financial development and sustainable foreign direct investment which does not impede on the environment.

Simple Polydisperse Droplet Emulsion Polymerase Chain Reaction with Statistical Volumetric Correction Compared with Microfluidic Droplet Digital Polymerase Chain Reaction.

Nucleic acid amplification technology, such as polymerase chain reaction (PCR), has enabled highly sensitive and specific disease detection and quantification, leading to more accurate diagnosis and treatment regimens. Lab-on-a-chip applications have developed methods to partition single biomolecules, such as DNA and RNA, into picoliter-sized droplets. These individual reaction vessels lead to digitization of PCR enabling improved time to detection and direct quantification of nucleic acids without a standard curve, therefore simplifying assay analysis. Though impactful, these improvements have generally been restricted to centralized laboratories with trained personnel and expensive equipment. To address these limitations and make this technology more applicable for a variety of settings, we have developed a statistical framework to apply to droplet PCR performed in polydisperse droplets prepared without any specialized equipment. The polydisperse droplet system allows for accurate quantification of droplet digital PCR (ddPCR) and reverse transcriptase droplet digital PCR (RT-ddPCR) that is comparable to commercially available systems such as BioRad's ddPCR. Additionally, this approach is compatible with a range of input sample volumes, extending the assay dynamic range beyond that of commercial ddPCR systems. In this work, we show that these ddPCR assays can reduce overall assay time while still providing quantitative results. We also report a multiplexed ddPCR assay and demonstrate proof-of-concept methods for rapid droplet preparation in multiple samples simultaneously. Our simple polydisperse droplet preparation and statistical framework can be extended to a variety of settings for the quantification of nucleic acids in complex samples.

Polydisperse emulsion digital assay to enhance time to detection and extend dynamic range in bacterial cultures enabled by a statistical framework.

Microbiological culture remains the most sensitive method for detecting viable and infectious bacteria, but these methods often require at least 24 hours to visibly identify bacterial growth. Lab-on-a-chip applications have utilized methods to isolate bacteria in picoliter-sized reaction vessels, resulting in digitized signals that offer improved time-to-detection and improved quantification. Although a great improvement, these approaches typically require expensive and specialized equipment, trained laboratory personnel, and maximum addressable volumes that can be orders of magnitude less than needed for clinically relevant limits of detection. To address these limitations, we have developed a simple method for preparing and semi-quantitatively analyzing small-volume droplets for performing digital culture, allowing for the detection of bacteria. This work includes a description of the method, characterization of resulting droplet sizes, comparison to traditional culture, and a statistical framework to quantify results. Though polydisperse, the droplet size distribution was consistent over different experiments, and there was a correlation between the observed number of positive droplets and the bulk concentration that can serve as a calibration curve for samples with unknown droplet size distributions. This statistical framework enables the simplification of droplet preparation and allows for accurate quantification even with polydisperse droplet sizes. The application of this method can also be extended to a variety of settings for the detection or quantification of bacteria in complex samples.

A Robust Bioimpedance Structure for Smartwatch-Based Blood Pressure Monitoring.

One potential method to estimate noninvasive cuffless blood pressure (BP) is pulse wave velocity (PWV), which can be calculated by using the distance and the transit time of the blood between two arterial sites. To obtain the pulse waveform, bioimpedance (BI) measurement is a promising approach because it continuously reflects the change in BP through the change in the arterial cross-sectional area. Many studies have investigated BI channels in a vertical direction with electrodes located along the wrist and the finger to calculate PWV and convert to BP; however, the measurement systems were relatively large in size. In order to reduce the total device size for use in a PWV-based BP smartwatch, this study proposed and examined a robust horizontal BI structure. The BI device was also designed to apply in a very small body area. The proposed structure was based on two sets of four electrodes attached around the wrist. Our model was evaluated on 15 human subjects; the PWV values were obtained with various distances between two BI channels to assess the efficacy. The results showed that the designed BI system can monitor pulse rate efficiently in only a 0.5 × 1.75 cm² area of the body. The correlation of pulse rate from the proposed design against the reference was 0.98 ± 0.07 (p < 0.001). Our structure yielded higher detection ratios for PWV measurements of 99.0 ± 2.2%, 99.0 ± 2.1%, and 94.8 ± 3.7% at 1, 2, and 3 cm between two BI channels, respectively. The measured PWVs correlated well with the BP standard device at 0.81 ± 0.08 and 0.84 ± 0.07 with low root-mean-squared-errors at 7.47 ± 2.15 mmHg and 5.17 ± 1.81 mmHg for SBP and DBP, respectively. The result demonstrates the potential of a new wearable BP smartwatch structure.

Intracellular transport of insulin granules is a subordinated random walk.

We quantitatively analyzed particle tracking data on insulin granules expressing fluorescent fusion proteins in MIN6 cells to better understand the motions contributing to intracellular transport and, more generally, the means for characterizing systems far from equilibrium. Care was taken to ensure that the statistics reflected intrinsic features of the individual granules rather than details of the measurement and overall cell state. We find anomalous diffusion. Interpreting such data conventionally requires assuming that a process is either ergodic with particles working against fluctuating obstacles (fractional brownian motion) or nonergodic with a broad distribution of dwell times for traps (continuous-time random walk). However, we find that statistical tests based on these two models give conflicting results. We resolve this issue by introducing a subordinated scheme in which particles in cages with random dwell times undergo correlated motions owing to interactions with a fluctuating environment. We relate this picture to the underlying microtubule structure by imaging in the presence of vinblastine. Our results provide a simple physical picture for how diverse pools of insulin granules and, in turn, biphasic secretion could arise.

Distribution of directional change as a signature of complex dynamics.

Analyses of random walks traditionally use the mean square displacement (MSD) as an order parameter characterizing dynamics. We show that the distribution of relative angles of motion between successive time intervals of random walks in two or more dimensions provides information about stochastic processes beyond the MSD. We illustrate the behavior of this measure for common models and apply it to experimental particle tracking data. For a colloidal system, the distribution of relative angles reports sensitively on caging as the density varies. For transport mediated by molecular motors on filament networks in vitro and in vivo, we discover self-similar properties that cannot be described by existing models and discuss possible scenarios that can lead to the elucidated statistical features.