Impact of coronavirus pandemic on stock index: A polynomial regression with time delay

Motivation

Under contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the possible nonlinear relations between the two indicators. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated.

Objectives

To contribute to filling the gaps identified in existing research, this study models relationship between the stock market index and coronavirus pandemic by leveraging volatility in the stock market and covid data through time delay and best degree in a polynomial environment. The resultant optimal time delay and best degree model is used to derive a high-accuracy prediction of stock market index.

Novelty

In line with the possible relations, the novelty of this study is that it proposes, validates and implements polynomial regression with time delay to model nonlinear relationship between the stock index and covid.

Methods

This study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment.

Findings

The empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation of the nonlinear relationship such predictors have with stock index for China. Relative to results from the polynomial regression without delay, the empirical evidence from the model with delay show that an optimal time delay of 17 weeks makes it possible to predict the stock index at high accuracy and record improvements of 16-fold or higher. The representative delay model is used to project for up to 17 weeks for future trends in the stock index.

Implication

The implication of the findings herein is that the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases to achieve better results.

Time and frequency analysis of daily-based nexus between global CO2 emissions and electricity generation nexus by novel WLMC approach

The studies have focused on changes in CO2 emissions over different periods, including the COVID-19 pandemic. Even if CO2 emissions are temporarily reduced during the pandemic according to annual figures, this may be misleading. Considering annual figures is important to understand the overall trend, but using data with much higher frequency (e.g., daily) is much better suited to investigate dynamic relationships and external effects. Therefore, this study comprehensively analyzes the association between CO2 emissions and disaggregated electricity generation (EG) sources across the globe by employing the novel wavelet local multiple correlation (WLMC) approach on daily data from 1st January 2020 to 31st March 2023. The results demonstrate that (1) based on the main statistics, daily CO2 emissions range between 69 MtCO2 and 116 MtCO2, indicating that there is an oscillation, but no sharp changes over the analyzed period. (2) based on the baseline regression using the dynamic ordinary least squares (DOLS) approach, the constructed estimation models have a high predictive ability of CO2 emissions, reaching ~ 94%; (3) in the further analysis employing the WLMC approach, there are significant externalities between EG resources, which affect CO2 emissions. The results present novel insights about time- and frequency-varying effects as well as a disaggregated analysis of the effect of EG on CO2 emissions, demonstrating the significance of the energy transition towards clean sources around the world.

Small villages and their sanitary infrastructure-an unnoticed influence on water quantity and a threat to water quality in headwater catchments

In rural catchments, villages often feature their own, separate urban water infrastructure, including combined sewer overflows (CSOs) or wastewater treatment plants (WWTPs). These point sources affect the water quantity and quality of the receiving low order streams. However, the extent of this impact is rarely monitored. We installed discharge and water quality measurements at the outlet of two small, neighbouring headwater catchments, one that includes a village, a WWTP, and two CSOs, while the other is predominantly influenced by agricultural activities. We also deployed electrical conductivity (EC) loggers at the CSOs to accurately detect discharge times. Discharge from the WWTP and CSOs led to higher peak flows and runoff coefficients during events. Less dilution of EC and increasing ammonium-N (NH4 - N) and ortho-phosphorus (oPO4 - P) concentrations indicate a significant contribution of poorly treated wastewater from the WWTP. During CSO events, water volumes and nutrient loads were clearly elevated, although concentrations were diluted, except for nitrite-N (NO2 - N) and particulate phosphorus (PP). Baseflow nitrate-N (NO3 - N) concentrations were diluted by the WWTP effluent, which led to considerably lower concentrations compared to the more agriculturally influenced stream. Concentrations of oPO4 - P, NH4 - N, and NO2 - N, which are most likely to originate from the WWTP, vary throughout the year but are always elevated. Our study shows the major and variable impact rural settlements can have on stream hydrology and water quality. Point sources should be monitored more closely to better understand the interaction of natural catchment responses and effects caused by sanitary infrastructure.

How resilient was trade to COVID-19?

We provide stylized facts on the short-run resilience of exports to the COVID-19 pandemic across product characteristics. Relying on global monthly product-level exports to the United States, Japan, and 27 European Union countries from January 2018 to December 2021, we show that products with a higher reliance on China or few countries as input suppliers saw stronger declines in exports as a result of the COVID-19 shock while those with more automated production processes saw exports increase. Our analysis also shows that product characteristics played different roles mediating export responses at different stages of the 2020-2021 COVID-19 crisis. We document rapid reductions in vulnerabilities for exports of unskilled-intensive production. Reliance on diversified inputs from abroad progressively contributed to resilience following an initial negative role when trade was severely disrupted globally.

COVID-19 and China commodity price jump behavior: An information spillover and wavelet coherency analysis

Jumps in commodity prices can make asset risk management challenging. This study explores the influence feature of the COVID-19 epidemic on China's commodity price jumps, using 5-min intraday high-frequency futures data of three China's commodity markets (energy, chemical, and metal) from January 23, 2020 to June 10, 2022. We find that firstly the information spillover from the COVID-19 spread situation to China's energy price jumps is relatively weak, and the COVID-19 epidemic shows the most substantial jump information spillover pattern to China's chemical price. The information spillover pattern is time-varying across the COVID-19 spread situation phase. Secondly, there are co-movement patterns between China's commodity price and China/global COVID-19 confirmed cases. This co-movement feature mainly occurs at the medium- or long-run time scales, and varies across commodities. Thirdly, the demand elasticity for China's commodities and its dependence on imports and exports are the main factors influencing the sensitivity of its price jumps to the COVID-19 outbreak.

The impact of COVID-19 on commodity options market: Evidence from China

Considering the severe economic impact of COVID-19, this study examines COVID-19's influence on the Chinese commodity market. The literature shows that COVID-19's influence in China during its abatement period has not been well investigated. We address this issue by the intraday analysis of the volatility from 16 commodity options contracts in the Chinese commodity options market over the period 2019-2021. We demonstrate that while the pandemic eased in China after its initial outbreak, it still significantly affected the volatility of Chinese agricultural commodities options. In contrast, its impacts on the volatility of options for petrochemicals, ores, and metals are negligible. This pattern reflects the role of pandemic-led supply disruptions affecting agricultural commodity prices as necessities, contributing to higher price volatility relative to non-agricultural commodities, which are less volatile.

Time-frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic

Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management. However, most existing research focuses on the lower-order moment nexus (i.e. the return and volatility interactions). For the first time, this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains. We combine the realized moment measures and wavelet coherence, and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach (Chatziantoniou et al. in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach. Technical report, University of Pretoria, Department of Economics, 2021) using intraday high-frequency data. The empirical results demonstrate that the comovement of realized volatility between BTC and other cryptocurrencies is stronger than that of the realized skewness, realized kurtosis, and signed jump variation. The comovements among cryptocurrencies are both time-dependent and frequency-dependent. Besides the volatility spillovers, the risk spillovers of high-order moments and jumps are also significant, although their magnitudes vary with moments, making them moment-dependent as well and are lower than volatility connectedness. Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term (1-7 days). Furthermore, the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic. Several practical implications are drawn for crypto investors, portfolio managers, regulators, and policymakers in optimizing their investment and risk management tactics.

A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic

During the COVID-19 pandemic, policymakers needed to assess the impact of large monetary and fiscal policy interventions in as close to real time as possible-yet existing survey-based indicators are usually released monthly or quarterly. The use of high-frequency data to track economic activity has become widespread. This paper constructs a near real-time economic activity indicator for the Brazilian economy during the COVID-19 pandemic. Brazil's integrated national electricity sector, which covers over 98% of the population, allows us to construct an economic activity indicator based solely on electricity consumption data that are available at near real time and accounts for activity in the large informal sector of the economy. We construct our indicator by isolating the variability in electricity consumption that is not related to economic activity, then measure how well monthly and quarterly versions of our indicator track against standard economic indicators. The results show strong correlation with standard indicators, notably during economic shocks.

Uncertainty index and stock volatility prediction: evidence from international markets

This study investigates the predictability of a fixed uncertainty index (UI) for realized variances (volatility) in the international stock markets from a high-frequency perspective. We construct a composite UI based on the scaled principal component analysis (s-PCA) method and demonstrate that it exhibits significant in- and out-of-sample predictabilities for realized variances in global stock markets. This predictive power is more powerful than those of two commonly employed competing methods, namely, PCA and the partial least squares (PLS) methods. The result is robust in several checks. Further, we explain that s-PCA outperforms other dimension-reduction methods since it can effectively increase the impacts of strong predictors and decrease those of weak factors. The implications of this research are significant for investors who allocate assets globally.

A Multi-market Comparison of the Intraday Lead-Lag Relations Among Stock Index-Based Spot, Futures and Options

Using 1-min data, we explore the dynamic variation of the intraday lead-lag relations between stock indices and their derivatives through a comprehensive study with broader coverage of research objectives and methodologies. This paper provides explicit evidence that the futures and options exhibit price leadership over the spot market, and the options is ahead of the futures on most trading days in all three markets. This paper also reports a new finding that the relation between the derivative and its underlying index reverses when the index return has a significantly larger mean value, and the reversal phenomenon is also observed in the relations between the futures and the options, which enriches the empirical results of intraday lead-lag relations. Moreover, these conclusions still hold under the impact of extreme events, e.g., the outbreak of the Covid-19. Finally, we construct a pair trading strategy based on the intraday lead-lag relationships, which can get better performance than the corresponding spot index. Our findings can potentially help regulators understand the price discovery process between the index and its derivatives, and also be of great value for timely adjustment of investors intraday trading strategies.

High-frequency machine datasets captured via Edge Device from Spinner U5-630 milling machine

The high-frequency (HF) machine data is retrieved from the Spinner U5-630 milling machine via an Edge Device. Unlike cloud computing, an Edge Device refers to distributed data processing of devices in proximity that generate data, which can thereby be used for analysis [1,2]. This data has a sampling rate of 2ms and hence, a frequency of 500Hz. The HF machine data is from various experiments performed. There are 2 experiments performed (parts 1 and 2). The experimented part 1 has 12 .json data files and part 2 has 11 .json files. In total, there are 23 files of HF machine data from 23 experiments. The HF machine data has vast potential for analysis as it contains all the information from the machine during the machining process. One part of the information was used in our case to calculate the energy consumption of the machine. Similarly, the data can be used for retrieving information of torque, commanded and actual speed, NC code, current, etc.

FX market volatility modelling: Can we use low-frequency data?

High-frequency data tend to be costly, subject to microstructure noise, difficult to manage, and lead to high computational costs. Is it always worth the extra effort? We compare the forecasting accuracy of low- and high-frequency volatility models on the market of six major foreign exchange market (FX) pairs. Our results indicate that for short-forecast horizons, high-frequency models dominate their low-frequency counterparts, particularly in periods of increased volatility. With an increased forecast horizon, low-frequency volatility models become competitive, suggesting that if high-frequency data are not available, low-frequency data can be used to estimate and predict long-term volatility in FX markets.

Macro factors and the realized volatility of commodities: A dynamic network analysis

This paper explores the relationship between macro-factors and the realized volatility of commodity futures. Three main commodities-soybeans, gold and crude oil-are investigated using high-frequency data. For macro factors, we select six indicators including economic policy uncertainty (EPU), the economic surprise index (ESI), default spread (DEF), the investor sentiment index (SI), the volatility index (VIX), and the geopolitical risk index (GPR). These indicators represent three dimensions from macroeconomics and capital markets to a broader geopolitical dimension. Through establishing a dynamic connectedness network, we show how these macro factors contribute to the volatility fluctuations in commodity markets. The results demonstrate clearly distinctive features in the reaction to macro shocks across different commodities. Crude oil and gold, for example, are more reactive to market sentiment, whereas DEF contributes the most to the realized volatility of soybeans. Macroeconomic factors and geopolitical risks are more relevant to crude oil volatilities compare to the other two. Our empirical results also reveal the fact that the macro influence on the realized volatility of commodities is time varying.

Macro factors and the realized volatility of commodities: A dynamic network analysis

This paper explores the relationship between macro-factors and the realized volatility of commodity futures. Three main commodities-soybeans, gold and crude oil-are investigated using high-frequency data. For macro factors, we select six indicators including economic policy uncertainty (EPU), the economic surprise index (ESI), default spread (DEF), the investor sentiment index (SI), the volatility index (VIX), and the geopolitical risk index (GPR). These indicators represent three dimensions from macroeconomics and capital markets to a broader geopolitical dimension. Through establishing a dynamic connectedness network, we show how these macro factors contribute to the volatility fluctuations in commodity markets. The results demonstrate clearly distinctive features in the reaction to macro shocks across different commodities. Crude oil and gold, for example, are more reactive to market sentiment, whereas DEF contributes the most to the realized volatility of soybeans. Macroeconomic factors and geopolitical risks are more relevant to crude oil volatilities compare to the other two. Our empirical results also reveal the fact that the macro influence on the realized volatility of commodities is time varying.

Weighting bias and inflation in the time of COVID-19: evidence from Swiss transaction data

Sharp changes in consumer expenditure may bias inflation during the COVID-19 pandemic. Using public data from debit card transactions, I quantify these changes in consumer spending, update CPI basket weights and construct an alternative price index to measure the effect of the COVID-induced weighting bias on the Swiss consumer price index. I find that inflation was higher during the lock-down than suggested by CPI inflation. The annual inflation rate of the COVID price index was -0.4% by April 2020, compared to -1.1% of the equivalent CPI. Persistent "low-touch" consumer behavior can further lead to inflation being underestimated by more than a quarter of a percentage point until the end of 2020.

Estimation of nonlinear water-quality trends in high-frequency monitoring data

Recent advances in high-frequency water-quality sensors have enabled direct measurements of physical and chemical attributes in rivers and streams nearly continuously. Water-quality trends can be used to identify important watershed-scale changes driven by natural and anthropogenic influences. Statistical methods to estimate trends using high-frequency data are lacking. To address this gap, an evaluation of the generalized additive model (GAM) approach to test for trends in high-frequency data was conducted. Our proposed framework includes methods for handling serial correlation, trend estimation and slope-change detection, and trend interpretation at arithmetic scale for log-transformed variables. Water-temperature and turbidity data, representing two analytes with different temporal patterns, collected from the James River at Cartersville, Virginia, USA, were chosen for this analysis. Results indicated that the model, including flow, season, time covariates, and interaction between flow and season performed well for both analytes. The same model structure was applied to specific conductance data, collected from a small highly urbanized watershed, with satisfactory model performance. The water temperature GAM results indicated that the significant decreasing-then-increasing patterns after 2012 were mainly driven by air temperature changes. The turbidity trend was not significant over time. The specific conductance results showed a consistently upward trend over the last decade due to ever-increasing urbanization in the small watershed. This study suggests that the GAM method has great potential as a useful tool for trend analysis on high-frequency data, and for informing watershed managers of hydro-climatic and human influences on water quality by detecting crucial signal variation over time.

Functional exploratory data analysis for high-resolution measurements of urban particulate matter

In this work we propose the use of functional data analysis (FDA) to deal with a very large dataset of atmospheric aerosol size distribution resolved in both space and time. Data come from a mobile measurement platform in the town of Perugia (Central Italy). An OPC (Optical Particle Counter) is integrated on a cabin of the Minimetrò, an urban transportation system, that moves along a monorail on a line transect of the town. The OPC takes a sample of air every six seconds and counts the number of particles of urban aerosols with a diameter between 0.28 μm and 10 μm and classifies such particles into 21 size bins according to their diameter. Here, we adopt a 2D functional data representation for each of the 21 spatiotemporal series. In fact, space is unidimensional since it is measured as the distance on the monorail from the base station of the Minimetrò. FDA allows for a reduction of the dimensionality of each dataset and accounts for the high space-time resolution of the data. Functional cluster analysis is then performed to search for similarities among the 21 size channels in terms of their spatiotemporal pattern. Results provide a good classification of the 21 size bins into a relatively small number of groups (between three and four) according to the season of the year. Groups including coarser particles have more similar patterns, while those including finer particles show a more different behavior according to the period of the year. Such features are consistent with the physics of atmospheric aerosol and the highlighted patterns provide a very useful ground for prospective model-based studies.