Lag Relationship Research Articles

Time–Frequency Co-Movement of South African Asset Markets: Evidence from an MGARCH-ADCC Wavelet Analysis

The growing prominence of generating a well-diversified portfolio by holding securities from multi-asset markets has, over the years, drawn criticism. Various financial market events have caused asset markets to co-move, especially in emerging markets, which reduces portfolio diversification and enhances return losses. Consequently, this study examines the time–frequency co-movement of multi-asset classes in South Africa by using the Multivariate Generalized Autoregressive Conditional Heteroscedastic–Asymmetrical Dynamic Conditional Correlation (MGARCH-DCC) model, Maximal Overlap Discrete Wavelet Transformation (MODWT), and the Continuous Wavelet Transform (WTC) for the period 2007 to 2024. The findings demonstrate that the equity–bond, equity–property, equity–gold, bond–property, bond–gold, and property–gold markets depict asymmetrical time-varying correlations. Moreover, correlation in these asset pairs varies at investment periods (short-term, medium-term, and long-term), with historical events such as the 2007/2008 Global Financial Crisis (GFC) and the COVID-19 pandemic causing these asset pairs to co-move at different investment periods, which reduces diversification properties. The findings suggest that South African multi-asset markets co-move, affecting the diversification properties of holding multi-asset classes in a portfolio at different investment periods. Consequently, investors should consider the holding periods of each asset market pair in a portfolio as they dictate the level of portfolio diversification. Investors should also remember that there are lead–lag relationships and risk transmission between asset market pairs, enhancing portfolio volatility. This study assists investors in making more informed investment decisions and identifying optimal entry or exit points within South African multi-asset markets.

A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation

Electricity is one of the most important energy sources for the progress of human society. Photovoltaic power generation (PPG) has great potential as a renewable energy source to support sustainable development. However, the unpredictability of photovoltaics because of the uncertain weather conditions, time lags and the complex interactions among related factors may result in instabilities in PPG, which limits its applications in the electric power system. Therefore, accurately identifying the related factors and representing the interactions are the key issues in predicting PPG. For this purpose, a novel seasonal grey prediction model with time-lag and interactive effects, denoted as SGMLI(1,n), is developed to predict the capacity of photovoltaic power generation. More specifically, a time-lag driving term, a periodic driving term, and an interaction driving term are proposed to represent the lag relationship, periodicity, and the interactive effects between two factors, respectively. Utilizing the least square method and the Whale Optimization Algorithm (WOA), the optimal solution to the nonlinear programming problem is estimated. For elaboration and verification, six grey forecasting models, two statistical techniques, and two machine learning models, are employed for comparison with the SGMLI(1,n) model. Experimental results reveal that the SGMLI(1,n) outperforms benchmark models in forecasting PPG.

Enhanced coastal upwelling indices for Moroccan Atlantic Coast, their force–response variability and sensitivity to extreme events between 1993 and 2021

The Eastern Boundary Upwelling Systems (EBUS) are highly productive ecosystems globally, contributing approximately 20% of the world’s fishery resources and bearing significant ecological and socioeconomic importance. These EBUSs are primarily influenced and sustained by seasonal equatorward wind patterns. Along the Atlantic coast of Morocco, there are two distinct and persistently occurring upwelling patterns: a robust one to the south of (26°N) and a weaker one to the north. In this study, we present a coastal upwelling (transport) index based on combined contributions from both Ekman and Geostrophic transports, complemented by a recently refined thermal upwelling index based on sea surface temperature (SST). The seasonality patterns of these indices align with earlier research findings, revealing a sequence of wind-to-chlorophyll-to-SST lag relationship at all latitudes (24°N, 27°N and 32°N) during the period from 1993 to 2021. Subsequently, the two indices and Chlorophyll-a signature and three potential basin-scale driving factors, the North Atlantic Oscillation (NAO), the Azores High and the East Atlantic Oscillation (EAO) are analyzed to explore the interannual variability of the upwelling system and connect them to those of the prevailing wind patterns. Our analysis reveals three prominent periodicity bands: one near-decadal cycle spanning 8–10 years and two interannual cycles of 4–5 years and 2–3 years. It appears that the interannual bands are influenced by variations in the amplitude of the Azores high center pressure and are associated with the EAO, while the near-decadal cycles may be linked to the NAO. Furthermore, our examination of extreme events suggests that high (low) upwelling years tend to coincide with low (high) EAO years.

Social media-based implosion of Silicon Valley Bank and its domino effect on bank stock indices: evidence from advanced machine and deep learning algorithms

Social media-driven speculations play a crucial role in triggering the collapse of the banking system and stock markets. In this paper, we investigate the effect of Twitter-based investor sentiment on the collapse of Silicon Valley Bank (SVB), the 16th largest bank in the US. Additionally, we examine the spillover effect of the social media-based investor sentiment and SVB collapse on the bank stock indices from twelve countries where Global Systemically Important Banks (G-SIBs) operate. Advanced machine and deep learning models are employed to model the social media-based investors’ sentiment regarding SVB implosion and its spillover effect on the G-SIBs’ bank stock indices. Our results reveal that social media-based negative investors’ sentiment played an important role in SVB implosion. Our results further show that the negative investors’ sentiment persisted, and its systemic shock was transmitted to the G-SIBs bank stock indices. Importantly, our results provide a lead and lag relationship between investors’ sentiment and returns of G-SIBs bank stock indices. The findings of this study offer crucial insights for policymakers to consider the external shocks associated with social media-based investors’ sentiment when devising policies related to bank runs, thus helping to prevent future financial crises and cross-border contagion.

Characteristics of the East Asian Summer Monsoon Using GK2A Satellite Data

In East Asia, where concentrated summer precipitation often leads to climate disasters, understanding the factors that cause such extreme rainfall is crucial for effective forecasting and preparedness. The western North Pacific subtropical high (WNPSH) is a key driver of summer precipitation variability, and therefore, its monitoring is critical to predicting the wet or dry periods during the East Asian summer monsoon. Using the Geo-KOMPSAT 2A (GK2A) satellite cloud amount data and ERA5 reanalysis data during the years 2020–2023, this study identified three leading empirical orthogonal function (EOF) modes and investigated the associated WNPSH variability at synoptic and subseasonal scales. The analysis includes a linear regression of meteorological fields onto the principal component (PC) time series. All three modes play a role in the spatiotemporal variability of the WNPSH, exhibiting lead–lag relationships. In particular, the second mode is responsible for its northwestward shift and intensification. As the WNPSH moves northwestward, the position of the monsoon rain band also shifts, and its intensity is modulated mainly by the moisture transport along the WNPSH boundary. Our results highlight the potential of high-resolution, real-time data from the GK2A satellite to elucidate WNPSH variability and its impact on the East Asian summer monsoon. By addressing the variability of the WNSPH using GK2A data, we pave the way for the development of a real-time monitoring framework with GK2A, which will improve our predictability and readiness for extreme weather events in East Asia.

The effect of the COVID-19 pandemic on TOM in the housing market

Purpose The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations. Design/methodology/approach Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present. Findings This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated. Originality/value This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

The lead lag relationship between convertible bonds and stocks: a perspective based on trading mechanism

ABSTRACT Based on minute-level high-frequency data, this paper examines the lead-lag relationship between convertible bonds and the stock market by using the thermal optimal path method. It is found that convertible bonds lead the stock market at both index and individual levels. Regression analysis shows that the unique ‘T + 0’ trading mechanism and wider price limits of convertible bonds significantly contribute to their leading effect. Additional analysis shows that the leading effect is significantly reduced when the price limit of convertible bond market is narrowed. Our findings can potentially help regulators to improve and develop the convertible bond market, and also be of value to investors in developing trading strategies.

Drought Propagation and Recovery Behaviors Across 407 Australian Catchments

AbstractA reliable understanding of linkages between meteorological, hydrological and agricultural droughts (MD, HD, and AD respectively) is crucial to building resilience and planning for future climate changes. Despite Australia being prone to severe droughts, lagtimes of propagation (and recovery), from meteorological to hydrological and agricultural droughts across its large hydroclimatic regions, are poorly understood. Therefore, we investigate the characteristics of drought propagation and recovery time lags for droughts of four timescales and a combination of drought onset and cessation criteria in 407 unregulated catchments within six major precipitation zones across the country. We find that the propagation and recovery lags depend on climatic conditions, drought criteria and timescales. The median of catchment average propagation times from MD to HD across Australia varied from 0.8 to 1.7 months for 1‐month timescales, increasing to 2.2–5.0 months for 12‐month timescales. The corresponding recovery lagtimes were 1.3–3.7 and 1.7–7.0 months respectively. Similarly, the median of catchment average propagation times from MD to AD ranged from 0.8 to 1.9 months for 1‐month timescales, increasing to 0.6–5.0 months for 12‐month. The corresponding recovery lagtimes were 0.7–2.8 and 0.3–8.7 months respectively. For droughts of smaller timescales, propagation and recovery lags are linearly correlated with recovery lagtimes consistently greater than propagation times. However, as the timescale increases, these relationships weaken suggesting effects of other catchment attributes (e.g., groundwater contributions) on lag relationships.

Interacting effects of fire and hydroclimate on oak and beech community prevalence in the southern Great Lakes region

Abstract Rising temperatures, increasing hydroclimate variability and intensifying disturbance regimes increase the risk of rapid ecosystem conversions. We can leverage multi‐proxy records of past ecosystem transformations to understand their causes and ecosystem vulnerability to rapid change. Prior to Euro‐American settlement, northern Indiana was a mosaic of prairie, oak‐dominated forests/woodlands and beech‐dominated hardwood forests. This heterogeneity, combined with well‐documented but poorly understood past beech population declines, make this region ideal for studying the drivers of ecosystem transformations. Here, we present a new record from Story Lake, IN, with proxies for vegetation composition (pollen), fire (charcoal) and beech intrinsic water use efficiency (δ13C of beech pollen; δ13Cbeech). Multiple proxies from the same core enable clear establishment of lead–lag relationships. Additionally, δ13Cbeech enables direct comparisons between beech population abundance and physiological responses to changing environments. We compare Story Lake to a nearby lake‐level reconstruction and to pollen records from nearby Pretty and Appleman Lakes and the distal Spicer Lake, to test hypotheses about synchrony and the spatial scale of governing processes. The 11.7 ka sediment record from Story Lake indicates multiple conversions between beech‐hardwood forest and oak forest/woodland. Beech pollen abundances rapidly increased between 7.5 and 7.1 ka, while oak declined. Oak abundances increased after 4.6 ka and remained high until 2.8 ka, indicating replacement of mesic forests by oak forest/woodland. At 2.8 ka, beech abundances rapidly increased, indicating mesic forest reestablishment. Beech and oak abundances correlate with charcoal accumulation rates but beech abundance is not correlated with δ13Cbeech. Fluctuations in beech abundances are synchronous among Story, Appleman and Pretty Lakes, but asynchronous between Story and Spicer Lakes, suggesting regulation by local‐scale vegetation‐fire‐climate feedbacks and secondarily by regional‐scale drivers. Holocene forest composition and fire dynamics appear to be closely co‐regulated and may be affected by local to regional climate variations. The importance of extrinsic drivers and positive/negative feedbacks changes over time, with higher ecoclimate sensitivity before 2.8 ka and greater resilience afterwards. Synthesis: Overall, oak‐ and beech‐dominated ecosystems were highly dynamic over the Holocene, with multiple ecosystem conversions driven by shifting interactions among vegetation, hydroclimate and fire regime.

Effects of interaction of multiple large-scale atmospheric circulations on precipitation dynamics in China

Different scenarios of precipitation, that lead to such phenomena as droughts and floods are influenced by concurrent multiple teleconnection factors. However, the multivariate relationship between precipitation indices and teleconnection factors, including large-scale atmospheric circulations and sea surface temperature signals in China, is rarely explored. Understanding this relationship is crucial for drought early warning systems and effective response strategies. In this study, we comprehensively investigated the combined effects of multiple large-scale atmospheric circulation patterns on precipitation changes in China. Specifically, Pearson correlation analysis and Partial Wavelet Coherence (PWC) were used to identify the primary teleconnection factors influencing precipitation dynamics. Furthermore, we used the cross-wavelet method to elucidate the temporal lag and periodic relationships between multiple teleconnection factors and their interactions. Finally, the multiple wavelet coherence analysis method was used to identify the dominant two-factor and three-factor combinations shaping precipitation dynamics. This analysis facilitated the quantification and determination of interaction types and influencing pathways of teleconnection factors on precipitation dynamics, respectively. The results showed that: (1) the Atlantic Multidecadal Oscillation (AMO), EI Niño-Southern Oscillation (ENSO), East Asia Summer Monsoon (EASM), and Indian Ocean Dipole (IOD) were dominant teleconnection factors influencing Standardized Precipitation Index (SPI) dynamics; (2) significant correlation and leading or lagging relationships at different timescales generally existed for various teleconnection factors, where AMO was mainly leading the other factors with positive correlation, while ENSO and Southern Oscillation (SO) were mainly lagging behind other factors with prolonged correlations; and (3) the interactions between teleconnection factors were quantified into three types: enhancing, independent and offsetting effects. Specifically, the enhancing effect of two-factor combinations was stronger than the offsetting effect, where AMO + NAO (North Atlantic Oscillation) and AMO + AO (Atlantic Oscillation) had a larger distribution area in southern China. Conversely, the offsetting effect of three-factor combinations was more significant than that of the two-factor combinations, which was mainly distributed in northeast and northwest regions of China. This study sheds new light on the mechanisms of modulation and pathways of influencing various large-scale factors on seasonal precipitation dynamics.

Spillover effects, lead and lag relationships, and stable coins time series

This research explores plausible spillover effects among S&P 500 Index, stable coins, and selected cryptocurrency time series by examining observable lead and lag interrelationships among the series. Considering the heteroscedastic and “fat-tailed” nature of the data distributions underlying the empirical analyses, we employ quantile Granger Causality tests to improve the validity of our statistical findings. Our test results suggest that stable coins, USDT, and USDC offer diversification benefits by decreasing portfolio risk. The log returns of the S&P 500 Index, stable coins, Bitcoin, Ethereum, and Binance coins demonstrate clear bidirectional causality and spillover effects in low and high quantiles. Interestingly, however, stable coin and USDT returns strongly lead S&P 500 Index returns in nearly all quantiles for post COVID-19 time periods. These findings indirectly support intuition based upon market co-movement or integration assertions and suggest that investors can obtain added diversification benefits deriving from causality or spillover effects of holding stable coins when forming investment portfolios.

Nonstationarity in the NAO–Gulf Stream SST Front Interaction

Abstract The interaction between the North Atlantic Oscillation (NAO) and the latitudinal shifts of the Gulf Stream sea surface temperature front (GSF) has been the subject of extensive investigations. There are indications of nonstationarity in this interaction, but differences in the methodologies used in previous studies make it difficult to draw consistent conclusions. Furthermore, there is a lack of consensus on the key mechanisms underlying the response of the GSF to the NAO. This study assesses the possible nonstationarity in the NAO–GSF interaction and the mechanisms underlying this interaction during 1950–2020, using reanalysis data. Results show that the NAO and GSF indices covary on the decadal time scale but only during 1972–2018. A secondary peak in the NAO–GSF covariability emerges on multiannual time scales but only during 2005–15. The nonstationarity in the decadal NAO–GSF covariability is also manifested in variations in their lead–lag relationship. Indeed, the NAO tends to lead the GSF shifts by 3 years during 1972–90 and by 2 years during 1990–2018. The response of the GSF to the NAO at the decadal time scale can be interpreted as the joint effect of the fast response of wind-driven oceanic circulation, the response of deep oceanic circulation, and the propagation of Rossby waves. However, there is evidence of Rossby wave propagation only during 1972–90. Here it is suggested that the nonstationarity of Rossby wave propagation caused the time lag between the NAO and the GSF shifts on the decadal time scale to differ between the two time periods.

From black gold to financial fallout: Analyzing extreme risk spillovers in oil-exporting nations

Considering various critical periods including the COVID-19 pandemic and the ongoing Russian–Ukraine war, this paper investigates the dynamics of extreme spillover effects from the crude oil market to the financial markets of major oil-exporting countries. With the increased integration of global financial systems, oil market fluctuations can have far-reaching implications for economies that are heavily reliant on oil exports. We employ a wavelet approach to explore the co-movement and lead–lag relationships between the oil market and the financial markets of the considered countries. Next, we follow the newly introduced frequency-based connectedness approach of Hanif et al. (2023) to explore the dynamic connectedness and risk transmission among these markets. First, results from the wavelet coherency technique show that the degree of co-movement during the Russia–Ukraine war was significantly lower than it was under both the pre-crises and COVID-19 pandemic periods as shown by fewer regions with warmer colors (red), which show significant dependence at the 5% level, especially for Canada. Secondly, the dynamic connectedness of these markets was largely driven by long-term dynamics during the Russia–Ukraine crisis period, unlike the short-term driven connectedness observed during the COVID-19 pandemic. The average degree of connectedness at high frequencies (short-term) forms a smaller proportion of the average level of connectedness at low frequencies (long-term), indicating a stronger long-term influence of the crisis on the interconnectedness of these markets. Additionally, we find that Canada and the United States were the major net transmitters of shocks to the network during the conflict period, while Iraq exhibited the strongest level of idiosyncratic shocks. Interestingly, the crude oil market was observed to send stronger shocks to the network at the onset of the war, with the impact gradually diminishing as the conflict progressed. Our study provides valuable insights for policymakers and investors as a guide towards more informed decision-making and appropriate risk management strategies in the face of oil price volatility in these regions.

Diversity of Lagged Relationships in Global Means of Surface Temperatures and Radiative Budgets for CMIP6 piControl Simulations

Abstract Radiative feedbacks over interannual time scales can be potentially useful for global warming estimation. However, the diversity of the lead–lag relationships in global mean surface temperature (GMST) and net radiation flux at the top of the atmosphere (GMTOA) create uncertainty during the estimation of radiative feedbacks. In this study, key physical processes controlling lead–lag relationships were elucidated by categorizing preindustrial control simulations of CMIP6 into three groups based on cross correlation values of GMTOA against GMST at lag 0 and lag +1 year. The diversity in the lead–lag relationships was primarily caused by the climatological state difference of the atmosphere over the equatorial Pacific, which modulated the strength of convective activity and sensitivity of low-level clouds. Diminished atmospheric stability caused enhanced convective activity, more efficient energy release, and smaller lags. In addition, enhanced stability in the lower atmosphere rendered the low-level clouds more sensitive to sea surface temperature changes and considerably delayed the radiative response. The climatological state difference of the atmosphere resulted from model-inherent atmospheric conditions. These findings suggest that the diversity of lead–lag relationships of GMST and GMTOA over interannual time scales could represent the characteristics of general atmospheric circulation models and possible solutions of the actual atmosphere, which could also affect long-term feedback features.

The profitability of lead–lag arbitrage at high frequency

Any lead–lag effect in an asset pair implies that future returns on the lagging asset have the potential to be predicted from past and present prices of the leader, thus creating statistical arbitrage opportunities. We utilize robust lead–lag indicators to uncover the origin of price discovery, and we propose an econometric model exploiting that effect with level 1 data of limit order books (LOBs). We also develop a high-frequency trading strategy based on the model predictions to capture arbitrage opportunities. The framework is then evaluated on six months of DAX 30 cross-listed stocks’ LOB data obtained from three European exchanges in 2013: Xetra, Chi-X, and BATS. We show that a high-frequency trader can profit from lead–lag relationships because of predictability, even when trading costs, latency, and execution-related risks are considered.

Reconstructing GRACE-derived terrestrial water storage anomalies with in-situ groundwater level measurements and meteorological forcing data

Study regionNorth China Plain (NCP), China, a semi-arid region with intense groundwater withdrawals. Study focusThis paper developed a framework using meteorological data, model-simulated terrestrial water storage anomalies (TWSA), and additional in-situ (groundwater level, GL) data to improve the unsatisfactory GRACE-TWSA reconstruction in arid and semi-arid regions due to the intense anthropogenic influence on groundwater. The inconsistency between point-scale data (GL) and grid-scale data (GRACE-TWSA and predictors other than GL) is handled by feature extraction techniques. Moreover, to deal with temporal non-stationarity, the time series are separated into trend and detrended components, the patterns of which are further learned by linear and nonlinear machine learning models, respectively.New hydrological insights for the region: Multi-site GL observations in NCP can not only serve as validation data but also as predictors providing invaluable information on human effects for the reconstructed TWSA improvement (from 6.51 to 3.86 cm for Root Mean Square Error and from 0.56 to 0.82 for Nash-Sutcliffe Efficiency). Our results show that multi-site GL data in NCP are highly inter-correlated and can be represented by several principal components, demonstrating the strong hydraulic connectivity in NCP. We also find a significant one-month lag and linear relationship between the trends of GRACE-TWSA and GL changes in NCP. These deeper understandings of hydrologic processes have implications for enhancing the GRACE-TWSA estimations in other similar regions.

Forecasting European stock volatility: The role of the UK

This study investigates the lead–lag relationships of volatility among European stock markets. Using weakly realized variance measures, we examine volatility spillover dynamics between the UK and other major stock markets in Europe, thereby identifying a long-run leading role for the UK market portfolio. Lagged UK volatility can significantly predict volatilities in non-UK countries, whereas lagged non-UK volatility has a limited association with UK volatility. Moreover, pairwise Granger causality estimations, predictive regression specifications, and out-of-sample validations reveal that volatility shocks in the UK are gradually reflected in market fluctuations across Europe with varying market-specific delays. Our findings support the limited attention explanation for the volatility predictability of the lagged UK equity index.

Regional spillover of housing (un)affordability: an empirical study on the residential housing markets for first-time buyers in the U.K

ABSTRACT This study examines the lead–lag relationships between the levels of housing affordability of different regions of the U.K. By utilizing government data, a number of housing affordability indicators are constructed to explore whether spatial diffusion exists between different regional submarkets of first-time homebuyers over the period of 2000 – 2021. The results reveal that during periods of economic expansion, housing unaffordability tended to diffuse from regions of slower economic growth to regions of higher economic development. It is further evident that in the aftermath of the GFC, the London housing market Granger-caused other regional markets in terms of housing (un)affordability. Lastly, the U.K’.s decision to leave the EU in 2016 seems to have led to more divergence between the submarket regions, which in a Granger causal sense, have become less causally correlated in terms of pricing. We conjecture that the causal interactions between the different regional housing submarkets exist with the lead–lag relationships governed primarily by their underlying macroeconomic fundamentals.

Steering Potential for Printing Highly Aligned Discontinuous Fibre Composite Filament.

DcAFF (discontinuous aligned fibre filament) is a novel material for fused filament fabrication (FFF) 3D printing made of highly aligned discontinuous fibres produced using high performance discontinuous fibre (HiPerDiF) technology. It reinforces a thermoplastic matrix to provide high mechanical performance and formability. Accurate printing of DcAFF poses a challenge, especially for complex geometries, because: (i) there is a discrepancy between the path where the filament experiences the adhering pressure from the filleted nozzle and the nozzle path; and (ii) the rasters display poor adhesion to the build platform immediately after deposition, which causes the filament to be dragged when the printing direction changes. This paper explains the implication of these phenomena on steering capabilities and examines the techniques for improving DcAFF printing accuracy. In the first approach, the machine parameters were adjusted to improve the quality of the sharp turning angle without changing the desired path, but this showed insignificant effects in terms of precision improvements. In the second approach, a printing path modification with a compensation algorithm was introduced. The nature of the inaccuracy of the printing at the turning point was studied with a first-order lag relationship. Then the equation to describe the deposition raster inaccuracy was determined. A proportional-integral (PI) controller was added to the equation to calculate the nozzle movement in order to bring the raster back to the desired path. The applied compensation path is shown to give an accuracy improvement in curvilinear printing paths. This is particularly beneficial when printing larger circular diameter curvilinear printed parts. The developed printing approach can be applied with other fibre reinforced filaments to achieve complex geometries.

Powering Ghana’s future: unraveling the dynamics of electricity generation and the path to sustainable energy

This study investigates powering Ghana’s future: unraveling the dynamics of electricity generation and the path to sustainable energy by estimating endogenous parameters and employing an unrestricted Vector Autoregression (VAR) model. The model examines the linear lead–lag relationships between variables in the Ghanaian electricity sector and power consumption, using data from 2002 to 2021. The results reveal structural long-and-short-run headwinds for the unrestricted models and indicate that the growth rate of the Gross Domestic Product (GDP) and electricity from fossil fuels are directly correlated. Granger causality analysis highlights a feedback relationship between GDP growth rate and electricity from fossil fuel sources. The impulse response function reveals that the GDP growth rate is sensitive to exogenous shocks with lasting effects. Variance decomposition results show that renewable energy without hydropower explains a minimal variance due to shocks, while total global greenhouse emissions account for a significant proportion of the variance due to headwinds. Electricity from fossil fuel sources explains a substantial part of the variance due to headwinds, suggesting Ghana’s overreliance on conventional energy sources. The study forecasts that installed renewable energy capacity will experience considerable growth by 2036, accounting for most of the energy mix. To promote a sustainable energy future, the study recommends implementing fiscal instruments that incentivize renewable energy consumption, gradually diversifying the energy mix towards natural gas as a medium-term transition fuel for grid electricity generation and shifting entirely to renewables in the long time. This research contributes valuable insights into the dynamics of electricity generation in Ghana and provides policy recommendations for sustainable energy development.