Quantile Levels Research Articles

Connectedness for natural gas and Asian stock markets: time-frequency and cross-quantile approaches

ABSTRACT This study examines the interrelationship between 12 major Asian stock markets and the natural gas market using data from 4 January 2010, to 31 December 2023. We employ three primary methodologies: the Maximal Overlap Discrete Wavelet Transform (MODWT), the Cross-Quantilogram (CQ), and the TVP-VAR model to investigate the connectedness and spillover effects between these markets. Our findings indicate that the volatility of the natural gas market imparts positive predictability to Asian stock markets, though the specific extent of impact varies across time scales and quantile levels. Furthermore, results from the TVP-VAR analysis reveal significant spillovers within the network in the short term, with the natural gas market acting as a risk receiver. Specifically, risk spillovers occur in the Hong Kong Stock Exchange, the Taiwan Stock Exchange, the South Korea Stock Exchange, the Shanghai Stock Exchange, and the Singapore Stock Exchange, while other markets remain net risk receivers.

The Moderating Roles of Economic Complexity in the Entrepreneurship‐Sustainable Environment Nexus for the Gulf Cooperation Council Economies

ABSTRACTThere are conflicting views on the effect of business and entrepreneurial activities on environmental degradation in developing economies. However, none of this study examines whether economic complexity can serve as a policy tool for mitigating the effect of entrepreneurial business activities on a sustainable environment. Economic complexity consists of the structural and economic transformation process from a simple production system to a more complex and innovation‐based one. It predicts the variations in income level and its impact on the choice of goods being produced in an economy. This study examines the moderating effect of economic complexity on the link between entrepreneurship and sustainable environment in Gulf Cooperation Council (GCC) countries from 2006 to 2020. It further examines the validity of the Entrepreneurial Environmental Kuznets Curve (EEKC). The study uses the Driscoll‐Kraay standard error fixed effect, Panels Corrected Standard Errors (PCSE), method of moment quantile regression and Dumitrescu–Hurlin causality that are robust to heteroscedasticity, cross‐sectional dependency and other pitfalls of least square estimating technique. The results validate the inverted U‐shaped EEKC hypothesis across all the quantiles. Economic complexity increases ecological degradation at the lower quantile levels, while it decreases environmental footprint at the upper quantiles. Furthermore, economic complexity moderates the detrimental impact of entrepreneurial activity on the environment at the higher quantiles. A two‐way relationship is established between entrepreneurial activity and the environment, while one one‐way connection from economic complexity to the environment was found. The study recommends that policymakers should encourage innovative rather than necessity entrepreneurs. Entrepreneurs should be encouraged to engage in business activities that are friendly toward preserving the ecological environment, and green innovative activities should be prioritised in their entrepreneurial activities.

A new reading of the relationship between financial development, trade openness, vulnerability and economic growth in Africa: New perspectives from method of moment’s quantile regression

One of the most frequently debated subjects in international forums is economic growth, which is regarded as a global priority. Consequently, researchers have turned their attention from conventional economic growth at a single average coefficient to divisible economic growth at levels of its value. Although the existing literature has discussed several determinants of economic growth, our article contributes to examining the sources of economic growth in African countries during the generations of reforms from 1990 to 2019 and in the context of economic vulnerability. The variables used in the analysis are gross domestic product, trade openness, financial development, and economic vulnerability. The study uses a quantile regression econometric model to examine these variables at different stages of reform. Quantile regression (QR) estimates for quantiles 0.05 to 0.95 showed mixed results: financial development is favorable to African economic growth at all quantile levels. However, economic vulnerability is a major impediment to economic growth at all quantile levels. In addition, it was found that a high degree of trade openness has a detrimental effect on African economic growth from quantile 0.5 of the dependent variable. Finally, another important result proves that financial development is a remedy for decision-makers against economic vulnerability.

Analysis of Factors Affecting CO2 Emissions in Türkiye Using Quantile Regression

This study aims to show how the impact of factors on carbon dioxide (CO2) emissions differs at the quantile level and to demonstrate the superiority of the quantile regression method over the OLS method by using quantile regression and ordinary least squares (OLS) methods in order to examine the factors affecting CO2 emissions in Türkiye in depth. Covering the period 1990–2021, this study evaluates the relationship between CO2 emissions and GDP per capita growth, population growth, and renewable energy consumption. One of the important findings of the study is that the increase in the population ratio, which is insignificant according to the OLS method, positively affects CO2 emissions at the 0.25 quantile point. According to both OLS and quantile regression methods, GDP growth does not affect CO2 emissions, while renewable energy consumption has a significant and negative effect according to both models. These results demonstrate that economic growth has no discernible impact on CO2 emissions in Türkiye, while investments in renewable energy can significantly lower emissions and open the door for quantile regression to be used more widely in related research. Unlike traditional methods that focus only on the conditional mean, the quantile regression method provides a comprehensive framework for Türkiye’s sustainable development policies by exploring factor effects at different emission levels.

Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective

This study presents the first analysis of the nexus between the Southern Oscillation Index (SOI), a measure of climate risk, and segmented clean energy indices (such as solar, renewable, and bioenergy). Our research findings indicate that (i) the Granger quantile causality significance of SOI on segmented clean energy indices is asymmetric across different conditional quantiles. Significant predictability of SOI is observed only at the 0.25 and 0.75 quantile levels for all segmented clean energy indices, except for the WilderHill Clean Energy Index and NASDAQ OMX Fuel Cell Index. (ii) The clean energy market is significantly influenced by SOI under bullish market conditions. Impacts of SOI on all clean energy markets are nearly negligible when clean energy indices are at the median and lower quantile levels. (iii) The influence of strong La Niña episodes on segmented clean energy indices is more pronounced than during periods of intense El Niño phenomena. (iv) SOI exhibited a positive correlation at mid-term and long-term frequencies with segmented Clean Energy sectors, excluding bioenergy, for the majority of the sample period. Our conclusions provide deeper insights for investors managing clean energy investments in extreme climate conditions. Additionally, they offer useful information for policymakers to formulate viable economic policies addressing climate change, ensuring energy security, and facilitating a safer transition to clean energy.

Multivariate copula-based conditional quantiles: analytic higher-order moments and ratio estimation approaches

ABSTRACT In this paper, we aim to modify multivariate copula-based conditional quantiles for a targeted random variable, given multiple random variables attaining their respective quantiles. Specifically, we propose two modifications through the Cornish–Fisher expansion, one of which involves its analytic higher-order unconditional moments. The second one accounts for its higher-order conditional moments estimated using a ratio estimation method. By considering multivariate elliptical and Archimedean copulas with Johnson's SU margins, our simulation study shows that this method provides more accurate conditional moment estimators compared to the naive method. They result in expanded conditional quantile estimators, whose efficiency is relatively better than their unexpanded versions, particularly at lower and higher quantile levels under a stronger (tail) dependence assumption. A much higher efficiency is gained when the first modification is performed. These findings are validated using an empirical study based on cryptocurrency return data and a comparative analysis against the existing estimation approaches.

The Effects of IT and Financial Development on Economic Growth in BRICS-T Countries: Insights from the Solow Growth Model

The aim of the study is to examine the impact of IT and financial development on economic growth in BRICS-T countries for the period 1990-2022 using the panel quantile technique. For this purpose, IT technology and financial development variables were added to the basic Solow model. According to the findings, the impact of physical capital stock on economic growth is positive in all quantiles. The effect of population growth on economic growth is significant starting from 0.4 quantile and the negative effect increases as the quantile level increases. Information technology, which is the subject of the study, positively affects economic growth and this effect decreases as the quantile level increases. The variables we used to represent the level of financial development, financial institutions have a positive but statistically insignificant coefficient sign, while financial markets have a statistically significant but negative coefficient sign. This negative effect increases as the quantile level increases.

Quantile connectedness among fintech, carbon future, and energy markets: Implications for hedging and investment strategies

This study proposes a novel framework to explore the quantile connectedness among fintech, carbon future, and energy markets with implications for hedging and investment strategies. We aim to construct portfolio strategies for extreme market conditions based on the results of quantile connectedness to address the complex price fluctuations in the energy markets. Using the daily data from January 4, 2017, to March 6, 2023, we find that (i) carbon future and crude oil are the net receivers, while fintech and clean energy are the net contributors. However, the net spillover direction of fintech, carbon futures, and energy markets change frequently under extreme market conditions; (ii) the COVID-19 pandemic and Russia-Ukraine conflict significantly enhance connectedness across all quantile levels; (iii) the sample period is dominated by the lower quantile connectedness, indicating that investors react more strongly to bad news. Furthermore, the result of portfolios shows that, compared to normal market conditions, the dynamic investment weights of minimum connectedness (MCOP) exhibit greater volatility under extreme market conditions. This highlights the differences in portfolio adjustments across different market conditions. In addition, the result shows that the minimum connectedness and minimum correlation portfolio have a greater cumulative return than the minimum variance portfolio. The analysis based on the Sharpe ratio shows that in extreme market conditions, carbon futures and fintech can serve as effective hedging tools for the energy markets. Robustness tests further confirmed our conclusions. Our results have positive implications for decision-makers and investors about risk management and diversified portfolio selection.

Environmental transition dynamics under external conflict risk: New evidence from European countries

We investigate the dynamic effect of external conflict risk on environmental transition by considering the impacts of eco-innovation, economic growth, and energy consumption in a panel comprising 26 European countries from 2011 to 2020. Using a non-parametric model represented by the panel QARDL, we find that in the short run, a 1% increase in external conflict risk statistically leads to a 0.25% and 0.13% increase in the likelihood of environmental transition for the median and extreme upper quantiles (50th and 95th percentiles), respectively. However, the same increase holds little importance when considering the lower quantile (5th percentile). The indicated effect substantially decreases in the long run, reaching zero across the different quantiles. We also note discrepancies in the impact of energy across various quantile levels. For instance, the effect of population is significant from the 5th to the 90th quintiles. Overall, our findings suggest that enhancing eco-innovation helps reduce environmental transition risk. The results are relevant to the development of environmental policies and provide valuable insights for energy officials, technology stakeholders, and policymakers in their attempts to strengthen resilience during an environmental transition.

Unified specification tests in partially linear quantile regression models

We propose specification tests for parametric quantile regression models versus semiparametric alternatives over a continuum of quantile levels. The test statistics are constructed as continuous functionals of a quantile-marked residual process. We show that using an orthogonal projection on the tangent space of nuisance parameters at each quantile index delivers unified asymptotic properties for tests based on different estimators. Consistency of the tests and asymptotic power under a sequence of local alternatives converging to the null at a parametric rate are also discussed. We propose a simple multiplier bootstrap procedure to carry out the tests, whose nominal levels are well approximated in our simulation study for modest sample sizes.

Navigating median and extreme volatility in stock markets: Implications for portfolio strategies

This study explores the interdependencies among developed stock markets using the LASSO technique with quantile regression within a network analysis framework. Traditional forecasting methods often fail during volatile market conditions, necessitating innovative approaches that blend interconnectedness and factor modeling. By employing quantile regression, which examines financial assets across various distribution quantiles, this study addresses tail risk, a critical aspect of market behavior during crises. The network analysis framework provides insights into the relationships between stock markets, highlighting how variables interact within a complex system. The study assesses market behaviors at different quantile levels, considering clustering coefficients to analyze cycles, middlemen, ins, and outs. Additionally, it examines the impact of several factors on market interconnectedness, offering insights into the interplay of individual stocks with broader market conditions. Key findings demonstrate that incorporating interconnectedness factors into forecasting models enhances accuracy and informs better decision-making, leading to portfolios that can withstand extreme market conditions and provide superior risk-adjusted returns.

Frequency domain cross-quantile coherency and connectedness network of exchange rates: Evidence from ASEAN+3 countries

This study examines the frequency domain connectedness and synchronization between the exchange rates of Association of Southeast Asian Nations (ASEAN) member countries and those of China, Japan, and South Korea across quantile levels. We propose a quantile cross-spectrum of exchange rates to establish the coherency of connectedness and synchronization measurements. Our empirical results are as follows: First, the return connectedness between the exchange rates is heterogeneous, being stronger in the long run than in the short run and more pronounced under normal market conditions than under extreme market conditions. Second, the dynamic return connectedness among the exchange rates follows a similar trend in the monthly and yearly cycles. Third, exchange rate returns and volatility exhibit long-term synchronization. However, short-term heterogeneity persists across market conditions and investment horizons. Overall, these findings offer valuable insights for monetary authorities in their efforts to maintain exchange rate stability and for investors in making informed portfolio decisions.

Uncertainty estimation of machine learning spatial precipitation predictions from satellite data

Abstract Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostly novel even for the more general task of quantifying predictive uncertainty in spatial prediction settings. On 15 years of monthly data from over the contiguous United States (CONUS), we compared quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression neural networks (QRNN). Their ability to issue predictive precipitation quantiles at nine quantile levels (0.025, 0.050, 0.100, 0.250, 0.500, 0.750, 0.900, 0.950, 0.975), approximating the full probability distribution, was evaluated using quantile scoring functions and the quantile scoring rule. Predictors at a site were nearby values from two satellite precipitation retrievals, namely PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals), and the site’s elevation. The dependent variable was the monthly mean gauge precipitation. With respect to QR, LightGBM showed improved performance in terms of the quantile scoring rule by 11.10%, also surpassing QRF (7.96%), GRF (7.44%), GBM (4.64%) and QRNN (1.73%). Notably, LightGBM outperformed all random forest variants, the current standard in spatial prediction with machine learning. To conclude, we propose a suite of machine learning algorithms for estimating uncertainty in spatial data prediction, supported with a formal evaluation framework based on scoring functions and scoring rules.

Consumptive Behavior of Students Examined from the Perspective of E-Wallet Application Usage

The purpose of this research is to identify the consumptive behaviour of students from the perspective of e-wallet application usage. The research data is analyzed using the quantile regression method, which is a more specific regression method that can provide different estimator values at various quantile positions of the data. The respondents used in this study were 212 students. The results of the research show that the use of e-wallet applications is positively correlated with the consumptive behaviour of students. The relationship between the effect of using e-wallets and the consumptive behaviour of students varies at different quantile distribution levels. A larger regression coefficient value indicates a greater role of e-wallet usage in students' consumptive behaviour. The most significant role of e-wallet usage is seen at the 0.25, 0.75, 0.10, 0.50, and 0.90 quantiles in the distribution of students' consumptive behaviour. The variation in the results of this analysis demonstrates the flexibility of quantile regression and makes it more complex.

Does larger scale enhance carbon efficiency? Assessing the impact of corporate size on manufacturing carbon emission efficiency

This study investigates the impact of corporate scale on manufacturing corporate carbon efficiency (MCEE) using empirical analyses and diverse modeling techniques. The research begins with rigorous unit root and cointegration tests, confirming the stationary nature of the data and establishing long-term equilibrium relationships among the variables. Subsequently, benchmark regression analyses employing various models, including system GMM, reveal a robust and significantly positive association between corporate scale and MCEE. The findings emphasize that as corporate scale increases, there is a substantial enhancement in MCEE, attributed to expanded resources, technological advancements, and improved management systems. Robustness tests employing alternative approaches consistently reaffirm this positive correlation, further validating the pivotal role of corporate scale in augmenting MCEE. Furthermore, nonlinear analyses demonstrate varying impacts across different quantile levels of MCEE, highlighting the consistent positive correlation between corporate scale and MCEE. Additionally, the panel threshold model underscores the influence of environmental regulations, R&D investments, and trade openness on this relationship. Notably, stricter environmental regulations intensify the impact of corporate scale on MCEE, emphasizing the importance of corporate expansion in minimizing environmental costs and enhancing resource efficiency. These findings underscore the significance of corporate scale in driving corporate carbon efficiency. They advocate for corporations to not only expand their production capacities but also focus on optimizing management strategies and resource allocation, particularly in contexts influenced by environmental regulations, R&D investments, and trade openness. Overall, this research contributes comprehensive insights into understanding the dynamics governing corporate environmental efficiency and emphasizes the critical role of corporate scale in fostering environmental sustainability.

Nonlinear relationships between the participation index and key financial assets: A quantile regression analysis

The Participation Index is essential for assessing market sentiment, investor confidence, and financial market direction. Gold, known for its historical role in protecting wealth and providing a stable asset during economic instability, plays a key role in investment portfolios as an alternative guarantee against financial market fluctuations. The USD and EUR are key current currencies in global economics, affecting international trade, financial transactions, and investment decisions. The purpose of this study is to analyze the complex relationships between the Participation Index (KAT) and three significant financial assets: gold, the US dollar (USD), and the Euro (EUR) using Quantile Regression analysis. Daily data between 16.11.2021-08.08.2023 were used in this study. According to the results; the impact of the euro exchange rate on the "KAT" variable exhibits a positive relationship, which increases as the quantile level rises. The impact of the exchange rate of the United States Dollar (USD) on the variable defined as "KAT" indicates a positive relationship, which intensifies as the quantile level rises. The relationship between gold prices and the "KAT" variable shows a positive strengthening impact as the quantile level increases, and these effects demonstrate a high level of statistical significance.

Exploring the impact of digital economy on urban entrepreneurship: Evidence from China's cities.

This paper aims to examine the impact of the digital economy on urban entrepreneurship and its spatial spillover effects. To achieve this purpose, this research relies on data from 252 prefecture-level cities in China from 2012 to 2019. The findings demonstrate that the development of the digital economy has a positive influence on entrepreneurial activity in cities, with particularly effects observed robust at higher quantile levels. Additionally, the results suggest that urban entrepreneurial activity may be a siphoning effect, impeding entrepreneurship in neighboring cities. Furthermore, further investigation shows regional and policy heterogeneity.

Forecasting crude oil volatility and stock volatility: New evidence from the quantile autoregressive model

This paper employs the quantile autoregressive (QAR) model to examine the forecasting relationship between stock volatility and crude oil volatility. We first utilize the sup-Wald test to evaluate Granger causality across various quantile levels, providing valuable information for forecasting. Our findings reveal that the causal effects between stock volatility and crude oil volatility differ considerably across different quantiles, with a V-shaped relationship evident at the quantile level. Results from out-of-sample forecasts indicate that the forecasting effect of oil volatility on stock volatility has both positive and negative impacts. In contrast, when using stock volatility to forecast crude oil volatility, predictability improves relative to the benchmark, particularly at more extreme quantiles. Further analysis highlights the necessity of forecast combinations to achieve an overall improvement in forecasting tasks.

A censored quantile transformation model for Alzheimer’s Disease data with multiple functional covariates

Abstract Alzheimer’s disease (AD) is a progressive disease that starts from mild cognitive impairment and may eventually lead to irreversible memory loss. It is imperative to explore the risk factors associated with the conversion time to AD that is usually right-censored. Classical statistical models like mean regression and Cox models fail to quantify the impact of risk factors across different quantiles of a response distribution, and previous research has primarily focused on modelling a single functional covariate, possibly overlooking the interdependence among multiple functional covariates and other crucial features of the distribution. To address these issues, this paper proposes a multivariate functional censored quantile regression model based on dynamic power transformations, which relaxes the global linear assumption and provides more robustness and flexibility. Uniform consistency and weak convergence of the quantile process are established. Simulation studies suggest that the proposed method outperforms the existing approaches. Real data analysis shows the importance of both left and right hippocampal radial distance curves for predicting the conversion time to AD at different quantile levels.

Examining Quantiles in Structural Health Monitoring

A central task within structural health monitoring is to determine the condition of a given structure, such as bridges, by gathering and assessing data from integrated sensory systems. This data can be viewed as functional and subjected to corresponding statistical methodologies. Prompt detection of potential structural damage is crucial due to its potential impact on a structure's service life. One approach to address this challenge has been to use methods like (functional) principal component analysis ((F)PCA). FPCA identifies the primary directions of signal variability and monitors the evolution of corresponding scores over time. However, this conventional monitoring concept falls short in accounting for skewness or shifting effects, as it merely represents curves as deviations from the mean. A comprehensive analysis of the entire signals' distributions becomes imperative to tackle the multifaceted nature of structural damages and environmental influences adequately. In this innovative approach, we expand upon FPCA by incorporating a quantile perspective, where we model the significant variations in each sensor profile at various quantile levels. This empowers us to consider scores at different quantiles as vital monitoring metrics without imposing distributional assumptions on scores or error terms. Furthermore, our model takes into account confounding effects, particularly the temperature. We validate our method through simulation studies and real-data scenarios using strain and inclination data from long-term monitoring projects of German bridges. Our results demonstrate the effectiveness of our strategy as both a visual inspection tool and a reasonably accurate damage identification technique.