Quantile Dependence Research Articles

Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants.

Quantile dependence of Bitcoin with clean and renewable energy stocks: new global evidence

ABSTRACT This paper examines the time-varying spillover effects and connectedness of the Bitcoin price with clean and renewable energy stocks using the quantile VAR framework. We use daily price indices spanning from 1 January 2014, to 18 October 2022. Before probing the quantile spillover effects between the markets examined, we first examine the mean-based averaged connectedness. These results indicate that Bitcoin receives more shocks from markets in the system than it transmits. Additionally, Bitcoin emerges as a net receiver of return shocks with index evolution among the markets examined, driven mainly by own shocks. Shifting to the results obtained using the QVAR approach, evidence reveals that Bitcoin acts as a net recipient of shocks under different quantiles in the system. In addition, Bitcoin returns strongly correlate with renewable energy stock returns under extreme events. We also confirm the dominance of renewable energy markets over Bitcoin and that the magnitude of their connectedness is time and event dependent. These findings confirm the diversification potential and safe-haven properties of Bitcoin for portfolio investors.

The Relationship Between Geopolitical Risks and Housing Returns in Türkiye: Evidence from the Cross - Quantilogram

The aim of the study is to analyze whether geopolitical risks have an effect on housing returns in Türkiye based on the data for the period January 2010 – September 2021. Considering its geography, Türkiye is one of the countries most likely to be exposed to geopolitical risks in the world. For this reason, from the point of view of both domestic and foreign investors investing in housing market, it is important to know whether this situation affects real estate returns, especially in periods when the growth in geopolitical risks is high and very high. For this purpose, in this study, the Cross-Quantilogram method introduced in the paper of Han et al.(2016), which is a robust measure of quantile dependence of two variables, was used. As a result of the analysis, it is seen that median and high-level geopolitical risks do not have an effect on housing prices or housing returns, but very high geopolitical risks have a short-term negative effect on returns. Based on all the findings, it is concluded that the geopolitical risks in Türkiye do not have a significant and permanent effect on the housing market.

Impact of Russia-Ukraine war attention on cryptocurrency: Evidence from quantile dependence analysis

Cryptocurrency values have recently fallen substantially. Why? We examine the impact of attention on the Russia-Ukraine War, proxied by Google Trends, on cryptocurrencies (and equity) to measure the returns coherences. Using quantile cross-spectral analysis and daily data for February 24, 2022–June 21, 2022, we observe that co-movements between War attention and cryptocurrencies depends on investment horizon and market state. Under bearish and normal (bull) markets, War attention negatively (positively) affects all cryptocurrencies in the short term. Results are consistent with cryptocurrency investors responding to War attention by seeking liquidity, with recent price falls explained as sell-offs by large holders.

Spatial Weighted Robust Clustering of Multivariate Time Series Based on Quantile Dependence With an Application to Mobility During COVID-19 Pandemic

In this article, a fuzzy clustering model for multivariate time series based on the quantile cross-spectral density and principal component analysis is extended by including: 1) a weighting system which assigns a weight to each principal component in accordance with its importance concerning the underlying clustering structure and 2) a penalization term allowing to take into account the spatial information. The iterative solutions of the new model, which employs the exponential distance in order to gain robustness against outlying series, are derived. A simulation study shows that the weighting system substantially enhances the effectiveness of the former approach. The behavior of the extended model in terms of the spatial penalization term is also analyzed. An application involving multivariate time series of mobility indicators concerning COVID-19 pandemic highlights the usefulness of the proposed technique.

How Is the ESG Reflected in European Financial Stability?

Environmental, social, and governance (ESG) factors are increasingly analysed to identify the risks and opportunities in contemporary economies. The banking sector influences the whole economy through the credit channel and balances its stability. The interplay of these elements motivated our main question, whether ESG scores impact European financial stability, measured for the banking sector. To this aim, we employ the cross-quantilogram methodology, which explores dependences at all levels of the distributions of two random variables. To determine the quantile dependence, we resort to methods of measuring systemic risk (Marginal Expected Shortfall—MES, CoVaR, and ΔCoVaR) for all commercial banks listed on European stock exchanges. While our approach provides a dashboard for analysis of the dependence of financial stability on ESG pillars, our findings indicate that such a connection is valid and cannot be identified with standard approaches that explore average distribution levels. We also document the differences in these impacts across the ESG pillars.

Tail risk transmission from commodity prices to sovereign risk of emerging economies

The aim of this paper is to examine the impact of large fluctuations in global commodity prices on the dynamics of sovereign risk for 18 emerging economies using a conditional quantile dependence approach and weekly data covering the period March 27, 2009–April 25, 2022. The results show the following: commodity prices and sovereign risk move in opposite directions. The impact of global commodity prices on sovereign risk differ across countries. Commodity price fluctuations have asymmetric effects on sovereign risk; for some emerging countries, large upward commodity price fluctuations reduce sovereign risk, whereas large downward commodity price movements have a limited impact. These results have potential implications for investors and policy makers regarding sovereign risk management decisions and the cost of funding of investment projects.

Spillovers and directional predictability between international energy commodities and their implications for optimal portfolio and hedging

This study sheds a new light on the dependence and the directional predictability between eight major energy price returns, using the Cross-Quantilogram (CQ) and the Partial CQ (PCQ) analysis. The energy prices cover the time series for the U.S. natural gas and seven internationally traded crude oil types. The results reveal a significant directional predictability running from most of energy commodities returns to the OPEC basket and the very light Tapis crude oil returns. However, the quantile predictability in both directions is enabled only for the relations between the light Brent and the light WTI, and between the OPEC basket and the Malaysian Tapis. The time-varying predictability analysis reveals that there is a significant upper quantile dependence between these international energy commodities. Finally, we find that the TAPIS can be a good hedging vehicle for other energy markets. These findings may be instructive for both policymakers (in terms of financial stability) and market participants (in terms of performance).

Multiscale dependence, spillovers, and connectedness between precious metals and currency markets: A hedge and safe-haven analysis

In this study, we examine the frequency volatility spillovers, connectedness, and quantile dependence between precious metals futures (gold, palladium, platinum, and silver) and the main US foreign exchange rates of Australia, Canada, China, Eurozone, Japan, Switzerland, and the UK. We use them as a hedging tool for developed currency markets. We further investigate the key drivers of frequency spillovers, before and during COVID-19, and the property of precious metals as a hedge and safe haven against currency risk exposure. We performed the quantile coherency analysis and found that the dependence is positive among the currencies and negative between currencies and precious material in most cases. Using the time-frequency spillovers analysis, we show that gold and silver are net transmitters of spillover to the other markets; however, palladium is a net receiver of spillover for all three horizons. The EUR is a net contributor of risk regardless of the time horizon. Using the quantile regression model, we demonstrate that the effect of the West Texas Intermediate crude oil price (WTI), Economic Policy Uncertainty (EPU) index, volatility index (VIX), and USD index (USDX) on total spillover varies over quantiles in terms of magnitude and direction. Finally, we show that all precious metals can be used as a strong hedge against these major currencies.

Does Economic Policy Uncertainty Affect Green Bond Markets? Evidence from Wavelet-Based Quantile Analysis

ABSTRACT This paper investigates the wavelet-based quantile dependence between Economic Policy Uncertainty (EPU) and green bond markets over 2014–2021. We first determine how the connectivity between EPU and green bonds differs across different investment horizons by decomposing EPU and green bond series into various frequency bands. Next, we provide a quantile-based framework to characterize the reliance between EPU and green bond markets across various market circumstances. Our findings show that the Granger causality from EPU to the green bond market is non-linear and varies across time scales. Our results benefit policymakers with a policy design to mitigate systematic volatility caused by external shocks in the green bond markets.

Oil shocks and BRIC markets: Evidence from extreme quantile approach

The present study aims to configure the extreme quantile dependence between oil shocks and BRIC markets from January 2, 1995 to July 27, 2021. Using the cross-quantilogram technique, the current study first decomposed oil shocks pertaining to demand and supply and analyzed their asymmetric impact on BRIC markets. Our findings manifest positive and persistent dependencies between oil demand shocks and BRIC markets. Meanwhile, substantial cross-quantile dependence is demonstrated among shocks in oil supply and the stock returns of Russia. The recursive cross-quantilogram analysis indicates time-varying characteristics reiterating that oil demand shocks are positively and significantly correlated with BRIC stock returns, particularly after the Global Financial Crisis and the financialization of energy commodities. However, weaker dependencies are observed in the normal market conditions in the absence of financial contagion. Finally, our results remain robust after controlling the impact of idiosyncratic risk shocks. Our findings are of particular prominence for BRIC markets, particularly in terms of oil-exporting (importing) status. Along with these, the findings carry substantial ramifications for policymakers, investors, and financial market constituents to restructure their current policies and strategies for avoiding uncertainty in the stock returns.

Frequency connectedness and cross-quantile dependence among medicare, medicine prices and health-tech equity

We investigate the dynamic connectedness among health-tech equity and medicine prices (producer and consumer) and Medicare cost indices for the US market. In doing so, we apply Cross-Quantilogram Dynamic Connectedness based on Time-Varying Parameter Vector Autoregression (TVP-VAR) approaches to analyse historical high-frequency time-series data. TVP-VAR results show that health-tech equity is the highest volatility transmitter while Medicare price is the highest volatility receiver. We also find medicine producer price is the net volatility contributor while the retail price of medicine is the net volatility receiver. The Cross-Quantilogram analysis confirms a strong bivariate quantile dependence between respective markets at a higher quantile of each market. Cross-quantilogram demonstrates a higher level of connectedness among the markets when considering medium and long memory. We observe health-tech equity turned to be a profound volatility contributor, while medicine price (both producer and retail prices) and Medicare appeared to net volatility receiver during the time of COVID19 Pandemic. The financial performance of health-tech equity returns elevates the price volatility of medicine and eventually Medicare cost, which imply that equity return should be incorporated forming medicine prices.

Measuring systemic risk in the global banking sector: A cross-quantilogram network approach

We propose a new systemic risk index based on the interdependence of extreme downside movements of stock returns using the cross-quantilogram and network analysis approach. While quantile dependence allows for sensitivity in times of market downturn, the topological network properties allow for capturing the interconnectedness of the banking system and identification of the specific contribution of each individual bank. Using this design, the proposed systemic risk index is not only easy to calculate and interpret but identifies the banking system's significant transmitters and receivers of extreme downside risk. For the empirical evaluation of the proposed risk index, we use a sample of 83 large banks during the 2003–2020 period, spanning multiple recent crises affecting the banking market. The proposed index is found to be robust in comparison to major alternative systemic risk measures.

Quantile Dependence and Portfolio Management between Oil, Gold, Silver and MENA Stock Markets

Quantile Dependence and Portfolio Management between Oil, Gold, Silver and MENA Stock Markets

Quantile-Specific Heritability of Mean Platelet Volume, Leukocyte Count, and Other Blood Cell Phenotypes

Introduction: “Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., mean platelet volume, MPV) is high or low relative to its distribution. Methods: Offspring-parent regression slopes (β_OP) were estimated by quantile regression, from which quantile-specific heritabilities (h²) were calculated (h² = 2β_OP/[1 + r_spouse]) for blood cell phenotypes in 3,929 parent-offspring pairs from the Framingham Heart Study. Results: Quantile-specific h² (±SE) increased with increasing percentiles of the offspring’s age- and sex-adjusted MPV distribution (p_linear = 0.0001): 0.48 ± 0.09 at the 10th, 0.53 ± 0.04 at the 25th, 0.70 ± 0.06 at the 50th, 0.74 ± 0.06 at the 75th, and 0.90 ± 0.12 at the 90th percentile. Quantile-specific h² also increased with increasing percentiles of the offspring’s white blood cell (WBC, p_linear = 0.002), monocyte (p_linear = 0.01), and eosinophil distributions (p_linear = 0.0005). In contrast, heritibilities of red blood cell (RBC) count, hematocrit (HCT), and hemoglobin (HGB) showed little evidence of quantile dependence. Quantile-dependent expressivity is consistent with gene-environment interactions reported by others, including (1) greater increases in WBC and PLT concentrations in subjects who are glutathione-S-transferase Mu1 (GSTM1) null homozygotes than GSTM1 sufficient when exposed to endotoxin; (2) significantly higher WBC count in AA homozygotes than carriers of the G-allele of the glutathione S-transferase P1 (GSTP1) rs1695 polymorphism at low but not high benzene exposure in shoe factory workers; (3) higher WBC counts in TT homozygotes than C-allele carriers of the interleukin-1β (IL1B) c.315C>T polymorphism after undergoing surgery for infective endocarditis but not before surgery. Discussion/Conclusion: Quantile-dependent expressivity may explain several purported gene-environment interactions involving blood cell phenotypes.

Nonlinear tail dependence between the housing and energy markets

This paper examines the quantile dependence between energy commodities (oil, coal, and natural gas) and the real housing returns of the nine US census divisions for the period 1991–2019. In contrast to the literature on the association between oil and housing markets, we contribute by studying the effect of additional commodities on the housing market returns. We use a cross-quantilogram and quantile regression approach and find regional variation in the impact of energy commodities on housing returns. The effect within the same region varies over the quantile distributions. In general, we observe that all energy commodities are negatively associated with real housing returns. Significant correlations are found more often when the oil and housing returns are in similar quantiles. Coal and natural gas show a stronger relationship with higher quantiles of housing returns. Further, the results for coal and natural gas remains relatively stable after controlling for macroeconomic variables.

The quantile dependence of the stock returns of “clean” and “dirty” firms on oil demand and supply shocks

The paper examines the degree to which firms' stock returns in the energy and technology sectors depend on oil demand and supply shocks by accounting for quantile dependence in shock transmission and causal linkages. Using monthly time-series data from January 2004 to December 2017, our evidence shows that the substitution between oil and clean commodities occurs only in the long-run when the oil market is subject to demand-driven shocks. Unlike oil demand shocks, we demonstrate that oil supply shocks display a relatively lower predictive power for the clean energy stock returns. We also report that firms in the dirty energy sectors display lower returns mainly due to their exposure to exogenous oil shocks, while firms in clean energy and technology sectors are more resilient to demand shocks as their exhibit positive returns in the long-run. Our findings provide evidence and guidance about investments opportunities in clean assets.

Projection quantile correlation and its use in high-dimensional grouped variable screening

In this paper, we propose a new measure, called Projection Quantile Correlation (PQC), to detect quantile dependence between a response and multivariate predictors at a given quantile level. The PQC measure is free of tuning parameters, robust against heavy tailed distributions and outliers, and does not require moment conditions on the predictors. We obtain theoretical properties of the PQC and its empirical counterpart. We then use the measure to select the grouped predictors that contribute to the conditional quantile of the response for high-dimensional data with group structures. We also establish sure independent screening properties for the group screening method. We illustrate the finite sample performance of the proposed method through simulations and an application to a dataset.

Quantile Dependence between Green Bonds, Stocks, Bitcoin, Commodities and Clean Energy

Quantile Dependence between Green Bonds, Stocks, Bitcoin, Commodities and Clean Energy

Quantile dependence between investor attention and cryptocurrency returns: evidence from time and frequency domain analyses

ABSTRACT This paper examines, in the time and frequency domains, the quantile dependence and directional predictability of investor attention to cryptocurrency returns. We find that there is significant tail dependence between investor attention and cryptocurrency returns. When market pays very high or very low attention to cryptocurrencies, there is an increased likelihood to have very large positive gains and suffer from very large negative results. Our results indicate that the quantile dependence between investor attention and cryptocurrency returns has a higher statistical significance in the long-term than medium-term and short-term. This implies that quantile dependence between investor attention and cryptocurrency returns is mainly dominated by low-frequency components. These findings have important implications for cryptocurrency investors.