Exploring the dynamic nonlinear relationship between crude oil price and implied volatility indices: A new perspective from MMV-MFDFA

Li, Shuping; Li, Jianfeng; Lu, Xinsheng; Sun, Yihong

doi:10.1016/j.physa.2022.127684

Cited by 11 publications

(7 citation statements)

References 58 publications

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“…They reveal that increasing OVX exhibits a larger negative influence on oil prices than declining OVX, indicating the existence of a long-run asymmetric cointegrating relationship between them. It is worth mentioning that this result of asymmetry is also recognized in previous findings [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. Furthermore, Shaikh [44] uses neural network and quantile approaches and suggests that crude oil prices are aligned with OVX.…”

Section: Literature Reviewsupporting

confidence: 69%

“…Nevertheless, they fail to ascertain its capability to ameliorate value-at-risk estimates. Li et al [43] explore the characteristics of the cross-correlations between the crude oil market and OVX. The findings reveal that the cross-correlated comportments of smaller changes are persistent, although large changes are anti-persistent between crude oil and OVX.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this regard, most studies examine the linear relationship between oil returns and OVX (e.g., [38][39][40]), whereas fewer consider the non-linear dependence. Agbeyegbe [41], Ji and Fan [27], Liu et al [21], and Echaust and Just [42] use approaches based on copula models; Li et al [43] apply a multiscale multivariate multifractal detrended fluctuation analysis; and Shaikh [44], Silva Junior [45], and Fousekis [46] use approaches based on quantile regression methods. Nevertheless, none of these studies assess the Granger causal relationship between crude oil returns and OVX based on quantilebased techniques accounting for various market conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Raggad

Bouri

2023

Mathematics

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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.

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Section: Literature Reviewsupporting

confidence: 69%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Raggad

Bouri

2023

Mathematics

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“…[19] extended the previous algorithm to the multiscale case through multivariate multifractal detrended fluctuation analysis (MMV-MFDFA), which is based on a moving fitting window that allows defining generalized dependent Hurst surfaces. An application of the above method can be found in [20]. However, the aforementioned methods are multivariate, due to their application to several variables measured at the same time.…”

Section: Introductionmentioning

confidence: 99%

Multi-Signal Multifractal Detrended Fluctuation Analysis for Uncertain Systems —Application to the Energy Consumption of Software Programs in Microcontrollers

de la Torre,

Pavón-Domínguez,

Dorronsoro

et al. 2023

Fractal Fract

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Uncertain systems are those wherein some variability is observed, meaning that different observations of the system will produce different measurements. Studying such systems demands the use of statistical methods over multiple measurements, which allows overcoming the uncertainty, based on the premise that a single measurement is not representative of the system’s behavior. In such cases, the current multifractal detrended fluctuation analysis (MFDFA) method cannot offer confident conclusions. This work presents multi-signal MFDFA (MS-MFDFA), a novel methodology for accurately characterizing uncertain systems using the MFDFA algorithm, which enables overcoming the uncertainty of the system by simultaneously considering a large set of signals. As a case study, we consider the problem of characterizing software (Sw) consumption. The difficulty of the problem mainly comes from the complexity of the interactions between Sw and hardware (Hw), as well as from the high uncertainty level of the consumption measurements, which are affected by concurrent Sw services, the Hw, and external factors such as ambient temperature. We apply MS-MFDFA to generate a signature of the Sw consumption profile, regardless of the execution time, the consumption levels, and uncertainty. Multiple consumption signals (or time series) are built from different Sw runs, obtaining a high frequency sampling of the instant input current for each of them while running the Sw. A benchmark of eight Sw programs for analysis is also proposed. Moreover, a fully functional application to automatically perform MS-MFDFA analysis has been made freely available. The results showed that the proposed methodology is a suitable approximation for the multifractal analysis of a large number of time series obtained from uncertain systems. Moreover, analysis of the multifractal properties showed that this approach was able to differentiate between the eight Sw programs studied, showing differences in the temporal scaling range where multifractal behavior is found.

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“…Many scholars use GARCH-BEKK model to study risk spillover ( 51 – 53 ). The MMV-MFDFA method which is newly proposed by Fan et al ( 54 ) measures the internal fluctuation of the system from the time and fluctuation dimensions, and it is also used in stock market ( 55 , 56 ).…”

Section: Introductionmentioning

confidence: 99%

Effect of COVID-19 on risk spillover between fintech and traditional financial industries

Zhou

2022

Front. Public Health

Self Cite

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COVID-19 has affected China's financial markets; accordingly, we investigate the effect of COVID-19 on the risk spillover between fintech and traditional financial industries. Using data from April 25, 2012 to April 22, 2022, which we divide into two parts (before and during the COVID-19 periods), we model the dynamic risk spillover relationship following the DCC-GARCH-BEKK and MMV-MFDFA methods. The results show that: (1) The dynamic relationship between fintech and traditional finance is almost positive most of the time, and the dynamic correlations between fintech and realty (real estate development and operation) are the largest. The dynamic linkage between fintech and traditional finance declines after the COVID-19 outbreak. (2) There exists a risk spillover from fintech to every type of bank before and during the COVID-19 periods. Notably, the risk spillover effect of fintech to large state-owned banks and city commercial banks is the largest separately before and during the COVID-19 periods. Meanwhile, there exist a two-way risk spillover between fintech and almost all other traditional financial industries before and during the COVID-19 periods. (3) Owing to the COVID-19 pandemic, the risk spillover relationship, which is in pairs and in the system become more complex. (4) Regarding the whole system, the correlation in the system is anti-persistent most of the time. Moreover, there are large fluctuations and more complex characteristics during the COVID-19 outbreak. However, the whole system was smooth most of the time before the outbreak of the COVID-19 pandemic.

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Exploring the dynamic nonlinear relationship between crude oil price and implied volatility indices: A new perspective from MMV-MFDFA

Cited by 11 publications

References 58 publications

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

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

Multi-Signal Multifractal Detrended Fluctuation Analysis for Uncertain Systems —Application to the Energy Consumption of Software Programs in Microcontrollers

Effect of COVID-19 on risk spillover between fintech and traditional financial industries

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