scholarly journals ISLAMIC, GREEN, AND CONVENTIONAL CRYPTOCURRENCY MARKET EFFICIENCY DURING THE COVID-19 PANDEMIC

2021 ◽  
Vol 7 ◽  
Author(s):  
Emna Mnif ◽  
Anis Jarboui
Keyword(s):  
New Technologies ◽  
Efficiency Index ◽  
Fluctuation Analysis ◽  
Market Reactions ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Tangible Assets ◽  
Before And After ◽  
Stock Market Reactions ◽  
The Impact ◽  
Detrended Fluctuation

Unlike conventional cryptocurrencies, Islamic ones are new technologies backed by tangible assets and are characterised by their fundamental values. After the COVID-19 outbreak, cryptocurrency responses have shown different behaviour to stock market reactions. However, there is a lack of studies on the efficiency of Islamic and green cryptocurrencies during the pandemic. This paper attempts to analyse the behaviour of three typical families of cryptocurrencies (conventional, Islamic, and green) extracted according to their availability in daily frequencies during COVID-19. For this purpose, their efficiency levels are studied before and after the outbreak by employing multifractal detrended fluctuation analysis (MFDFA) to make the best predictions and strategies. The inefficiency of the cryptocurrencies is assessed through a magnitude of long-memory (MLM) efficiency index, and the impact of COVID-19 on their efficiency is evaluated. The primary results show that HelloGold was the most efficient market before the COVID-19 outbreak and that subsequently Ethereum has been the mostefficient. In addition, the findings reveal that the cryptocurrency reactions are not similar and show more resilience in the Ethereum and Litecoin markets than in other cryptocurrency markets. The main contribution of this study is the evaluation of the impact of COVID-19 on the various classes of crypto money. This work has practical implications, as it provides new insights into trading opportunities and market reactions. Moreover,  he work has theoretical implications based on its evaluation of three distinct models from different doctrine viewpoints.

scholarly journals COVID-19, bitcoin market efficiency, herd behaviour

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emna Mnif ◽  
Anis Jarboui
Keyword(s):  
Efficiency Index ◽  
Fluctuation Analysis ◽  
Hausdorff Topology ◽  
Content Type ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Nonlinear Dynamical ◽  
Generalized Hurst Exponent ◽  
Herd Behaviour ◽  
Detrended Fluctuation ◽  
Practical Implications

PurposeUnlike previous crisis where investors tend to put their assets in safe havens like gold, the recent coronavirus pandemic is characterised by an increase in the Bitcoin purchasing described as risk heaven. This paper aims to analyse the Bitcoin dynamics and the investor response by focusing on herd biases. Therefore, the main objective of this work is to study the degree of efficiency through multifractal analysis in order to detect herd behaviour leading to build the best predictions and strategies.Design/methodology/approachThis paper develops a novel methodology that detects the presence of herding biases and assesses the inefficiency of Bitcoin through an inefficiency index (MLM) by using statistical indicators defined by measures of persistence. This study, also, investigates the nonlinear dynamical properties of Bitcoin by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA) leading to deduce the effect of COVID-19 on the Bitcoin performance. Besides, this work performs an event study to capture abnormal changes created by COVID-19 related events capable to analyse the Bitcoin market response.FindingsThe empirical results of the generalized Hurst exponent GHE estimation indicates that Bitcoin is multifractal before this pandemic and becomes less fractal after the outbreak. Using an efficiency index (MLM), Bitcoin is found to be more efficient after the pandemic. Based on the Hausdorff topology, the authors showed that this pandemic has reduced the herd bias.Research limitations/implicationsThe uncertainty of COVID-19 disease and the lasting of its duration make it difficult to make the best prediction.Practical implicationsThe main contribution of this study is the evaluation of the Bitcoin value after the COVID19 outbreak. This work has practical implications as it provides new insights on trading opportunities and social reactions.Originality/valueTo the authors’ knowledge, this work represents the first study that analyses the Bitcoin response to different events related to COVID-19 and detects the presence of herding behaviour in such a crisis.

The Impact of Surgical Patent Ductus Arteriosus Closure on Autonomic Function in Premature Infants

2017 ◽  
Vol 34 (09) ◽  
pp. 874-878
Author(s):  
Marina Metzler ◽  
Vedavalli Govindan ◽  
Tareq Al-Shargabi ◽  
Dilip Nath ◽  
Anita Krishnan ◽  
...  
Keyword(s):  
Heart Rate ◽  
Patent Ductus Arteriosus ◽  
Premature Infants ◽  
Ductus Arteriosus ◽  
Fluctuation Analysis ◽  
Surgical Closure ◽  
Before And After ◽  
The Impact ◽  
Detrended Fluctuation ◽  
Patent Ductus

Background Patent ductus arteriosus (PDA) is a common complication of prematurity and a risk factor for poor outcome. Infants undergoing surgical PDA ligation are at highest risk for neurodevelopmental injury. Autonomic dysfunction has been described in premature infants with PDA. Aim To interrogate the autonomic nervous system by analysis of advanced heart rate variability (HRV) metrics before and after surgical closure of the PDA. Study Design Prospective, observational study. Subjects Twenty-seven infants born before 28 weeks' gestation were included in this study. Methods Continuous electrocardiogram data were sampled at a rate of 125 Hz for a total of 6 hours before and 6 hours after 30 hours of surgical closure. HRV was determined by detrended fluctuation analysis to calculate the short and long root mean square (RMSL and RMSS) and α components at two time scales (long and short). Results Gestational age (GA) was positively associated with RMSL, RMSS, and αS and was negatively associated with αL. There was no difference between RMSs, RMSL, αS, or αL before and after surgery; however, median heart rate was lower after surgery (p < 0.01). Conclusion Advancing GA is highly associated with increasing HRV; however, surgical ligation does not affect HRV in the postoperative period.

Multifractal detrended fluctuation analysis of temperature in Spain (1960–2019)

2021 ◽  
pp. 126118
Author(s):  
Javier Gómez-Gómez ◽  
Rafael Carmona-Cabezas ◽  
Ana B. Ariza-Villaverde ◽  
Eduardo Gutiérrez de Ravé ◽  
Francisco José Jiménez-Hornero
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

scholarly journals Gamification and Hazard Communication in Virtual Reality: A Qualitative Study

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4663
Author(s):  
Janaina Cavalcanti ◽  
Victor Valls ◽  
Manuel Contero ◽  
David Fonseca
Keyword(s):  
Virtual Reality ◽  
New Technologies ◽  
Training Environment ◽  
Hazard Communication ◽  
Head Mounted Display ◽  
Compliance Behavior ◽  
Before And After ◽  
Recorded Data ◽  
The Impact ◽  
Different Levels

An effective warning attracts attention, elicits knowledge, and enables compliance behavior. Game mechanics, which are directly linked to human desires, stand out as training, evaluation, and improvement tools. Immersive virtual reality (VR) facilitates training without risk to participants, evaluates the impact of an incorrect action/decision, and creates a smart training environment. The present study analyzes the user experience in a gamified virtual environment of risks using the HTC Vive head-mounted display. The game was developed in the Unreal game engine and consisted of a walk-through maze composed of evident dangers and different signaling variables while user action data were recorded. To demonstrate which aspects provide better interaction, experience, perception and memory, three different warning configurations (dynamic, static and smart) and two different levels of danger (low and high) were presented. To properly assess the impact of the experience, we conducted a survey about personality and knowledge before and after using the game. We proceeded with the qualitative approach by using questions in a bipolar laddering assessment that was compared with the recorded data during the game. The findings indicate that when users are engaged in VR, they tend to test the consequences of their actions rather than maintaining safety. The results also reveal that textual signal variables are not accessed when users are faced with the stress factor of time. Progress is needed in implementing new technologies for warnings and advance notifications to improve the evaluation of human behavior in virtual environments of high-risk surroundings.

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

ARRHYTHMIA DIAGNOSIS FROM ECG SIGNAL ANALYSIS USING STATISTICAL FEATURES AND NOVEL CLASSIFICATION METHOD

2021 ◽  
pp. 2150025
Author(s):  
SAURAV MANDAL ◽  
NABANITA SINHA
Keyword(s):  
Cardiac Arrhythmia ◽  
Present Model ◽  
Fluctuation Analysis ◽  
Statistical Features ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Ecg Signals ◽  
Proposed Model ◽  
Electrocardiogram Ecg ◽  
Detrended Fluctuation ◽  
Heart Condition

This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy.

A multifractal detrended fluctuation analysis of housing market: a role of financial crises in developed economies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Syed Ali Raza ◽  
Nida Shah ◽  
Muhammad Tahir Suleman ◽  
Md Al Mamun
Keyword(s):  
Financial Crisis ◽  
Housing Market ◽  
Market Efficiency ◽  
House Price ◽  
Fluctuation Analysis ◽  
Content Type ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Long Run ◽  
Before And After

Purpose This study aims to examine the house price fluctuations in G7 countries by using the multifractal detrended fluctuation analysis (MF-DFA) for the years 1970–2019. The study examined the market efficiency between the short-term and long-term in the full sample period, before and after the global financial crisis period. Design/methodology/approach This study uses the MF-DFA to analyze house price fluctuations. Findings The findings confirmed that the housing market series are multifractal. Furthermore, all the markets showed long-term persistence in both the short and long-term. The USA is identified as the most persistent house market in the short run and Japan in the long run. Moreover, in terms of efficiency, Canada is identified as the most efficient house market in the long run and the UK in the short run. Finally, the result of before and after the financial crisis period is consistent with the full sample result. Originality/value The contribution of this study in the literature is fourfold. This is the first study that has examined the house prices efficiency by using the MF-DFA technique given by Kantelhardt et al. (2002). Previously, the house market prices and efficiency has been investigated using generalized Hurst exponent (Liu et al., 2019), Quantile Regression Approach (Chae and Bera, 2019; Tiwari et al., 2019) but no study to the best of the knowledge has been done that has used the MF-DFA technique on the housing market. Second, this is the first study that has focused on the house markets of G7 countries. Third, this study explores the house market efficiency by dividing the market into two periods i.e. before and after the financial crisis. The study strives to investigate if the financial crisis determines the change in the degree of market efficiency or not. Finally, the study gives valuable insights to the investors that will help them in their investment decisions.

Sign in / Sign up

Export Citation Format

Share Document