Price efficiency and safe-haven property of Bitcoin in relation to stocks in the pandemic era

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Natalia Diniz-Maganini ◽  
Abdul A. Rasheed
Keyword(s):  
Fluctuation Analysis ◽  
Safe Haven ◽  
Price Efficiency ◽  
Market Prices ◽  
Content Type ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Intraday Data ◽  
Country Index ◽  
Equity Index

Purpose When investors experience extreme uncertainty, they seek “safe havens” to reduce their risk, to limit their losses and to protect the value of their portfolios. The purpose of this paper is to examine the safe-haven properties of Bitcoin compared to the stock market. Design/methodology/approach Based on intraday data, this study compares the price efficiencies of Bitcoin and Morgan Stanley Capital Index (MSCI) using Multifractal Detrended Fluctuation Analysis for the second half of 2020. This study then evaluates Bitcoin’s safe-haven property using Detrended Partial-Cross-Correlation Analysis (DPCCA). Findings This study finds that the price efficiency of Bitcoin is lower than that of MSCI. Further, Bitcoin was not a safe haven at any time for the MSCI index. The net cross-correlations between Bitcoin and MSCI are weak and they vary at different time scales. Research limitations/implications The behavior of market prices varies over time. Therefore, it is important to replicate this study for other time periods. Social implications The paper sheds light on the price behavior of Bitcoin during a period of instability. The results suggest that the construction of portfolios should differ based on the time horizons of the investors. Originality/value The authors compare Bitcoin against a global equity index instead of a specific country index or commodity. They also demonstrate the applicability of DPCCA in finance research.

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.

scholarly journals Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis

2020 ◽  
Vol 13 (10) ◽  
pp. 248
Author(s):  
Ashok Chanabasangouda Patil ◽  
Shailesh Rastogi
Keyword(s):  
Long Memory ◽  
Cross Correlation ◽  
Structural Breaks ◽  
Primary Objective ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Price Series ◽  
Cross Correlation Analysis ◽  
Large Fluctuations ◽  
Secondary Objective

The primary objective of this paper is to assess the behavior of long memory in price, volume, and price-volume cross-correlation series across structural breaks. The secondary objective is to find the appropriate structural breaks in the price series. The structural breaks in the series are identified using the Bai and Perron procedure, and in each segment, Multifractal Detrended Fluctuation Analysis (MFDFA) and Multifractal Detrended Cross-Correlation Analysis (MFDCCA) are conducted to capture the long memory in each series. The price series is persistent in small fluctuations and anti-persistent in large fluctuations across all the structural segments. This confirms that long memory in the series is not affected by the structural breaks. Both volume and price-volume cross-correlation are anti-persistent in all the structural segments. In other words, volume acts as a carrier of the information only in the non-volatile (normal) market. The varying Hurst exponent across the structural segments indicates the varying levels of persistence and signifies the volatile market. The findings of the study are useful for understanding the practical implications of the Adaptive Market Hypothesis (AMH).

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.

scholarly journals Multifractal Detrended Cross-Correlation Analysis of Electricity and Carbon Markets in China

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Shaohui Zou ◽  
Tian Zhang
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Carbon Price ◽  
Research Area ◽  
Carbon Market ◽  
Fluctuation Analysis ◽  
Carbon Markets ◽  
Strong Negative Correlation ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Cross Correlation Analysis

With the development of carbon market, the complex dynamic relationship between electricity and carbon market has become the focus of energy research area. In this paper, we applied a new developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation and multifractality between electricity and carbon markets. We analyze the daily return of electricity and carbon prices over a period of 6 years to do the research. The results show that, firstly, we find that there is a strong negative correlation between domestic carbon price and electricity price and a significant cross-correlation between the return series of electricity and carbon markets. Secondly, through multifractal detrended fluctuation analysis, it is proven that there are obvious multifractal characteristics in the return series of electricity and carbon markets, and the results of traditional linear analysis are unreliable. We also find that, based on multifractal detrended cross-correlation analysis, the law cross-correlation between electricity and carbon markets exists significantly. The long-range correlation of small fluctuations and large fluctuations and the fat tail distribution of return series are the reasons for the formation of multifractality.

Investigating efficiency of frontier stock markets using multifractal detrended fluctuation analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Faheem Aslam ◽  
Paulo Ferreira ◽  
Wahbeeah Mohti
Keyword(s):  
Stock Returns ◽  
Long Range ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Content Type ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Multifractal Behavior ◽  
Detrended Fluctuation

PurposeThe investigation of the fractal nature of financial data has been growing in the literature. The purpose is to investigate the multifractal behavior of frontier markets using multifractal detrended fluctuation analysis (MFDFA).Design/methodology/approachThis study used daily closing prices of nine frontier stock markets up to 31-Aug-2020. A preliminary analysis reveals that these markets exhibit fat tails and clustering patterns. For a more robust analysis, a combination of Seasonal and Trend Decomposition using Loess (STL) and MFDFA has been employed. The former method is used to decompose daily stock returns, where later detected the long rang dependence in the series.FindingsThe results confirm varying degree of multifractality in frontier stock markets, implying that they exhibit long-range dependence. Based on these multifractality levels, Serbian and Romanian stock markets are the ones exhibiting least long-range dependence, while Slovenian and Mauritius stock markets indicating highest dependence in their series. Furthermore, the markets of Kenya, Morocco, Romania and Serbia exhibit mean reversion (anti-persistent) behavior while the remaining frontier markets show persistent behaviors.Practical implicationsThe information given by the detection of the fractal measure of data can support for investment and policymaking decisions.Originality/valueFrontier markets are of great potential from the perspective of international diversification. However, most of the research focused on other emerging and developed markets, especially in the context of multifractal analysis. This study combines the STL method and a physics-based robust technique, MFDFA to detect the multifractal behavior of frontier stock markets.

MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS BASED ON PSEUDO-BILINEAR FRACTAL INTERPOLATION FUNCTIONS ON METALLIC GLASSES

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850047
Author(s):  
QINGGE KONG ◽  
QING YU ◽  
MEIFENG DAI ◽  
YUE ZONG ◽  
XIAODONG WANG ◽  
...  
Keyword(s):  
Metallic Glasses ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Fluctuation Analysis ◽  
Fractal Interpolation ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Fractal Interpolation Functions ◽  
Detrended Fluctuation ◽  
Interpolation Functions

Based on the multifractal detrended cross-correlation analysis, which is the most effective way to detect long-range cross-correlation of time series, in this paper, we present a new method called multifractal detrended fluctuation analysis based on pseudo-bilinear fractal interpolation functions (MFDFA-PBFIF). In order to get a better detrended effect, we replace the polynomial fitting with PBFIFs in detrended process, and the result shows that the MFDFA-PBFIF can achieve a more accurate result. Then, we analyze the Legendre spectrum to detect the multifractal property on metallic glasses with MFDFA-PBFIF.

scholarly journals Multifractal Cross Correlation Analysis of Agro-Meteorological Datasets (Including Reference Evapotranspiration) of California, United States

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1116
Author(s):  
Adarsh Sankaran ◽  
Jaromir Krzyszczak ◽  
Piotr Baranowski ◽  
Archana Devarajan Sindhu ◽  
Nandhineekrishna Kumar ◽  
...  
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Reference Evapotranspiration ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Scaling Exponents ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
The Individual

The multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA were examined. The investigation of multifractality of datasets from stations with differing terrain conditions using the Multifractal Detrended Fluctuation Analysis (MFDFA) showed the existence of a long-term persistence and multifractality irrespective of the location. The scaling exponents of SR and T time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. The MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from weekly to inter-annual time scales. The multifractal exponents of P and U are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the MFCCA studies for agro-meteorological time series. The temporal evolution of cross-correlation determined by the MFCCA successfully captured the dynamics in the nature of associations in the P-ET0 link.

ECG classification using multifractal detrended moving average cross-correlation analysis

2021 ◽  
Author(s):  
Jian Wang ◽  
Wenjing Jiang ◽  
Yan Yan ◽  
Wenbing Chen ◽  
Junseok Kim
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Signal Sequence ◽  
Moving Average ◽  
Subsequent Treatment ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Ecg Signals ◽  
Cross Correlation Analysis

Accurate detection of arrhythmia signal types is of great significance for the early detection of heart disease and its subsequent treatment. The primary purpose of this study is to explore an electrocardiogram (ECG) classification system to improve its performance and achieve excellent computing performance, especially for large sample datasets. We classified ECG signals using the Hurst exponent, which is an ECG feature extracted by multifractal detrended moving average cross-correlation analysis (MF-XDMA). In addition, we used multifractal methods such as multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended moving average (MF-DMA) to extract the features of ECG signals, and we used a support vector machine (SVM) to classify the four types of feature data. The experimental results show that MF-XDMA-SVM has the best classification performance for atrial premature beat (APB) and bigeminy signals, which indicates that MF-XDMA-SVM is the most effective for the extraction of ECG signal sequence features among the four multifractal models.

Non-Linear Impact of Chinese Treasury Bond Futures on the Information Content of IRS

2021 ◽  
Author(s):  
Wei Yang ◽  
Qingsong Ruan ◽  
Linsen Yin
Keyword(s):  
Information Content ◽  
Cross Correlation ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Interest Rate Swap ◽  
Cross Correlation Analysis ◽  
Treasury Bond ◽  
Cross Correlations ◽  
The Impact ◽  
Bond Futures

This paper investigates the impact of Chinese Treasury bond (CTB) futures on the information content of interest rate swap (IRS) from a multifractality perspective. We first use multifractal detrended fluctuation analysis (MF-DFA) method and show that the swap rate and the CTB yield exhibit strong multifractality. In addition, employing multifractal detrended cross-correlation analysis (MF-DCCA) method, we find that cross-correlations between the swap rate and the CTB yield are multifractally persistent. Moreover, after the reintroduction of Treasury bond futures, the persistence of cross-correlation between the series is weaker. Our results indicate that the information content of IRS decreased after the re-launch of CTB futures.

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