Measurement and multifractal properties of short-term international capital flows in China

Ye, Ning; Wang, Yiming; Yang, Zhenyu; Geng, Yan

doi:10.1016/j.physa.2016.10.063

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…Furthermore, this technique allows measuring the random walk behavior, persistence and anti-persistence in financial data. The MF-DFA technique has been widely used to explore the complexity in financial markets such as stock markets (Onali and Goddard, 2009; Rizvi et al , 2014), exchange markets (Norouzzadeh and Rahmani, 2006; Wang et al , 2011), international capital flows (Ning et al , 2017) and crude oil markets (Cao et al , 2013; Alvarez-Ramirez et al , 2002).…”

Section: Methodsmentioning

confidence: 99%

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

Raza

Shah

Suleman

et al. 2021

IJHMA

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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.

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Section: Methodsmentioning

confidence: 99%

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

Raza

Shah

Suleman

et al. 2021

IJHMA

View full text Add to dashboard Cite

show abstract

“…According to Kantelhardt, Zschiegner, and Koscielny (2002), MF-DFA is an important method for quantitatively studying the multifractal behaviors of time series. Some recent studies applied this method in exploring the multifractal structures of different time series, such as the time series of stock prices (Anagnostidis, Varsakelis, & Emmanouilides, 2016;Barro & Urs ua, 2017;Chauhan, Kumar, & Pathak, 2017), foreign exchange rates (Norouzzadeh & Rahmani, 2006;Wang, Wu, & Pan, 2011), crude oil prices (Alvarez-Ramirez, Cisneros, Ibarra-Valdez, & Soriano, 2002;He & Chen, 2010) and international capital flows (Ning, Wang, Yang, & Geng, 2017). The multifractal properties in financial markets are usually caused by the long-range correlation or fat-tail distribution of the series of main market variables (Cao, Cao, & Xu, 2013;Deng, Hung, & Qiao, 2018;Du & Ning, 2008;Lu, Tian, Zhou, & Li, 2013;Onali & Goddard, 2009;Wang & Wang, 2018;Yeung & Lento, 2018).…”

Section: Mf-dfa Methodologymentioning

confidence: 99%

Nonlinearity and efficiency dynamics of foreign exchange markets: evidence from multifractality and volatility of major exchange rates

Han

Wang

2020

Economic Research-Ekonomska Istraživanja

Self Cite

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This study investigates the efficiencies of the exchange markets for four major currencies-the euro (EUR), the pound (GBP), the Canadian dollar (CAD) and the Japanese yen (JPY)-from 2005 to 2019 by using multifractal detrended fluctuation analysis (MF-DFA). This study also investigates the causes of these efficiencies. Significant multifractal properties are demonstrated by the four markets, and long-range correlation and fat-tail distribution properties are the main causes. We calculate and compare the multifractal degrees in three subsamples, which are classified based on their temporal relation to two economic events: the 2008 financial crisis and the announcement by the Federal Reserve of its withdrawal from the quantitative easing policy in 2014. Empirical results suggest that multifractal properties exist at different levels in the subsamples, thus showing that these events affect foreign exchange market efficiencies in terms of statistics and the fractal market. The JPY exchange market has the fewest multifractal properties, thus indicating that this exchange market has the highest market efficiency among these four exchange markets. The empirical results have implications for the nonlinear mechanism and efficiency in foreign exchange markets, which may help investors effectively manage market risks and benefit a stable global economy.

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“…Meanwhile, IMF raises forecast of GDP growth in U.S. and indicates Federal Reserve would raise the interest rate, which pushes capital flows out of China. Besides, Ning et al (2017) further explain that serious financial friction and information incompleteness of financial markets in China also result in inconsistency between the two variables.…”

Section: Empirical Data and Analysismentioning

confidence: 96%

“…Makhetha-Kosi et al (2016) discover that the positive interest rate differential in South Africa did not translate to proportionately higher capital inflows. Ning et al (2017) indicate emerging market economies lost a large amount of capital, which result from the 14 Zb. rad.…”

Section: Literature Reviewmentioning

confidence: 99%

Does the Mundell-Fleming model fit in China?

Wang¹,

Su²,

Ran³

2019

Zb. rad. Ekon. fak. Rij.

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This paper examines the causal relationship between interest rate differential (IRD) and international short-term capital flow (SCF) to determine whether such a relationship in China supports the Mundell-Fleming model. With structural changes existing, we find that long-run relationship using full-sample data is unstable, suggesting that causality test is not reliable. Consequently, we use a time-varying rolling-window approach to revisiting the dynamic causal relationship, and the results show that IRD has both positive and negative impacts on SCF in several sub-periods, but SCF has no effect on IRD in China. When China suffers external and internal shocks, SCF should not be curbed only by adjusting IRD. Therefore, it is critical for policymakers to pay attention to specific backgrounds (e.g. economic situation, monetary policies) and further employ interest rate and fiscal policies in reducing negative influence from SCF on the Chinese economy.

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Measurement and multifractal properties of short-term international capital flows in China

Cited by 9 publications

References 12 publications

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

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

Nonlinearity and efficiency dynamics of foreign exchange markets: evidence from multifractality and volatility of major exchange rates

Does the Mundell-Fleming model fit in China?

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