Time-dependent lead-lag relationship between the onshore and offshore Renminbi exchange rates

Xu, Hai-Chuan; Zhou, Wei‐Xing; Sornette, Didier

doi:10.1016/j.intfin.2017.05.001

Cited by 45 publications

(25 citation statements)

References 27 publications

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“…Therefore, the offshore Renminbi (CNH/USD), quoted also by the Swiss forex bank Dukascopy from July 2, 2012 is here used. There, in addition, already also exist quantitative studies documenting that the three RMB markets, including the third one existing, offshore RMB non-deliverable forward market (NDF market), largely cointegrate after the reform (Xu et al, 2017;Ruan et al, 2019). Collection of the corresponding charts illustrating price p(t) changes of all the instruments considered in the present study is depicted in Fig.…”

Section: Data Specificationmentioning

confidence: 94%

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

et al. 2019

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Statistical and multiscaling characteristics of WTI Crude Oil futures prices expressed in US dollar in relation to the most traded currencies as well as to gold futures and to the E-mini S&P500 futures prices on 5 min intra-day recordings in the period January 2012 -December 2017 are studied. It is shown that in most of the cases the tails of return distributions of the considered financial instruments follow the inverse cubic power law. The only exception is the Russian ruble for which the distribution tail is heavier and scales with the exponent close to 2. From the perspective of multiscaling the analysed time series reveal the multifractal organization with the left-sided asymmetry of the corresponding singularity spectra. Even more, all the considered financial instruments appear to be multifractally crosscorrelated with oil, especially on the level of medium-size fluctuations, as the multifractal cross-correlation analysis carried out by means of the multifractal cross-correlation analysis (MFCCA) and detrended cross-correlation coefficient ρ q show. The degree of such cross-correlations is however varying among the financial instruments. The strongest ties to the oil characterize currencies of the oil extracting countries. Strength of this multifractal coupling appears to depend also on the oil market trend. In the analysed time period the level of cross-correlations systematically increases during the bear phase on the oil market and it saturates after the trend reversal in 1st half of * Corresponding author Email address: marcin.watorek@ifj.edu.pl (Marcin Watorek) 2016. The same methodology is also applied to identify possible causal relations between considered observables. Searching for some related asymmetry in the information flow mediating cross-correlations indicates that it was the oil price that led the Russian ruble over the time period here considered rather than vice versa.

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Section: Data Specificationmentioning

confidence: 94%

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

et al. 2019

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“…According to (18), it can be obviously found that the lead-lag relationship between two time series X(t) and Y(t) changes in the five different time periods. There is zero time lag between X and Y in the first and fifth time periods.…”

Section: Comparison On Multiple-change-of-regime In Time Lagmentioning

confidence: 99%

“…The time series X is assumed to be the first-order AR process (17) and η is a Gaussian white noise with mean of 0 and variance of 0.1. Similarly to 2.3.1, by using existing TOP method and adjusted TOP method, we conduct the simulation tests on detection of lead-lag relationship between X and Y descried as (18) and present the results in Figure 2. Figure 2 shows the transverse trajectory 〈 ( )〉 as a function of the time step i based on the existing TOP method and adjusted TOP method.…”

Section: Comparison On Multiple-change-of-regime In Time Lagmentioning

confidence: 99%

“…(a) Thermal optimal average path (transverse trajectory 〈x(i)〉 as a function of the time step i) for model(18) at four different temperatures T = 0.2, 0.5, 1 and 2 based on the existing TOP method; (b) Average thermal path (transverse trajectory 〈 ( )〉 as a function of the time step i) for model(18) at four different temperatures T = 0.2, 0.5, 1 and 2 based on our adjusted TOP method with β = 30.…”

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confidence: 99%

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An Analysis of China’s Onshore and Offshore Exchange Rates—Adjusted Thermal Optimal Path Approach Based on Pruning and Path Segmentation

Yan

Lai

2019

Entropy

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The study of the lead-lag relationship between the Hong Kong offshore Renminbi (CNH) spot market and onshore (CNY) spot market is of great importance for its wide application in market risk management. In this paper, we study the correlation between the CNH and CNY spot markets in the contexts of daily closing price change and the 2011–2016 Bid-Ask spread (BAS). We test the existence of causality relation between CNH/CNY pairwise change and BAS by using the conventional method of vector auto-regression (VAR) model in the observation period. Furthermore, we detect the local lead-lag dependence relationships between CNH/CNY pairwise change and BAS by using a non-parametric approach-adjusted Thermal Optimal Path (TOP) method. Through introducing a Pruning and Path segmentation algorithm, we address the problem of computation infeasibility that may be encountered in application of the existing TOP method for the detection of lead-lag relationship between two time series with long time duration. Theoretical analyses and simulation results are presented to verify validity of adjusted TOP method in the setting of big time-series data set. This study also provides some interesting findings: (1) the offshore CNH market is informationally integrated with the onshore CNY market from two aspects of closing price change over two consecutive single days and BAS used as a proxy for market liquidity; (2) Local dependency between the two markets changes with economic conditions changing, which would facilitate both investor and policy maker decision making.

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“…To proxy for RMB foreign exchange rates, we employ the on-shore RMB-USD exchange spot rates (RU) and the on-shore RMB-JPY exchange spot rates (RY). For robustness check purpose, we also select the off-shore exchange spot rates between RMB and USD (OFFRU) and between RMB and JPY (OFFRY), because the existing literature shows evidence that the off-shore RMB exchange rates are much dependent on the on-shore rates (Funke et al 2015;Ren et al 2018;Xie et al 2017;Xu et al 2017). For stock market indices, we use the Tokyo Stock Exchange Price Index (TPX) and the Nihon Keizai Shimbun 225 (NIK) in Japan, and the Shanghai Composite Stock Market Index (SHCI) and the Shenzhen Composite Stock Market Index (SZCI) in China.…”

Section: Data and Descriptive Statisticsmentioning

confidence: 99%

RMB Exchange Rates and Volatility Spillover across Financial Markets in China and Japan

Qin

Zhang

2018

Risks

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This study examines empirically the volatility spillover effects between the RMB foreign exchange markets and the stock markets by employing daily returns of the Chinese RMB exchange rates and the stock markets in China and Japan during the period in 1998–2018. We find evidence that there exist co-volatility effects among the financial markets in China and Japan, and the volatility of RMB exchange rates contribute to the co-volatility spillovers across the financial markets. Reversely, the return shock from the stock markets can also generate co-volatility spillover to the foreign exchange markets. The bidirectional relationship reveals that both the fundamental hypothesis and the investor-induced hypothesis are valid. Our estimates also show that the spillover effects led by the stock market in Japan are stronger than that from the foreign exchange markets and the Chinese stock markets, implying that market with higher accessibility has greater spillover effects onto other markets. We also found that the average co-volatility spillover effects among the RMB exchange markets and the stock markets in Japan and China are generally negative. These findings have important policy implications for risk management and hedging strategies.

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Time-dependent lead-lag relationship between the onshore and offshore Renminbi exchange rates

Cited by 45 publications

References 27 publications

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

An Analysis of China’s Onshore and Offshore Exchange Rates—Adjusted Thermal Optimal Path Approach Based on Pruning and Path Segmentation

RMB Exchange Rates and Volatility Spillover across Financial Markets in China and Japan

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