Co-movements in commodity markets and
implications in diversification benefits

Cai, Xiao Jing; Fang, Zheng; Chang, Youngho; Tian, Shuairu; Hamori, Shigeyuki

doi:10.1007/s00181-018-1551-3

Cited by 21 publications

(9 citation statements)

References 48 publications

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“…It can be observed that the covariances are quite high among all the asset returns during the crisis period of 2019. These results are consistent with previous studies (Cai et al, 2020;Yang et al, 2017;Hamdi et al, 2019), which find a similar relationship among commodities and equity indices. Further, the covariances between the 3 year G-Sec and Auto and Bankex exhibit similar covariances among low frequency (0-16 weeks), medium frequency (16-32 weeks) and high frequency (>32 weeks) over the sample period.…”

Section: Portfolio Allocation Resultssupporting

confidence: 93%

“…In Figure 4, we present the results of the wavelet coherency approach for combinations of the assets. Wavelet analysis helps understand the dynamic nature of covariance among assets so that suitable assets can be identified for portfolio construction (Rua and Nunes, 2009;Cai et al, 2020;Yang et al, 2017;Conraria et al, 2008). The plots show the covariance between the assets in the time-frequency space and are shown by different colors.…”

Section: Portfolio Allocation Resultsmentioning

confidence: 99%

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Long-term dependency between sovereign bonds and sectoral indices of India: evidence using Hurst exponent and wavelet analysis

Das

Kumar

2021

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PurposeThe purpose of this study is to provide a new way to optimize a portfolio and to show that combining the Hurst exponent and wavelet analysis may help to increase portfolio returns.Design/methodology/approachThe authors use the Hurst exponent and wavelet analysis to study the long-term dependencies between sovereign bonds and sectoral indices of India. The authors further construct and evaluate the performance of three portfolios constructed on the basis of Hurst standard deviation (SD) – global minimum variance (GMV), most diversified portfolio (MDP) and equal risk contribution (ERC).FindingsThe authors find that an ERC portfolio generates positive superior return as compared other two. Since our sample includes periods of two crisis – post-2007 financial crisis and the ongoing pandemic, this study reveals that combining government bond with equities and gold provides a higher returns when the portfolios are constructed using the risk exposures of each asset in the overall portfolio risk.Practical implicationsThe findings provide guidance to portfolio managers by helping them to select assets using the Hurst approach and wavelet analysis thereby increasing the portfolio returns.Originality/valueIn this study, the authors use a combination of Hurst exponent and wavelet analysis to understand the long-term dependencies among various assets and provide a new methodology to optimize a portfolio. As far as the authors’ knowledge, no study in the past has attempted to provide a joint framework for portfolio optimization and therefore this study is the first to apply this methodology.

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Section: Portfolio Allocation Resultssupporting

confidence: 93%

Section: Portfolio Allocation Resultsmentioning

confidence: 99%

Long-term dependency between sovereign bonds and sectoral indices of India: evidence using Hurst exponent and wavelet analysis

Das

Kumar

2021

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“…However, for many economic variables, time domain analysis does not always portray the true picture of the relationship as it may be hidden in the frequency contents of variables. Wavelet analysis helps in studying the variable in more depth as it can decompose the time series (Gençay et al 2003;Yang et al 2018;Cai et al 2020).…”

Section: What Is Wavelet Transformation?mentioning

confidence: 99%

Investigating the Causal Linkages among Inflation, Interest Rate, and Economic Growth in Pakistan under the Influence of COVID-19 Pandemic: A Wavelet Transformation Approach

Hayat

Ghulam

Batool

et al. 2021

JRFM

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This research is the earliest attempt to understand the impact of inflation and the interest rate on output growth in the context of Pakistan using the wavelet transformation approach. For this study, we used monthly data on inflation, the interest rate, and industrial production from January 1991 to May 2020. The COVID-19 pandemic has affected economies around the world, especially in view of the measures taken by governmental authorities regarding enforced lockdowns and social distancing. Traditional studies empirically explored the relationship between these important macroeconomic variables only for the short run and long run. Firstly, we employed the autoregressive distributed lag (ARDL) cointegration test and two causality tests (Granger causality and Toda–Yamamoto) to check the cointegration properties and causal relationship among these variables, respectively. After confirming the long-run causality from the ARDL bound test, we decomposed the time series of growth, inflation, and the interest rate into different time scales using wavelet analysis which allows us to study the relationship among variables for the very short run, medium run, long run, and very long run. The continuous wavelet transform (CWT), the cross-wavelet transform (XWT), cross-wavelet coherence (WTC), and multi-scale Granger causality tests were used to investigate the co-movement and nature of the causality between inflation and growth and the interest rate and growth. The results of the wavelet and multi-scale Granger causality tests show that the causal relationship between these variables is not the same across all time horizons; rather, it is unidirectional in the short-run and medium-run but bi-directional in the long-run. Therefore, this study suggests that the central bank should try to maintain inflation and the interest rate at a low level in the short run and medium run instead of putting too much pressure on these variables in the long-run.

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“…Wavelet coherence technique has been widely used in other fields of studies, among others Madaleno and Pinho (2012) and Al Barghouthi, Qureshi, Ur Rehman, Shahzad, and Qureshi (2017) in the stock market, Kirikkaleli (2020) in political risk, Gallegati (2012) and Kirikkaleli and Athari (2020) in the financial market, Dewandaru et al (2014) in Islamic and conventional equity indices, Das, Kannadhasan, Al-Yahyaee, and Yoon (2018) in the gold market and Saiti, Ali, Abdullah, and Sajilan (2014), Pal andMitra (2017) Jiang, Lao, Mo, andNie (2018) and Cai, Fang, Chang, Tian, and Hamori (2020) in commodity market including vegetable oils markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Co-Movement among World Vegetable Oil Prices: A Wavelet-Based Analysis

Azam¹,

Sarmidi

Nor

et al. 2021

IJBS

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This paper examines the co-movement among major world vegetable oil prices (palm, soybean, rapeseed, and sunflower oil) by using wavelet-based analysis for a dataset ranging from January 2003 until March 2018. The wavelet approach is superior to the other techniques as it provides a flexible tool in investigating the changes in time and frequency domain simultaneously. The empirical result shows that the strong comovement among vegetable oils are driven by strong contagion and interdependence but incomplete vegetable oil market integration among major world vegetable oil. Interestingly, palm oil shows a lower degree of relationship with other edible oil in all scales after 2015. Meanwhile, more recent data shows that soybean and rapeseed oil have shown an increase in interdependence among each other. The study finds that multiple cross-correlations indicate that soybean oil is a potential leader of the vegetable oils market followed by palm oil primarily in the low scale period. This study on the time-frequency domain gives new insight for traders and investors in the vegetable oil market relating to the strategy for portfolio diversification and risk management.

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Co-movements in commodity markets and implications in diversification benefits

Cited by 21 publications

References 48 publications

Long-term dependency between sovereign bonds and sectoral indices of India: evidence using Hurst exponent and wavelet analysis

Long-term dependency between sovereign bonds and sectoral indices of India: evidence using Hurst exponent and wavelet analysis

Investigating the Causal Linkages among Inflation, Interest Rate, and Economic Growth in Pakistan under the Influence of COVID-19 Pandemic: A Wavelet Transformation Approach

Co-Movement among World Vegetable Oil Prices: A Wavelet-Based Analysis

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