New estimates of time-varying currency betas: A trivariate BEKK approach

Jayasinghe, Prabhath; Tsui, Albert K.; Zhang, Zhaoyong

doi:10.1016/j.econmod.2014.06.003

Cited by 24 publications

(13 citation statements)

References 39 publications

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“…Eq. (1) specifies in matrix form the returns process as a VAR(1) model, which is suggested by Iglesias-Casal et al (2020), Liu et al (2017), Yu et al (2019, Jayasinghe et al (2014), and Mensi et al (2014), among others and is frequently used to capture the linear interdependencies among returns in a system. VAR models generalize numerous univariate autoregressive (AR) models by allowing for more than one evolving variable.…”

Section: The Var(1) Asymmetric Bekk Mgarch(11)mentioning

confidence: 99%

Dynamic asymmetric optimal portfolio allocation between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies

2021

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Section: The Var(1) Asymmetric Bekk Mgarch(11)mentioning

confidence: 99%

Dynamic asymmetric optimal portfolio allocation between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies

2021

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“…The conditional variance consists of full matrix for A and B rather than diagonal matrices using the stochastic process derived in Equation (1). However, the full BEKK model is concerned with too many parameters and is less parsimonious that high computation is demanded in estimation Jayasinghe et al (2014), as it cannot be derived from stochastic processes. further suggested that unless assumption was in place, no regularity conditions exists for checking the internal consistency of the alternative models, hence no valid asymptotic properties of the quasi-maximum likelihood estimate (QMLE) of the associated parameters.…”

Section: The Diagonal Bekk Modelmentioning

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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“…The present literature on long memory can be found in various areas including stock market returns (Andersen et al, 2003;Caporale, Gil-Alana, & Plastun, 2017;Christensen et al, 2007;Hiremath & Kamaiah, 2010;Onour, 2010); currency exchange returns (Andersen & Bollerslev, 1997;Jayasinghe et al, 2014); real estate market returns (Wilson & Okunev, 1999;Stevenson, 2002) and futures market returns (Barkoulas et al, 1997;Kang et al, 2009), whereas limited work has been done in the area of agricultural commodities (Hyun-Joung, 2008;Mann et al, 2012). One of the primary motivations for this study is to better understand recent increases in world food price volatilities, especially wheat.…”

Section: Introductionmentioning

confidence: 99%

Modeling Long Range Dependence in Wheat Food Price Returns

Musunuru¹

2019

IJEF

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The present paper focuses on analyzing the volatility dynamics of wheat commodity based on the presence of long memory. The paper utilizes several econometric tests to identify the presence and magnitude of the fractional difference parameter. Fractional GARCH models, namely FIGARCH and FIEGARCH, are employed to examine the long memory property. Twenty years of wheat daily price data were used to study the long-range dependence. The results reveal that fractional integration is found in the daily wheat price return series. Overall, the FIGARCH model seems a better fit, in describing the time-varying volatility of the commodity adequately, compared to the FIEGARCH model. Food price shocks are likely to persist for a long time for wheat, resulting in higher market risk for producers and increased purchasing costs for consumers.

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New estimates of time-varying currency betas: A trivariate BEKK approach

Cited by 24 publications

References 39 publications

Dynamic asymmetric optimal portfolio allocation between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies

Dynamic asymmetric optimal portfolio allocation between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies

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

Modeling Long Range Dependence in Wheat Food Price Returns

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