A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission

Joshi, Prashant; Wang, Jinghua; Busler, Michael

doi:10.3390/jrfm15030116

Cited by 4 publications

(2 citation statements)

References 29 publications

(32 reference statements)

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“…There is little literature to leverage the ML algorithm for robustness checks in empirical research. Joshi et al (2022) are among the first to use ML to identify the robustness of asset returns and volatility connectedness. ML is recognised as the algorithm method for improving model predictability and accuracy (Bertomeu et al, 2021; Kellner et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

Ngene

Wang

2023

Accounting & Finance

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We evaluate asset returns and volatility connectedness using the time‐frequency connectedness model and machine learning approaches. Using 48 years of monthly indices of equity real estate investment trusts (EREITs), mortgage real estate investment trusts (MREITs), stocks, commodities, and bonds, we find that shocks to EREIT, and MREIT returns have a transitory impact on other assets. However, asset volatility connectedness among assets occurs at lower frequencies as markets slowly process pricing information. Therefore, shocks to EREIT and MREIT decay gradually and spill over to the other three assets for long periods. We also find that the intensity of the time‐frequency connectedness of returns and volatility varies with business cycles and significant domestic and global non‐recession events. We attribute the dominance of real estate investment trusts (REITs) in destabilising the financial system through long‐term volatility transmission to the heavy linkage of REITs to highly illiquid underlying direct real estate markets and high REITs leverage, making them more sensitive to real estate fundamentals, monetary shocks and macroeconomic risks than stocks and bonds. The result of the algorithm‐based machine learning model broadly supports the dominance of EREITs in the transmission of returns and volatility and shows that commodities have more explanatory power in the transmission of volatility than in returns. The empirical findings have implications for strategic and tactical asset allocation and policy design for market stability.

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Section: Resultsmentioning

confidence: 99%

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

Ngene

Wang

2023

Accounting & Finance

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“…In the meantime, a growing number of studies has been assessing the capabilities of cryptocurrency, such as being a diversifier, hedge, or safe-haven against financial assets during normal periods and times of global uncertainty (such as the COVID-19 pandemic), respectively. For instance, some studies suggest cryptocurrency acts as a diversifier (Briere et al 2015;Bouri et al 2017b;Maghyereh and Abdoh 2020;Zeng et al 2020;Corbet et al 2018;Kajtazi and Moro 2019;Gil-Alana et al 2020;Charfeddine et al 2020;Bakry et al 2021;Joshi et al 2022;Kumaran 2022). On the other hand, several studies show that cryptocurrency can offer new opportunities for a hedge investment (Dyhrberg 2016a(Dyhrberg , 2016bBouri et al 2017a;Baur et al 2018a;Demir et al 2018;Klein et al 2018;Guesmi et al 2019;Naeem et al 2020;Huynh et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Investigating the Co-Volatility Spillover Effects between Cryptocurrencies and Currencies at Different Natures of Risk Events

Hsu

2022

JRFM

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This paper examines and confirms the varying volatility of the relationship between cryptocurrency and currency markets at different time periods, such as when the market encountered multiple risk events including the US–China trade war, COVID-19, and the Russian–Ukraine war. We employ the Diagonal BEKK model and find that the co-volatility spillover effects between the returns of cryptocurrencies and currencies, with the exception of Tether and the U.S. dollar index, evolved significantly. Furthermore, the co-volatility spillover effects between cryptocurrencies and EUR have the largest effects and fluctuations. Large-cap cryptocurrencies (Bitcoin and Ethereum) have greater co-volatility spillover effects between them and currencies. Regarding the ability of cryptocurrencies to act as safe-haven for currencies, we observe that Bitcoin, Ethereum, and Tether served as safe-havens during the US–China trade war, and Bitcoin was a safe-haven during COVID-19. During the 2022 Russian–Ukraine war, Bitcoin and Tether were safe-havens. Interestingly, our findings point out that Bitcoin provides a more consistent safe-haven function for currency markets. Overall, by including multiple global risk events and a comprehensive dataset, the results support our conjecture (and earlier studies) indicating that the capabilities of cryptocurrency are time-varying and related to market status and risk events with different natures.

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Chinese stock market integration with developed world: A portfolio diversification analysis

Sher,

Haizhong,

Khan

et al. 2024

Heliyon

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A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission

Cited by 4 publications

References 29 publications

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

Investigating the Co-Volatility Spillover Effects between Cryptocurrencies and Currencies at Different Natures of Risk Events

Chinese stock market integration with developed world: A portfolio diversification analysis

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