Time-frequency causality and connectedness between international prices of energy, food, industry, agriculture and metals

Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution

“…This study analyses highly correlated markets in short- and long-run volatility spill overs following the approach of Elsayed et al., (2020) ; Tiwari et al., (2020) ; Tiwari et al., (2020) and Tiwari et al., (2020) . Giving that findings of prior studies investigating the association between different markets such as equities, commodities, bonds, and other financial asset classes are, in general, mixed and quite ambiguous because of the use of different methodologies based on different assumptions and analysis of different time scales ( Ewing and Malik, 2017 ; Corbet et al., 2019 ; Gil-Alana et al., 2020 ; Lucey and Li 2015 ; Ortas and Moneva 2013 ; Hachenberg and Schiereck 2018 ; Pham 2016 ), this study uses a time-series framework proposed as a methodology that comprises the estimation of DY (2012) and BK (2017) based spill over indices in a multivariate framework for various reasons.…”

Return connectedness among commodity and financial assets during the COVID-19 pandemic: Evidence from China and the US

“…In terms of research method, conditional correlation methods ( Balcilar and Ozdemir, 2013 ; Chen et al, 2020 ; Dua and Tuteja, 2016 ; El Ghini and Saidi, 2016 ; Lei et al, 2019 ; Liang et al, 2020a ; Liang et al, 2020b ; Öztek and Öcal, 2017 ; Tsuji, 2020 ; Wei et al, 2017 , 2020a , 2020b , 2021 ; Wright and Hirano, 2002 ; Zhang et al, 2019 , 2020 ; Zhang et al, 2019a,b ), Granger-causality ( Balboa et al, 2015 ; Massa and Rosellón, 2020 ; Papana et al, 2017 ; Woźniak, 2016 ; Yang et al, 2021 ), copula models ( Apergis et al, 2020 ; Boako et al, 2019 ; Kotkatvuori-Örnberg, 2016 ; Mensah and Premaratne, 2017 ; Rodriguez, 2007 ; Wen et al, 2012 ), and conditional value-at-risk ( Ji et al, 2018a , 2019 ; Li and Wei, 2018 ; Mensi et al, 2017 ; Reboredo and Ugolini, 2015 , 2016 ) are widely used to explore the characteristics of the information transmission between two variables/assets. Since Diebold and Yılmaz (2009 , 2012 , 2014) 's study which explore the connectedness across an asset system, a large body of literature realizes the importance of uncovering system spillovers in a large system ( Ji et al, 2018b ; Lundgren et al, 2018 ; Tiwari et al, 2020 ; Wang et al, 2020 ; Wei et al, 2019 ; Yoon et al, 2019 ; Zeng et al, 2019 ; Zhang, 2017 ). However, the dynamic connectedness network of Diebold and Yılmaz (2009 , 2012 , 2014) is measured using the rolling window method, implying that different settings of rolling-window sizes will result in unstable connectedness measures.…”

Dynamic Spillovers Between International Crude Oil Market and China's Commodity Sectors: Evidence From Time-Frequency Perspective of Stochastic Volatility

“…Crude oil prices are effective in transmitting shocks to other commodity markets (Choi and Hammoudeh, 2010;Nazlioglu et al, 2013;Ahmadi et al, 2016;Luo and Ji, 2018;Lovcha and Perez-Laborda, 2020). However, although literature has focused on the association between crude oil prices and a particular class of commodity (mostly energy or agricultural commodities), the findings are mixed and ambiguous in general, which may due to different modeling techniques and timescales (Tiwari et al, 2020). Moreover, the existing literature pays little attention to the connectedness between crude oil prices and various essential commodities.…”

“…Some empirical studies have investigated the volatility connectedness between crude oil price and commodities of a particular class or group, such as the agricultural commodities (Nazlioglu et al, 2013;Mensi et al, 2014;Wang et al, 2014;Luo and Ji, 2018), the energy commodities (Ng and Donker, 2013;Lovcha and Perez-Laborda, 2020), the precious metals commodities (Ewing and Malik, 2013;Bildirici and Turkmen, 2015), and the industries commodities (Choi and Hammoudeh, 2010). However, the results of the current literature examining the relationship between energy prices and other commodity prices are mixed and generally ambiguous, which may be due to the use of different models based on various assumptions and analysis of different timescales (Tiwari et al, 2020). Because different processes influence both the demand side and the supply side of commodities, various price movements are be observed in different commodity sectors (Balli et al, 2019;Ji et al, 2019).…”