Modelling time and frequency connectedness among energy, agricultural raw materials and food markets

Adeleke, Adebowale Musefiu; Awodumi, Olabanji Benjamin Article Modelling time and frequency connectedness among energy, agricultural raw materials and food markets Journal of Applied Economics Provided in Cooperation with: University of CEMA, Buenos Aires Suggested Citation: Adeleke, Adebowale Musefiu; Awodumi, Olabanji Benjamin (2022) : Modelling time and frequency connectedness among energy, agricultural raw materials and food markets, Journal of Applied Economics, ISSN 1667-6726, Taylor & Francis, Abingdon, Vol. 25, Iss. 1, pp. 644-662, https://doi.org/10.1080/15140326.2022.2056300 This Version is available at: https://hdl.handle.net/10419/314179 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Applied Economics ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/recs20 Modelling time and frequency connectedness among energy, agricultural raw materials and food markets Musefiu Adebowale Adeleke & Olabanji Benjamin Awodumi To cite this article: Musefiu Adebowale Adeleke & Olabanji Benjamin Awodumi (2022) Modelling time and frequency connectedness among energy, agricultural raw materials and food markets, Journal of Applied Economics, 25:1, 644-662, DOI: 10.1080/15140326.2022.2056300 To link to this article: https://doi.org/10.1080/15140326.2022.2056300 © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 27 Apr 2022. Submit your article to this journal Article views: 1608 View related articles View Crossmark data Citing articles: 9 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=recs20 Modelling time and frequency connectedness among energy, agricultural raw materials and food markets Musefiu Adebowale Adeleke a and Olabanji Benjamin Awodumi b a Department of Economics, Faculty of Economics and Management Sciences of University of Ibadan, Ibadan, Nigeria; b Nigerian Institute of Social & Economic Research (NISER), Ibadan, Nigeria ABSTRACT The study analyzes volatility connectedness of energy, agricultural raw materials and food markets for both time and frequency domains (January 1960 to August 2020). The DY and BK approaches are adopted at both commodity-group and sub-group levels. Time domain estimates indicate that the energy market produced more risk spillover in the food market than raw material market. Rubber contributes the largest to spillover in the crude oil and sugar markets. Estimates from frequency domain reveal that raw material and food markets are net transmitter and net recipient of volatility spillover, respectively, at the lowest and highest frequency domains. Crude oil is the largest source of spillover in the tobacco, meat and natural gas markets in the high-frequency band. Finally, the meat and crude oil markets are the largest receiver of shock spillover from all other markets over the low- and high-frequency bands, respectively. Policy implications are derived from the findings. ARTICLE HISTORY Received 29 December 2020 Accepted 17 March 2022 KEYWORDS Energy; agricultural raw materials; food; spillover index; time and frequency connectedness 1. Introduction Energy has become a critical input in the production process of commodities, including agricultural products. Earlier view considered the influence of oil prices on the cost of production of agricultural commodities, such that as oil price rises, producing these commodities become costlier (Hanson, Robinson, & Schluter, 1993). The increased global attention on the need to control environmental pollution and shift to alternative energy source, especially from renewables, largely inform the recent dimension of the oilagricultural commodities link and connectedness. This development provides key ingredient into the current debate that a rise in oil price generates huge incentive for the production of biofuels as its demand rises for both domestic and commercial purposes (Wright, 2014). In the recent decades, the financialization of the commodity markets have been unprecedented with increased liquidity of commodity futures that has attracted huge investment from individual investors and organizations (Tiwari, Nasreen, Shahbaz, & Hammoudeh, 2019). This follows the globalization and increased integration of world market. Prior to the year 2000, prices of agricultural and energy commodities were relatively stable with noticeable upward trends in raw materials and food beginning from early 2000. This CONTACT Musefiu Adebowale Adeleke [email protected] University of Ibadan, Ibadan, Nigeria JOURNAL OF APPLIED ECONOMICS 2022, VOL. 25, NO. 1, 644–662 https://doi.org/10.1080/15140326.2022.2056300 © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. APPLIED ECONOMETRICS development has been largely attributed to the ability of biofuels to substitute oil and other fossil fuels, while investors consider it as a good hedge against inflation as well as unfavourable exchange rate movement. In addition, Lucotte (2016) pointed out the influence of the energy-agricultural commodity nexus on the fiscal and monetary policy, as well as external balance with important implication for economic stability. This implies that the connectedness among energy, agricultural raw materials and food are significant considerations for the management of risk, hedging and portfolio selection (Ciner, Gurdgiev, & Lucey, 2013; Rafiq & Bloch, 2016). Besides, the need for sound investment decisions as well as policy options requires a all stakeholders, including investors, regulators, policymakers and governments to better understanding the recent dynamics of the oil and agricultural commodity markets (Nazlioglu, Erdem, & Soytas, 2013). This study aims to investigate the time and frequency connectedness among energy, agricultural raw materials and food markets. Empirical literature remains inconclusive on this relationship. One strand of the literature argue that oil price surge is the main driver of the recent increase in the demand for food and raw materials. This is evident in Baffes (2007); Collins (2008); Yang, Qiu, Huang, & Rozelle (2008); Chang & Su (2010). Another strand of the literature provided no significant link between energy and agricultural markets (Gilbert, 2010; Zhang, Lohr, Escalante, & Wetzstein, 2010). The literature also shows that the energy-agricultural commodities nexus has been analyzed using different time horizons and econometric frameworks, while recent studies have also accommodated volatility of these markets. Most related studies either focus on the broad agricultural commodity groups (Tiwari et al., 2019) or simply selected agricultural products (Guhathakurta, Dash, & Maitra, 2019; Kang, Tiwari, Albulescu, & Yoon, 2019a; Nazlioglu et al., 2013). This study considers both the main agricultural commodity groups and selected individual commodities (within each group) for a robust analysis. It also isolates raw materials from food markets as they exhibit deferring characteristics in either feeding further production activities or used for final consumption, respectively. The Diebold and Yilmaz (2012) and the recent Baruník and Křehlík (2018) approach is adopted to measure the connectedness among energy, raw material and food markets for robustness of estimates. Baruník and Křehlík (2018) particularly allows measure of connectedness in the frequency domain and emphasizes that the frequency dynamics enables the study of the varying degree of persistence which emanates from shocks with a heterogeneous frequency (Tiwari et al., 2019). The rest of the paper is organized as follows. Following the introduction section, Section 2 reviews relevant literature on the connectedness of energy and commodity markets. Section 3 presents a detailed presentation of the methodology (DY, 2012 and BK, 2018) adopted in the study while empirical analysis and discussion of findings are contained in Section 4. Section 5 concludes the study with policy implication. 2. Literature review Connectedness among commodity markets continues to receive empirical attention, with relatively large number of studies on the spillover between oil and agricultural markets. Findings from these studies vary considerably depending on the approach or method of analysis. For instance, Barbaglia, Croux and Wilms (2019) employed vector auto regressive (VAR) model to show evidence of significant volatility spillover between agricultural JOURNAL OF APPLIED ECONOMICS 645 and energy commodities, with strong bidirectional connectivity between sugar and natural gas in the 2016 network. Similar strong volatility connectedness is found by Fasanya and Akinbowale (2019) between oil and food markets using the DY approach to analyze data spanning January 1997–June 2017. Using wavelet, DY and BK approaches, Tiwari et al. (2019) however demonstrated that food and fuel are net transmitter and recipient of volatility spillovers, respectively, in a connectedness system that include industrial input, agricultural input, and industrial metals. Accounting for the role of cryptocurrencies and metals, Qiang, Bahloul, Geng and Gupta (2019a) employed time-varying entropy-based approach to analyze information interdependence energy and agricultural commodities. Estimates indicate that sugar, crude oil and natural gas are net information receivers between daily data from August, 2015 to September, 2018. Using similar methods, Ji, Bouri, Roubaud and Kristoufek (2019b) confirms these findings after utilizing monthly data covering the period September 2008–December 2016. The same approach was adopted by Zhang and Broadstock (2018) to analyze daily data spanning 1982 (January)–2017 (June). Their results indicate that markets for crude oil and raw materials are net receivers of volatility spillovers, while the food market is a net transmitter. For these markets, they found higher connectedness during the post-global financial crisis period than the pre-crisis period. Connectedness among international crude oil and agriculture commodities was investigated by Kang et al. (2019a) between January 1990 and May 2017. Estimates from Baruník and Křehlík (2018) approach showed bi-directional and asymmetric connectedness between markets for oil and agriculture products at all different frequency bands. They also reported that sugar and meat are net recipients of volatility spillover while crude oil is a net transmitter. Diebold and Yilmaz (2014) approach of Guhathakurta et al. (2019) provided evidence that oil contributes highest to volatility in the market for sugar, but rubber is the least contributor to oil volatility between 13 March 1996 and 28 June 2018. They further revealed that rubber is a net recipient of risk spillover, while sugar and oil are net transmitter, with high volatility transfer in the period of boom and bust cycles. On the contrary to these submissions, weak volatility spillover have been discovered in a number of studies. This is evident in Luo and Ji (2018) who analyzed daily data spanning 2006–2015 period for the U.S. crude oil market and Chinese agricultural commodity market. Adopting vector HAR and Diebold and Yilmaz (2014) techniques, they further reveal higher market interdependence for negative volatility than positive volatility, while crude oil market is a net transmitter of shock. Using wavelet-based copula approach, Yahya, Oglend and Dahl (2019) found no strong differences in volatility connectedness between crude oil and agricultural commodities pre- and postcrisis. Strong external influence have also been reported to have significant implication on volatility connectedness between energy and agricultural markets. Bayesian analysis employed by Du, Yu and Hayes (2011) confirmed the volatility spillover between markets for oil and agricultural commodities, with strong external influence of factors such as ethanol production. Similar results were reported by Mensi, Hammoudeh, Nguyen and Yoon (2014) between energy and cereal markets using VARDCC-GARCH and VARBEKK-GARCH models. Shahzad, Hernandez, Al-Yahyaee and Jammazi (2018) adopted 646 M. A. ADELEKE AND O. B. AWODUMI standard Value-at-Risk (VaR) models and bivariate copular functions to establish asymmetry spillovers from oil to agricultural commodity markets, with stronger implication following financial crisis. Moreover, variance causality results of Nazlioglu et al. (2013) yielded similar volatility spillover from oil market to agricultural markets after the crisis period, while Wang, Zhang, Li, Chen and Wei (2019) showed that oil is a net receiver of return spillovers during financial stress, with connectedness increasing sharply with markets for wheat, copper and gold during crises. Hernandez, Shahzad, Uddin and H (2018) submitted the existence of positive effect of extreme low oil return quantiles on the lowest quantiles of agricultural commodities, which suggests that these commodities are poor diversifiers for oil during poor market conditions. Few studies however focussed on other commodity markets, but could not volatility spillovers to the markets for agricultural products. These provide links among markets for oil, gold and stock (Kang & Lee, 2019); heavy industrial metal, precious metal, oil and bond (Kang, Maitra, Dash, & Brooks, 2019b), energy, precious and industrial metals (An et al., 2020); and energy, stock, precious and industrial metals (Ahmed & Huo, 2020). The foregoing reveals that the connectedness of agricultural commodity markets with other markets such as those of energy and metal markets have received quite a number of research attention. However, these studies have largely focused on time domain, while the few ones that considered frequency domain either considered product aggregates (Tiwari et al., 2019), or a limited range of agricultural product (Wang et al 2019: Kang et al., 2019a). Also, the tobacco market has been ignored across all studies (See Table 1), creating a huge gap in the commodity connectedness literature. These are the gaps filled by the present study. 3. Methodology 3.1. Empirical methodology This study employed both time (Diebold & Yilmaz, 2012) and frequency (Baruník & Křehlík, 2018) connectedness approaches to measure the degree of association in volatility among global energy, agricultural raw materials, and food items at both aggregate and sub-product levels. The generalized forecast error variance decomposition (FEVD) within the time domain framework that assumes a stationary covariance variable of order (p) – VAR (p): At¼X R i¼1 φiAtiþεi(1) Where At is an N x 1 vector matrix of prices and in our case, it refers to the energy (Natural gas and crude oil), agricultural raw materials (rubber and tobacco) and food (beef meat and sugar), φi in this study assumed a 3 × 3 or 6 × 6 autoregressive coefficient matrices for aggregate and sub-level analysis, respectively. Also, where εi is a vector of residual with a common feature of zero mean and constant variance εi,0;σ2 ð Þ. Equation (1) can be re-specified following a moving average procedure as presented in equation (2) if the VAR process is stationary: JOURNAL OF APPLIED ECONOMICS 647 At¼X 1 j¼0 Bjεtj(2) Equation (2) form the basis for the derivation of variance decompositions necessary to obtain the spillover indexes. In the equation, where Bj is an N x M matrix that follows a recursive process such that Bj¼φ1Bj1þφ2Bj2þ. . . þφNBjN;where B0 is an identity matrix of an N x N dimension and Bj¼0forj <0:Therefore, the spillover indexes for net pairwise, directional, and total connectedness can be obtained following the FEVD approach. One major merit of employing this approach lies in its ability to exclude any error induced on the results by the ordering of the series. φu vðHÞu;v¼σ1 vv PH1 h¼0δpPÞu;v � �2 PH1 h¼0ðδpPδ0 pÞu;u (3) Equation (3) present the generalized form of FEVD. Where φu vðHÞu;v is the variance contribution of series v to variable u, δp is a square matrix corresponding to lag p, and σvv ¼P ð Þvv. In equation (3), cross-variable and own-variable contributions are contained in the off-diagonal and the main diagonal elements, respectively, of the φ (H) matrix, with the effect not summing up to one (1) within the column of φ (H). Thus, the connectedness measurement is then defined as the ratio between the sum of the offdiagonal elements and the sum of the whole matrix (Diebold & Yilmaz, 2012). CH¼100 1 Tr � φH � � P� φH ð Þu;v ! (4) Where Tr � φH � �, CH and � φH  �u;v are the matrix trace operator, the connectedness measure of the whole market, and the contribution of the v-th series of the market to the FEVD of the element u and � φH  �u;v¼ðφHÞu;v PN v¼1ðφHÞu;v with P n v¼1ð� φHÞu;v¼1and P n u;v¼1ð� φHÞu;v¼ N:Additionally, the directional spillovers received by market u from all the other markets v and vice-versa can also be measured. The net volatility spillovers from each market to all other market is obtained by taking the difference between the directional spillovers obtained from volatility received from all markets to direction spillovers obtained from volatility to market u. On the other approach, this study obtained the frequency domain (Baruník & Křehlík, 2018) from equation (3) in order to have a more detailed understanding of the connectedness among energy, agricultural raw materials, and food markets. Though, equation (4) is in the space of time domain-based impulse function ψh, Baruník and Křehlík (2018) changed the assumption to frequency reaction function of the form Ψcab  �¼ P h cabhΨh;by using the Fourier transform of the coefficients Ψ;withi ¼ffiffiffiffiffiffiffi 1 p. According to Baruník and Křehlík (2018), the generalized FEVD on frequency band w takes the following form; δw ð Þi;j¼0:5πcαiθð Þðf θð ÞÞi;jdθ(5) 648 M. A. ADELEKE AND O. B. AWODUMI Where αiθð Þ denote the weighting function stated in Baruník and Křehlík (2018). Using the spectral representation of the generalized FEVD, the frequency band connectedness on the frequency band w is then defined as; Cf w¼100 �Pi�j_ δw � �i;j P_ δ1 ð Þi;jTr _ δw n o P_ δ1 ð Þi;j 0 B @1 C A(6) Given the above equation (5) the overall connectedness within the frequency band w can be computed as; Ck w¼100 �1Tr _ δw n o P_ δw ð Þi;j 0 @1 A(7) In this paper, the volatility series is obtained from the general estimation of the GARCH (1, 1) model of the form ^ @2 t¼^ Qþ^ α^ m2 t1þ^ β^ @2 t1. 3.2. Data This study utilizes global monthly energy, agricultural raw materials and food price data that are available on consistent basis from January 1960 to August 2020. Two major levels of analysis were carried out. At the aggregate level, we utilize the price indices for energy (2010 = 100; which include coal, crude oil, natural gas and liquefied natural gas indices), agricultural raw materials (2010 = 100; which include timbers, rubber, tobacco and cotton indices) and food (2010 = 100; which include cereals, vegetable oils, and meals, sugar, bananas, beef, chicken and oranges indices). For the sub-group analysis, we use monthly data on crude oil and natural gas as both constitute 84.6 percent and 10.8 percent of data on energy index, respectively, while rubber (22.4%) and tobacco (13.9%) are used to proxy agricultural raw materials index. Last, monthly data on sugar (31.5%) and beef (22.0%) are used to proxy the food index. Explicitly, Table 2 summarizes the variables, measurement and source of data employed in this study. 4. Analysis and discussion of results 4.1. Preliminary analysis The trend and dynamic evolution of energy, raw material and food prices are presented in Figure 1. All the prices appear to fluctuate significantly for most of the period 1960– 2020. Common spikes are noticed in the early 1970s, early 1980, 2008–2009 and 2011. The jump in prices in the 1970s is followed by significant decline in food and raw material prices that may largely result from the period of economic stagnation across the Western world during the 1970s. This also results in relatively stable energy prices between 1973 and 1978 – witness some stable between on the eve of 1973 and 1974. The early 1980 recession resulted in the fall in market prices including energy and JOURNAL OF APPLIED ECONOMICS 649 Table 1. Summary of literature. S/ N Author Period Focus Market Estimation Technique Findings 1 Kang et al. (2019a) January 1990– May 2017 Oil and agricultural commodities DY and BK Bi-directional and asymmetric connectedness between oil and agriculture commodity markets 2 Qiang et al. (2019a) 2 September 2008– 27 December 2016 Agriculture, energy, metals and livestock DY Heating oil and gold are the net information transmitters. Crude oil is a net information receiver. 3 Ji et al. (2019b) 15 August 2015 – 27 September 2018 Energy, metals, agricultural commodities and cryptocurrencies Transfer entropy approach The least connected commodities are metals 4 Barbaglia et al. (2019) 3 January 2012– 28 October, 2016 Energy, agriculture and biofuel commodities Vector auto regressive Volatility spillovers exist between energy and biofuel (and agricultural commodities) 5 Guhathakurta et al. (2019) 13 March 1996– 28 June 2018 Oil, agro commodities and metals DY Oil is the highest transmitter of volatility to metal commodities. 6 Luo and Ji (2018) 3 January 2006 – 31 December 2015 U.S. Oil market and China’s agricultural commodity Vector HAR and DY Weak volatility spillover from the US crude oil market to Chinese agricultural commodity markets. 7 Zhang and Broadstock (2018) January 1982– June 2017 Oil, precious, industrial and agro commodities DY Metals are net transmitters while oil is a net receiver. 8 Wang et al (2019) 5 January 2000– 10 May 2019 Gold, wheat, oil and copper DY and BK Gold and oil are net receivers of return spillovers under financial stress. 9 Yahya et al. (2019) July 1986–June 2016 Oil and agricultural commodities Wavelet-based copula approach Similar connectedness between crude oil and agricultural commodities pre and post crisis 10 Fasanya and Akinbowale (2019) January 1997– June 2017 Oil and agricultural commodities DY Oil are net receivers of return spillovers 11 Mensi et al. (2014) 3 January 2000– 29 January 2013, Oil and cereals commodities VAR-BEKK-GARCH and VARDCCGARCH models Significant connectedness between energy and cereals markets 12 Du et al. (2011) 16 November 1998– 26 January 2009. Oil and agricultural commodities MCMC Strong volatility spillover between oil and agricultural commodities 13 Hernandez et al. (2018) 3 January 2000 to 9 August 2018 Oil, precious metal and agricultural commodities Crossquantilogram Precious metals and agricultural are poor diversifiers for oil 14 Nazlioglu et al. (2013) 1 January 1986– 21 March 2011 Oil and agricultural commodities Causality in variance Test Oil transmits volatility to agricultural commodity markets 15 Shahzad et al. (2018) 4 January 2000– 9 June 2017 Oil and agricultural commodities ARMA-GARCH and bivariate copula models Asymmetry spillovers from oil to agricultural commodities 16 Tiwari et al., 2019) January 1990– May 2017 Oil, food, metal, raw materials, beverage and industrial metals Wavelet, DY and BK Food and raw materials are net transmitters Note: DY = Diebold and Yilmaz (2012); BK = Baruník and Křehlík (2018); SUR = Seemingly unrelated regression technique; MCMC = Bayesian Markov Chain Monte Carlo 650 M. A. ADELEKE AND O. B. AWODUMI and sugar markets. This is followed by sugar and rubber, influenced by 3 and 2 other markets, respectively. The findings in the case of sugar supports those reported by Kang et al. (2019a). Aggregate Network Analysis DY, 2012 Bk, 2018 Frequency 1 Bk, 2018 Frequency 2 Bk, 2018 Frequency 3 Bk, 2018 Frequency 4 Sub-Sector Network Analysis DY, 2012 Bk, 2018 Frequency 1 Bk, 2018 Frequency 2 Bk, 2018 Frequency 3 Bk, 2018 Frequency 4 Figure 2. Network analysis of pairwise spillovers. Source: Authors estimation from pairwise spillovers using r software. Note: Frequency 1, 2, 3, and 4 refers to the 1–12 months, 12–24 months, 24– 48 months, and periods above 48 months accordingly. JOURNAL OF APPLIED ECONOMICS 657 Table 8. BK 2018 volatility spillover results (sub-commodity group). Natural Gas Crude Oil Meat Sugar Rubber Tobacco FROM_ABS FROM_WTH Frequency 1: Band 3.14 to 0.79 (1 − 12 months spillover) Natural Gas 3.93 0.51 0.03 0 0.04 0.05 0.11 2.46 Crude Oil 0.1 0.67 0.09 0 0.1 0.03 0.05 1.24 Meat 0.07 0.13 3.84 0.01 0.25 0.31 0.13 2.99 Sugar 0.01 0.02 0.11 12.65 0.28 0.04 0.08 1.83 Rubber 0.05 0.08 0.05 0.02 1.49 0.11 0.05 1.21 Tobbacco 0.07 0.11 0.02 0 0.22 0.17 0.07 1.62 TO_ABS 0.05 0.14 0.05 0.01 0.15 0.09 0.49 TO_WTH 1.17 3.27 1.17 0.15 3.47 2.1 11.35 Net −0.06 0.09 −0.08 −0.07 0.1 0.02 Net Recipient Net Transmitter Net Recipient Net Recipient Net Transmitter Net Transmitter Frequency 2: Band 0.79 to 0.31 (12 − 24 months spillover) Natural Gas 7.3 3.02 0.04 0.02 0.46 0.02 0.59 5.78 Crude Oil 1.5 4.61 0.37 0.06 0.63 0.21 0.46 4.51 Meat 0.11 0.26 8.34 0.1 0.06 1.46 0.33 3.25 Sugar 0.05 0.1 0.05 22.19 0.16 0.41 0.13 1.25 Rubber 0.36 0.92 0.08 0.31 6.9 0.52 0.36 3.55 Tobbacco 0.1 0.11 0.02 0 0.21 0.45 0.07 0.72 TO_ABS 0.35 0.74 0.09 0.08 0.25 0.44 1.95 TO_WTH 3.44 7.17 0.9 0.82 2.47 4.25 19.06 Net −0.24 0.28 −0.24 −0.05 −0.11 0.37 Net Recipient Net Transmitter Net Recipient Net Recipient Net Recipient Net Transmitter Frequency 3: Band 0.31 to 0.21 (24 − 48 months spillover) Natural Gas 2.63 1.77 0.03 0.03 0.15 0.01 0.33 9.07 Crude Oil 0.27 1.37 0.07 0.01 0.03 0.05 0.07 1.91 Meat 0.01 0.1 2.59 0.05 0.11 0.54 0.13 3.68 Sugar 0.02 0.06 0.11 8.21 0 0.1 0.05 1.32 Rubber 0.04 0.14 0.08 0.12 2.63 0.05 0.07 1.95 Tobbacco 0.09 0.1 0.01 0 0.07 0.26 0.05 1.27 TO_ABS 0.07 0.36 0.05 0.03 0.06 0.12 0.7 TO_WTH 1.91 9.92 1.37 0.94 1.65 3.42 19.21 Net −0.26 0.29 −0.08 −0.02 −0.01 0.07 Net Recipient Net Transmitter Net Recipient Net Recipient Net Recipient Net Transmitter Frequency 4: Band 0.21 to 0.00 (> 48 months spillover) Natural Gas 47.2 24.09 2.07 0.28 5.44 0.88 5.46 6.67 Crude Oil 13.87 22.29 8.46 0.21 43.21 1.81 11.26 13.76 (Continued) 658 M. A. ADELEKE AND O. B. AWODUMI Table 8. (Continued). Natural Gas Crude Oil Meat Sugar Rubber Tobacco FROM_ABS FROM_WTH Meat 4.9 20.1 20.39 0.12 19.92 16.24 10.21 12.48 Sugar 0.87 0.76 4.08 44.26 4.03 1.42 1.86 2.27 Rubber 5.74 2.09 7.5 1.15 66.96 2.62 3.18 3.89 Tobbacco 8.99 27.64 1.95 0.32 22.23 36.85 10.19 12.45 TO_ABS 5.73 12.45 4.01 0.35 15.8 3.83 42.16 TO_WTH 7 15.21 4.9 0.42 19.32 4.68 51.53 Net 0.27 1.19 −6.2 −1.51 12.62 −6.36 Net Transmitter Net Transmitter Net Recipient Net Recipient Net Transmitter Net Recipient JOURNAL OF APPLIED ECONOMICS 659 Overall results of both time and frequency domain spillover indices demonstrate that volatility spillover from raw materials, especially rubber, to oil and food markets is stronger than those from oil and food to raw materials. Also, volatility spillover increases from the short-run to the long-run. 6. Summary of findings and conclusion The study investigated volatility connectedness of energy, agricultural raw materials and food markets both at product group and individual commodity levels. Monthly data spanning January 1960 to August 2020 to estimate the short run to long-term frequency connectedness. The study employed the Diebold and Yilmaz (2012) as well as the frequency domain spillover index proposed by Baruník and Křehlík (2018). First, the main commodity groups were analysed using both methods. Then, two commodities each were selected for each group based on their composition in the commodity group index. Findings from the time domain aggregate level analysis shows that the energy market drives more volatility spillover in the food market than the raw material market. Energy market is found to be the least source of volatility spillover in the system, while raw materials market is the largest producer of risk spillover. Frequency domain estimates indicate that the food market is the leading cause of risk spillover in most of the frequency bands. Over the low- and high-frequency bands, the raw material and food markets are net transmitter and net recipient of volatility spillover, respectively, both in the low and high frequency bands. Energy market exhibit a status of net transmitter over the low-frequency band but net recipient over the medium to high-frequency bands. Thus, development in the energy market has important implication for food prices due to the various effects on the processing, transportation and distribution costs, which must be fully considered in the design of policies, initiatives and programmes relating to agricultural commodity development. According to the time domain individual commodity level estimates, rubber represents the largest cause of volatility spillover in the crude oil and sugar markets, while sugar market contributes the lowest to spillover other individual markets. Results from frequency domain analysis reveal a movement from weak to strong volatility connectedness over the low-to-high-frequency bands. Crude oil is discovered to be the largest source of volatility spillover in the markets for tobacco, meat and natural gas over the high-frequency band. Generally, the market for meat is the largest receiver of risk spillover from all other markets combined over the low-frequency band while crude oil market receives the largest shock in the high frequency. This may reflect the high degree of perishability of meat. In addition, while rubber market is the largest net transmitter of volatility spillover at all frequency bands, meat and tobacco are the largest net recipient. The findings provide key insights for portfolio allocation and hedging decisions, and for government in the quest to protect raw materials and food markets from risk spillover from other markets like oil market. Disclosure statement No potential conflict of interest was reported by the author(s). 660 M. A. ADELEKE AND O. B. AWODUMI Notes on contributors Musefiu Adebowale Adeleke (Ph.D) Olabanji Benjamin Awodumi (Ph.D) ORCID Musefiu Adebowale Adeleke http://orcid.org/0000-0003-4510-0526 Olabanji Benjamin Awodumi http://orcid.org/0000-0002-8152-2796 References Ahmed, A. D., & Huo, R. (2020). Volatility transmissions across international oil market, commodity futures and stock markets: Empirical evidence from China. Energy Economics, 2020. doi:10.1016/j.eneco.2020.104741 An, S., Gao, X., An, H., Liu, S., Sun, Q., & Jia, N. (2020). Dynamic volatility spillovers among bulk mineral commodities: A network method. Resources Policy, 66(101613), 1–12. Baffes, J. (2007). 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