COVID-19 pandemic and the crude oil market risk: Hedging options with non-energy financial innovations

Salisu, Afees A.; Obiora, Kingsley Article COVID-19 pandemic and the crude oil market risk: Hedging options with non-energy financial innovations Financial Innovation Provided in Cooperation with: Springer Nature Suggested Citation: Salisu, Afees A.; Obiora, Kingsley (2021) : COVID-19 pandemic and the crude oil market risk: Hedging options with non-energy financial innovations, Financial Innovation, ISSN 2199-4730, Springer, Heidelberg, Vol. 7, Iss. 1, pp. 1-19, https://doi.org/10.1186/s40854-021-00253-1 This Version is available at: https://hdl.handle.net/10419/237265 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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Salisu1* and Kingsley Obiora2 Introduction This study seeks to unravel the hedging effectiveness of financial innovations in nonenergy Exchange Traded Funds (ETFs) against oil price risks during COVID-19 pandemic. The research objective situates among numerous recent literature involving the connection between the current pandemic and the energy market (see, e.g., Apergis and Apergis 2020; Devpura and Narayan 2020a, b, c; Fu and Shen 2020; Gil-Alana and Monge 2020; Huang and Zeng 2020; Iyke 2020a; Liu etal. 2020; Narayan 2020a; Polemis and Soursou 2020; Prabheesh etal. 2020; Qin etal. 2020; Salisu and Adediran 2020). The widely held view is that the pandemic has impacted oil price negatively as lockdown Abstract This study examines the hedging effectiveness of financial innovations against crude oil investment risks, both before and during the COVID-19 pandemic. We focus on the non-energy exchange traded funds (ETFs) as proxies for financial innovations given the potential positive correlation between energy variants and crude oil proxies. We employ a multivariate volatility modeling framework that accounts for important statistical features of the non-energy ETFs and oil price series in the computation of optimal weights and optimal hedging ratios. Results show evidence of hedging effectiveness for the financial innovations against oil market risks, with higher hedging performance observed during the pandemic. Overall, we show that sectoral financial innovations provide resilient investment options. Therefore, we propose that including the ETFs in an investment portfolio containing oil could improve risk-adjusted returns, especially in similar financial crisis as witnessed during the pandemic. In essence, our results are useful for investors in the global oil market seeking to maximize risk-adjusted returns when making investment decisions. Moreover, by exploring the role of structural breaks in the multivariate volatility framework, our attempts at establishing robustness for the results reveal that ignoring the same may lead to wrong conclusions about the hedging effectiveness. Keywords: Pandemics, Financial innovations, Energy markets, Hedging, Optimal portfolio JEL Classification: I19, G15, G19, C52, G11 Open Access © The Author(s), 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. RESEARCH Salisuand Obiora Financ Innov (2021) 7:34 https://doi.org/10.1186/s40854‑021‑00253‑1 Financial Innovation *Correspondence: [email protected] 1 University of Ibadan Centre for Econometric and Allied Research, Ibadan, Oyo, Nigeria Full list of author information is available at the end of the article Page 2 of 19 Salisuand Obiora Financ Innov (2021) 7:34 measures at containing the virus have led to the shutdown of many companies. Meanwhile, the ensuing disruptions to global demand and supply chains have engendered irregular movements in energy prices (see also, Iyke and Ho 2020; Iyke 2020a). Although the motivation to hedge oil market risks is justified by studies suggesting the search for alternative hedging options for oil market risks (see Selmi etal. 2018; Olson etal. 2019; Sharma and Rodriguez 2019; Okorie and Lin 2020), the pandemic period offers yet greater motivation in this regard. This is because the crisis affecting the market becomes heightened with other markets (e.g., equities and currencies) that could be available to investors for diversification, which have also been impacted adversely by the pandemic(see Gil-Alana and Claudio-Quiroga 2020; Salisu etal. 2020a, b; Sharma 2020; Iyke 2020b; Narayan 2020b, c; Narayan etal. 2020).1 Therefore, this study contributes to the literature by exploring alternative hedging options for oil risks in financial innovations based on the ETFs, whose potential for hedging is increasingly gaining relevance in the literature. See arguments regarding the classes of financial innovations with low/negative correlations with most traditional portfolios and their potential risk-free nature qualifying them for hedging roles in Alexander and Barbosa (2008), Tari (2010), Agapova (2011), Gao (2012), Sharma and Rodriguez (2019), and Cheema etal. (2020). More specifically, many studies have discussed the strengths of ETFs as an important financial innovation and alternative investment assets (Agapova 2011; Gao 2012). More generally, financial innovations possess outstanding qualities; they are flexible investment options that offer risk-averse investors the prospect of holding a diversified basket of assets (although traded as single stocks as found in major global exchanges) without the need to trade in the physical assets defined in conventional investment portfolios (Dannhauser 2017; Marskz and Lechman 2018; Naeem etal. 2020; Ozdurak and Ulusoy 2020; Sakarya and Ekinci 2020). We approach the contribution of the study by focusing on financial innovations in non-energy ETFs because we are interested in evaluating the hedging powers for oil price risk. Therefore, the energy components are isolated as the conventional wisdom in the literature; that is, investment assets in the same market/sector are believed to be positively correlated, and therefore, one cannot serve as a good hedge against another because both move in the same direction (see also, El-Sharif etal. 2005; Naeem etal. 2020; Ozdurak and Ulusoy 2020). For instance, Fig.1 in the appendix depicts positive co-movements between energy sector financial innovations and the WTI oil price in 7 out of 10 sectors selected. Hence, the exclusion of energy sector’s financial innovations in the analysis of the hedging potential of financial innovations is justified. Thus, we consider 10 non-energy sectoral classifications of non-energy ETFs (see Table1) as each of these financial innovations signifies a claim on similar underlying assets in the sectors (see Agapova 2011) and is expected to be negatively correlated with the oil market for possible risk hedging benefits. We employ the vector autoregressive moving average of the generalized autoregressive conditional heteroscedastic family (VARMA-GARCH) as the underlying model for 1 For instance, on April 20, 2020, the West Texas Intermediate (WTI) dropped by a record 300% low (Devpura and Narayan 2020a, b, c). In the first quarter of 2020, global stock price recorded a loss of about 12.35% (Qin etal. 2020; Salisu etal. 2020a, b). Page 3 of 19 Salisuand Obiora Financ Innov (2021) 7:34 the hedging relationship between oil price and non-energy financial innovations. This modeling framework becomes relevant after rounds of preliminary data testing including the graphical analysis showing largely negative co-movements between the variables and tests for serial correlation, conditional heteroscedasticity, sign-bias, and asymmetry, which all indicate the need to capture ARCH effects, asymmetry, and possible time-variation in the model (see also, Arouri and Nguyen 2010; Arouri etal. 2011a, b; Arouri etal. 2011a, b; Salisu and Mobolaji 2013; Salisu and Oloko 2015a; Salisu etal. 2020a, b, among others). In addition, the technique employed for the analysis tends to offer superior forecast performance relative to other competing models such as vector 0 40 80 120 160 0 100 200 300 400 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 TECH NO LOGY Crudeoil 0 40 80 120 160 0 20 40 60 80 100 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 REALES TAT E Crude oil 0 40 80 120 160 20 40 60 80 100 120 140 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 TELECOM Cru d eoil 0 40 80 120 160 0 20 40 60 80 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 MATERIALS Crudeoil 0 40 80 120 160 4 8 12 16 20 24 28 32 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 FINANCIALS Cru deoil 0 40 80 120 160 0 20 40 60 80 100 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 INDUSTRIALS Cru d eoil 0 40 80 120 160 10 20 30 40 50 60 70 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 CO NSUMERSTAPLES Crude oil 0 40 80 120 160 20 30 40 50 60 70 80 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 UTILITIES Cru de oil 0 40 80 120 160 20 40 60 80 100 120 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 HEALTH Crudeoil 0 40 80 120 160 0 40 80 120 160 200 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 CO NS UMER DISCRETIONARY Cru d eoil Fig. 1 Pairwise graphs between non-energy sector ETFs and crude oil prices Table 1 Non-energy exchange traded funds Source: www. etfdb. com/ etfs/ sector The selected ETFs are based on Exchange Traded Funds categorization and ranking by the EFT database as at the end of December 2020 Sector ETF proxy Symbol Consumer discretionary Consumer discretionary select sector SPDR fund XLY Consumer staples Consumer staples select sector SPDR fund XLP Financials financial select sector SPDR fund XLF Health Health care select sector SPDR fund XLV Industrials Industrial select sector SPDR fund XLI Materials Materials select sector SPDR fund XLB Real estate Vanguard real estate Index fund VNQ Technology Invesco QQQ QQQ Telecom Vanguard communication services ETF VOX Utilities Utilities select sector SPDR fund XLU Page 4 of 19 Salisuand Obiora Financ Innov (2021) 7:34 autoregressive (VAR-based) models and its variants (see Lypny and Powalla 1998; Lee etal. 2005; Yang and Lai 2009) in the modeling financial series with the foregoing statistical features thrown up at the pre-estimation stage. To achieve the stated objective, we obtain the optimal hedge ratio (OHR) and optimal portfolio weight (OPW) associated with an investment in oil and non-energy financial innovations. Overall, we find that sectoral financial innovations are robust and resilient alternative investments. Further, we suggest that including them in an oil-based investment portfolio could provide alternative valuable asset class that can improve the risk-adjusted returns for investors, especially during a crisis. Therefore, when making investment decisions, investors in the global crude oil market that seek to maximize riskadjusted returns are likely to find the results useful. For robustness, we test and account for structural breaks in the estimation process. The presence of the breaks shows that the optimal portfolio combination of financial innovations and oil could be over-estimated, whereas the hedging effectiveness could be underestimated when such breaks are ignored. In other words, ignoring any significant structural break, when in fact it exists, may lead to wrong conclusions about hedging effectiveness. Following this background, we offer some preliminary analyses in “Data and methodology” section to determine the appropriate model for analyses. In “Analysis” section, we evaluate the relative hedging effectiveness of financial innovations for crude oil market risk due to the pandemic. In “Robustness—accounting for structural breaks” section, we discuss the additional results for robustness, and in “Conclusion” section, we conclude the paper. Data andmethodology Data description andsummary statistics The dataset used in the empirical estimation comprises daily prices of top-ranked non-energy ETFs2 and crude oil (using the West Texas Intermediate crude oil price as a proxy3) and covers the period between August 2004 and December 2020. The nonenergy ETFs considered are Technology, Healthcare, Real estate, Materials, Consumer discretionary, Financials, Industrials, Utilities, Consumer staples and Telecom sectors (see Killa 2020).4 Table1 highlights the selected ETFs for the 10 sectors (excluding the energy sector). Similarly, daily data on the sectoral ETF series are collected from finance. yahoo.com, and crude oil spot prices are obtainable from the US Energy Information Administration Database (https:// eia. gov). To evaluate the impact of the unprecedented COVID-19 pandemic outbreak on the hedging relationship, we partition the full data sample (8/01/2004 to 12/30/2020) into pre-COVID (8/01/2004 to 12/31/2019) and COVID (1/2/2020 to 12/30/2020) periods. Table2 summarizes the statistics consisting of the mean, maximum, minimum, standard deviation, skewness, and kurtosis, of the return series of both the ETFs and oil prices. The mean values of the returns series for the 10 non-energy ETF sectors under 2 https:// etfdb. com/ etfs/ sector/. 3 The West Texas Intermediate crude oil price is considered a good reflection of the global crude oil price (see Narayan and Gupta 2015). 4 https:// finan ce. yahoo. com/ news/ toprankedetfsstockstop15000 3045. html. Page 5 of 19 Salisuand Obiora Financ Innov (2021) 7:34 Table 2 Summary statistics for non-energy ETFs and oil returns Consumer discretionary Consumer staples Financials Health Industrials Materials Real estate Technology Telecom Utilities Oil Full data sample (8/01/2004 to 12/30/2020) Mean 0.040 0.028 0.006 0.033 0.028 0.024 0.013 0.054 0.022 0.022 0.040 Maximum 12.316 11.534 25.090 10.244 10.061 11.207 19.487 9.542 26.304 12.367 12.316 Minimum − 14.565 − 11.673 − 19.660 − 13.705 − 14.255 − 20.510 − 16.546 − 11.364 − 14.585 − 10.746 − 14.565 Std. Dev 1.462 1.000 2.019 1.150 1.414 1.582 1.897 1.329 1.399 1.194 1.462 Skewness − 0.603 − 1.057 0.303 − 0.685 − 0.575 − 0.934 − 0.200 − 0.671 1.024 − 0.497 − 0.603 Kurtosis 17.637 24.383 22.990 15.522 13.682 17.431 20.136 10.602 45.652 16.146 17.637 Before COVID-19 (8/01/2004 to 12/31/2019) Mean 0.037 0.028 0.007 0.033 0.028 0.021 0.016 0.048 0.017 0.025 0.010 Maximum 0.087 0.040 0.054 0.083 0.071 0.073 0.068 0.131 0.045 0.060 0.036 Minimum − 14.565 − 11.673 − 19.660 − 13.705 − 14.255 − 14.782 − 16.546 − 11.364 − 14.585 − 10.746 − 16.832 Std. Dev 1.403 0.952 1.969 1.107 1.332 1.496 1.868 1.262 1.359 1.112 2.150 Skewness − 0.517 − 0.881 0.451 − 0.665 − 0.524 − 0.537 − 0.149 − 0.570 1.360 − 0.165 0.124 Kurtosis 18.880 26.295 25.529 17.004 13.953 11.765 21.854 11.048 52.910 15.618 7.807 COVID-19 sample (1/2/2020 to 12/30/2020) Mean 0.100 0.027 − 0.021 0.042 0.030 0.063 − 0.038 0.159 0.101 − 0.018 0.100 Maximum 8.923 5.148 8.774 4.795 8.319 10.899 7.942 6.055 6.238 6.594 8.923 Minimum − 10.963 − 9.144 − 12.379 − 7.824 − 11.780 − 20.510 − 10.911 − 9.031 − 9.296 − 10.511 − 10.963 Std. Dev 2.181 1.567 2.679 1.676 2.338 2.571 2.314 2.104 1.911 2.081 2.181 Skewness − 0.914 − 1.470 − 0.616 − 0.689 − 0.599 − 1.976 − 0.585 − 0.979 − 1.012 − 1.111 − 0.914 Kurtosis 8.782 11.278 6.967 6.572 7.403 20.422 6.547 5.831 6.926 9.015 8.782 Page 6 of 19 Salisuand Obiora Financ Innov (2021) 7:34 consideration indicate positive average returns, both for the full and pre-COVID-19 sample periods. However, during the COVID sample, we find negative mean values for four sectors, namely, financials, industrials, real estate and technology sectors, whereas others remain positive. Meanwhile, the overall mean value involving the full sample for the oil sector is negative, whereas it is mixed for the two sub-sample. Moreover, it is positive for the pre-COVID-19 sample, whereas it is negative for the COVID-19 period. The standard deviation, which gives an insight into the volatility of the return series, reveals higher values during COVID-19 for the non-energy ETFs than the full sample and preCOVID periods. This indicates that the ETFs exhibit more volatility during the COVID19 period than the pre-COVID-19 sample. In addition, all the series are negatively skewed during COVID-19, given the negative values of the skewness statistics and are leptokurtic. Unsurprisingly, all the return series exhibit a conditional heteroscedasticity effect that must be dealt with in the estimation process required for the hedging analysis. A pairwise graphical representation between crude oil price and each non-energy sector ETFs shows evidence of opposite movements, which somewhat attests to the potential of the ETFs as a good hedge against oil price risk. The model This study employs the GARCH-based VARMA model proposed by Ling and McAleer (2003). The VARMA-GARCH models were featured as prominent instruments used in empirical literature for modeling interdependencies among financial time series with or without asymmetric shock effects (see Salisu and Mobolaji 2013; Salisu and Oloko 2015b; Al-Maadid etal. 2017; Salisu etal. 2020a, b). However, the choice of appropriate variants, that is, between constant conditional correlations (CCC) or its dynamic variant DCC, and between symmetric and asymmetric effects, is determined based on the outcomes of certain formal pretests.5 The general version of the VARMA-GARCH model has two parts: the mean equation part and the variance equation part. The former is typically a VAR model, and the latter is specified in a way that mimics the VARMA comprising ARCH and GARCH terms. Consequently, we construct a bivariate VARMAGARCH(1,1) model and specify the mean equations that capture the return spillover effects between the two series under consideration, that is, ETF and crude oil price, and vice versa:,6,7 (1) r oil t =ϕoil +φoilroil t−1 +θoilr etf t−1 +εoil t 5 The preliminary test results are presented and discussed in the next section. 6 A similar methodology was recently adopted by Salisu, Vo and Lawal (2020a, b) to assess the hedging potential of gold against oil price risk. 7 We acknowledge that the interplay of several factors is responsible for the movements in global crude oil prices (some of which have been evaluated in other literature). However, during the outbreak of the coronavirus pandemic, there seems to be a consensus in the literature (see, e.g., Gil-Alana and Monge 2020; Narayan 2020a, b, c; Salisu, Ebuh, and Usman 2020a, b) that the huge decline in oil prices was mainly due to political and economic decisions meant to curtail the viral spread, such as economic lockdown and domestic and international travel restrictions. In addition, crude oil price has never recorded a negative price in its entire history until this period. Hence, the high impact of the pandemic might have overshadowed all other impacts. Notwithstanding, the way the VARMA-GARCH is specified accommodates shocks due to other factors that may be responsible for the unprecedented movements in oil prices. As the term implies, VARMA is a vector autoregressive moving average, which forms the components of the multivariate GARCH model used in this study. Page 7 of 19 Salisuand Obiora Financ Innov (2021) 7:34 where retf t and roil t respectively denote each of non-energy sector’s ETFs and crude oil price return in period t ; ϕetf and ϕoil are constant terms; φetf and φoil are coefficients of the lagged terms of own-returns respectively for non-energy ETF and crude oil; θetf and θoil are coefficients of the lagged terms of cross-return spillovers; and εetf t and εoil t are independently and identically distributed errors. Note that the superscripts, oil and etf, respectively, denote oil price and ETF returns. The conditional variance equations that provide the computation of the volatility spillover effects between the two asset classes are specified in Eqs. (3) and (4) for non-energy ETF and crude oil price returns, respectively: These equations show that conditional variance for each sector depends on its immediate past values and innovations and the past values and innovations of the other sector. The parameters αi and βi (where i = 1, 2) measure the shock and volatility spillover effects between the two return series, respectively, whereas the superscripts identify each series. Meanwhile, subscripts 1and 2, respectively, capture ownand cross-spillover effects. The conditional covariance, which is preliminarily assumed to be of CCC,8 is expressed as where ρEO is the conditional constant correlations between non-energy financial innovations and crude oil returns. In line with the objective of this paper, the estimated coefficients obtained from the VARMA-GARCH model are employed to evaluate the optimal weights and hedging effectiveness of non-energy sectoral financial innovations in an investment portfolio containing oil. The OPW establishes the proportion of investments in ETFs and crude oil to be included in a portfolio to ensure optimality. Significant volatility spillovers between two investment assets in a given portfolio may indicate that investments in the two assets are volatile and susceptible to risk and uncertainty. Hence, investors engage in hedging to mitigate such associated risks through investment in futures contract without jeopardizing expected future returns. Following the approach proposed by Kroner and Ng (1998) and Arouri etal. (2011a, b), we construct an OPW of holding the two assets (i.e., ETFs and crude oil) using the conditional variance and covariance estimates obtained after estimating Eqs. (3), (4), and (5): (2) retf t =ϕetf +φetf retf t−1 +θetf retf t−1 +ε etf t (3) h etf t=cetf +αetf 1  εetf t−1 2 +αetf 2  εoil t−1 2 +βetf 1  hetf t−1  +βetf 2  hoil t−1  (4) h oil t=coil +αoil a  εoil t−1 2 +αoil b  εetf t−1 2 +βoil a  hoil t−1  +βoil b  hetf t−1  (5) h EO t =ρEO ×  hetf t ×  h oil t 8 An alternative variant of the variance equations is the one that allows for time variation in the conditional correlations, which is described as dynamic conditional correlations. To determine the choice of conditional correlations to account for the hedging analysis, we employ Engle and Sheppard’s (2001) test as part of the preliminary tests. Page 8 of 19 Salisuand Obiora Financ Innov (2021) 7:34 and, where EO,t denotes the weight of non-energy sector’s ETFs in a one-dollar ETF/crude oil investment portfolio at time t . Also, the term— hEO t is the conditional covariance between the ETF and crude oil returns at time t . Meanwhile, the OHR between each non-energy ETF and crude oil return is defined as where αEO,t is the OHR between the oil and each non-energy sector’s ETF under consideration. The description of the data used, including preliminary analyses and formal pretests, is discussed in the next section. Analysis Preliminary tests We begin the results section with the formal preliminary tests conducted to determine the appropriate variant of the VARMA-GARCH model to be adopted for the main estimation, as discussed in the modeling section. The estimates obtained from the GARCH models are crucial in the estimation of the OPW and hedging effectiveness between each considered non-energy ETF and oil return. The considered pretests include serial correlation, conditional heteroscedasticity, asymmetry, and conditional correlation tests. The serial correlation test is conducted using Ljung-Box Q-statistics, whereas the ARCH-LM test is used for the conditional heteroscedasticity test over pre-determined lag lengths of 5 and 10. We test for asymmetry using Engle and Ng’s (1993) sign and bias tests, and we used Engle and Sheppard’s (2001) test to evaluate the presence or absence of the CCC in the multivariate volatility model. All the results of the pretests are summarized in Tables3 and 4. The results of the ARCH-LM tests indicate that all returns exhibit conditional heteroscedasticity with the hypothesis of no ARCH effects rejected for the series under consideration. Therefore, such effects must be accommodated in the empirical estimation. The Ljung-Box tests, using both the correlogram Q-statistic and its squared variant, further confirm the presence of serial correlation across all return series, both at 5 and 10 lag orders. Table4 summarizes the results of Engle and Ng’s (1993) sign and bias tests and Engle and Sheppard’s (2001) tests. The estimated results of Engle and Ng’s (1993) sign and joint size bias tests, both of which evaluate the evidence of asymmetric effects in the relationship between each ETF and oil price return, confirm the presence of the same nexus for the pre-covid sample. Meanwhile, the results show evidence of asymmetric relationship only for the financial innovations in Consumer Staples and Real Estate (6)  EO,t=h etf t−hEO t h oil t −2 h EO t + h etf t (7)  EO,t=    0, if EO,t<0 EO,t, if 0 < EO,t≤ 1 1, if EO,t>1 (8) α EO,t= h EO t h etf t Page 15 of 19 Salisuand Obiora Financ Innov (2021) 7:34 These findings support evidence of hedging effectiveness between considered sectoral financial innovations and oil price returns. Further, we report improved hedging performance during the pandemic, thus substantiating the earlier advancement for the consideration of sectoral financial innovations as resilient alternative investment options that could help improve the risk-adjusted returns for oil investors during a crisis. By further accounting for structural breaks in the analysis, we establish that the optimal portfolio combination of financial innovations and oil could be over-estimated, whereas the hedging effectiveness could be underestimated when such breaks are ignored. In other words, ignoring any significant structural break despite its existence may lead to wrong conclusions about the hedging effectiveness. Overall, investors in the global crude oil market that seek to maximize risk-adjusted returns should find the outcome of the study useful when making investment decisions. Several possibilities exist for future researchers to extend this study. One of the immediate choices is to explore the hedging effectiveness of other forms of financial innovations excluding ETFs, such as Sukuk (Islamic) bonds, hedge funds, and mutual funds, for covering the oil market risks. In addition, other extensions like the expanded energy market risks can be explored in future studies. Appendix See Fig.2. Table 7 Optimal portfolio weights and hedge ratios using breaks adjusted series The table reports average optimal portfolio weights (OPW) and optimal hedge ratios (OHR) for non‑energy ETFs in an oil investment portfolio after adjusting for structural breaks in each of their return series Full sample Before COVID-19 During COVID-19 OPW OHR OPW OHR OPW OHR Consumer discretionary 0.8523 0.0921 0.8077 0.0946 0.8475 0.1047 Consumer staples 0.8961 0.0560 0.9038 0.0501 0.8990 0.0716 Financials 0.9163 0.1095 0.8628 0.0836 0.8534 0.1136 Health 0.9447 0.1074 0.9026 0.0477 0.8824 0.0715 Industrials 0.8681 0.1759 0.9118 0.0806 0.8059 0.1401 Materials 0.8655 0.1445 0.8479 0.1344 0.7996 0.1771 Real estate 0.7182 0.2311 0.8629 0.0492 0.6494 0.2698 Technology 0.8268 0.0525 0.9241 0.0697 0.6763 0.1507 Telecom 0.8650 0.0576 0.9308 0.0554 0.7845 0.1553 Utilities 0.8473 0.0774 0.8663 0.0478 0.7761 0.1736 Page 16 of 19 Salisuand Obiora Financ Innov (2021) 7:34 Acknowledgements We are thankful to the journal, the Editor-in-Chief and the Guest Editor for providing a platform for exchange of ideas on COVID-19 pandemic and Financial Innovations. Authors’ contributions AS conceptualized the study, formulated the methodology, performed the econometric analysis and drafted the manuscript. KO participated in data curation, results validation, reviewing and editing and helped to draft the manuscript. Both authors read and approved the final manuscript. Funding No specific financial support was received to carry out the study. Availability of data materials The data that support the findings of this study are available on request from the corresponding author. Some of the data are not publicly available due to privacy or ethical restrictions. Declarations Competing interests The authors do not have any conflict of interest to declare. Author details 1 University of Ibadan Centre for Econometric and Allied Research, Ibadan, Oyo, Nigeria. 2 Economic Policy Directorate, Central Bank of Nigeria, Abuja, Nigeria. Received: 21 November 2020 Accepted: 4 May 2021 References Agapova A (2011) Conventional mutual index funds versus exchange-traded funds. J Financ Mark 14(2):323–343 Alexander C, Barbosa A (2008) Hedging index exchange traded funds. J Bank Finance 32:326–337 Al-Maadid A, Caporale GM, Spagnolo F, Spagnolo N (2017) Spillovers between food and energy prices and structural breaks. Int Econ 150:1–18. https:// doi. org/ 10. 1016/j. inteco. 2016. 06. 005 0 40 80 120 160 0 10 20 30 40 50 60 12 13 14 15 16 17 18 19 20 AMJ Crudeoil 0 40 80 120 160 0 20 40 60 80 100 12 13 14 15 16 17 18 19 20 AMLP Crudeoil 0 40 80 120 160 6 8 10 12 14 16 18 12 13 14 15 16 17 18 19 20 ICLN Crudeoil 0 40 80 120 160 10 20 30 40 50 12 13 14 15 16 17 18 19 20 IXC Crud e oil 0 40 80 120 160 0 20 40 60 80 100 120 12 13 14 15 16 17 18 19 20 mlpa Crudeoil 0 40 80 120 160 10 20 30 40 50 60 12 13 14 15 16 17 18 19 20 PBW Crudeoil 0 40 80 120 160 10 20 30 40 50 60 12 13 14 15 16 17 18 19 20 TAN Crudeoil 0 40 80 120 160 20 40 60 80 100 120 140 160 12 13 14 15 16 17 18 19 20 VDE Crudeoil 0 40 80 120 160 20 40 60 80 100 120 12 13 14 15 16 17 18 19 20 XLE Crudeoil 0 40 80 120 160 0 100 200 300 400 12 13 14 15 16 17 18 19 20 XO P Crudeoil Fig. 2 Co-movements between energy sector ETFs and crude oil prices Page 17 of 19 Salisuand Obiora Financ Innov (2021) 7:34 Apergis E, Apergis N (2020) Can the COVID-19 pandemic and oil prices drive the US Partisan Conflict Index? Energy Res Lett. https:// doi. org/ 10. 46557/ 001c. 1314 Arouri MEH, Nguyen DK (2010) Oil prices, stock markets and portfolio investment: evidence from sector analysis in Europe over the last decade. Energy Policy 38(8):4528–4539 Arouri MEH, Jouini J, Nguyen DK (2011a) Volatility spillovers between oil prices and stock sector returns: implications for portfolio management. J Int Money Financ 30(7):1387–1405 Arouri MEH, Lahiani A, Nguyen DK (2011b) Return and volatility transmission between world oil prices and stock markets of the GCC countries. Econ Model 28(4):1815–1825 Asness CS, Krail RJ, Liew JM (2001) Do hedge funds hedge? J Portfolio Manag 28(1):6–19 Babikir A, Gupta R, Mwabutwa C, Owusu-Sekyere E (2012) Structural breaks and GARCH models of stock return volatility: the case of South Africa. Econ Model 29(6):2435–2443. https:// doi. org/ 10. 1016/j. econm od. 2012. 06. 038 Baur DG, Lucey BM (2010) Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financ Rev 45:217–229 Beck T, Demirgüç-Kunt A, Merrouche O (2010) Islamic vs. conventional banking: business model, efficiency and stability. The World Bank, Washington. https:// doi. org/ 10. 1596/ 181394505446 Beck T, Chen T, Lin C, Song FM (2016) Financial innovation: the bright and the dark sides. J Bank Finance 72:28–51 Berg E, Schmitz B, Starp M (2006) Weather derivatives as an instrument to hedge against the risk of high energy cost in greenhouse production (No. 1004-2016-78626) Chang, B. H., Sharif, A., Aman, A., Suki, N. M., Salman, A., & Khan, S. A. R. (2020). The asymmetric effects of oil price on sectoral Islamic stocks: New evidence from quantile-on-quantile regression approach. Resources Policy, 65, 101571. Chapra MU (2011) The global financial crisis: can islamic finance help? Islamic economics and finance. Springer, pp 135–142. https:// doi. org/ 10. 1057/ 97802 30361 133_5 Cheema MA, Faff RW, Szulczuk K (2020) The 2008 global financial crisis and COVID-19 pandemic: How safe are the safe haven assets? Covid Econ Vetted Real-Time Pap 34:88–115 Chen Z (1995) Financial innovation and arbitrage pricing in frictional economies. J Econ Theory 65(1):117–135 Chou YK (2007) Modeling financial innovation and economic growth: Why the financial sector matters to the real economy. J Econ Educ 38(1):78–90 Conlon T, McGee R (2020) Safe haven or risky hazard? Bitcoing during the COVID-19 bear market. Financ Res Lett. https:// doi. org/ 10. 1016/j. frl. 2020. 101607 Corbet S, Larkin C, Lucey B (2020) The contagion effects of the COVID-19 pandemic: evidence from gold and cryptocurrencies. Financ Res Lett. https:// doi. org/ 10. 1016/j. frl. 2020. 101554 Dannhauser CD (2017) The impact of innovation: Evidence from corporate bond exchange-traded funds (ETFs). J Financ Econ 125(3):537–560 Devpura N, Narayan PK (2020) Hourly oil price volatility: the role of COVID-19. Energy Res Lett. https:// doi. org/ 10. 46557/ 001c. 13683 El-Sharif I, Brown D, Burton B, Nixon B, Russell A (2005) Evidence on the nature and extent of the relationship between oil prices and equity values in the UK. Energy Econ 27(6):819–830 Engle RF, Ng VK (1993) Measuring and testing the impact of news on. J Finance 48(1749):1778 Engle RF, Sheppard K (2001) Theoretical and empirical properties of dynamic conditional correlation MVGARCH. UCSD working paper No. 2001-15 Fu M, Shen H (2020) COVID-19 and corporate performance in the energy industry. Energy Res Lett. https:// doi. org/ 10. 46557/ 001c. 12967 Gao Y (2012) Hedging effectiveness of energy exchange traded funds. Doctoral dissertation, Concordia University Gil-Alana LA, Claudio-Quiroga G (2020) The COVID-19 impact on the asian stock markets. Asian Econ Lett. https:// doi. org/ 10. 46557/ 001c. 17656 Gil-Alana LA, Monge M (2020) Crude oil prices and COVID-19: persistence of the shock. Energy Res Lett. https:// doi. org/ 10. 46557/ 001c. 13200 Godil DI, Sarwat S, Sharif A, Jermsittiparsert K (2020) How oil prices, gold prices, uncertainty and risk impact Islamic and conventional stocks? Empirical evidence from QARDL technique. Resour Policy 66:101638. https:// doi. org/ 10. 1016/j. resou rpol. 2020. 101638 Hasan MM, Dridi J (2010) The effects of the global crisis on Islamic and conventional banks: a comparative study. IMF working papers, pp 1–46. https:// doi. org/ 10. 1142/ S1793 99331 10002 70 Huang W, Zheng Y (2020) COVID-19: structural changes in the relationship between investor sentiment and crude oil futures price. Energy Res Lett 1(2) Investopedia (2020) Top health care stocks for July, 2020. Available online: https:// www. inves toped ia. com/ inves ting/ tophealt hcarestocks/. Assessed 7 Nov 2020 Iyke BN (2020a) COVID-19: the reaction of US oil and gas producers to the pandemic. Energy Res Lett 1(2):13912. https:// doi. org/ 10. 46557/ 001c. 13912 Iyke BN (2020b) The disease outbreak channel of exchange rate return predictability: evidence from COVID-19. Emerg Mark Financ Trade 56(10):2277–2297 Iyke BN (2020c) Economic policy uncertainty in times of COVID-19 pandemic. Asian Econ Lett. https:// doi. org/ 10. 46557/ 001c. 17665 Iyke BN, Ho SY (2020) Consumption and exchange rate uncertainty: evidence from selected Asian countries. World Econ 43(9):2437–2462 Jin J, Han L, Wu L, Zeng H (2020) The hedging effectiveness of global sectors in emerging and developed stock markets. Int Rev Econ Financ 66:92–117. https:// doi. org/ 10. 1016/j. iref. 2019. 11. 001 Killa S (2020) Top-ranked ETFs, stocks from top sector of the last decade. Retrieved from https:// finan ce. yahoo. com/ news/ toprankedetfsstockstop15000 3045. html Kroner KF, Ng VK (1998) Modeling asymmetric comovements of asset returns. Rev Financ Stud 11(4):817–844 Kumar D (2014) Return and volatility transmission between gold and stock sectors: application of portfolio management and hedging effectiveness. IIMB Manag Rev 26(1):5–16. https:// doi. org/ 10. 1016/j. iimb. 2013. 12. 002 Page 18 of 19 Salisuand Obiora Financ Innov (2021) 7:34 Lee M, Chiou J-S, Wu P-S, Chen C-D (2005) Hedging with S&P500 and E-mini S&P500 stock index futures. J Stat Manag Syst 8(2):275–294 Liang B (2001) Hedge fund performance: 1990–1999. Financ Anal J 57(1):11–18 Ling S, McAleer M (2003) Asymptotic theory for a vector ARMA–GARCH model. Econ Theory 19:278–308 Liu L, Wang E-Z, Lee C-C (2020) Impact of the COVID-19 pandemic on the crude oil and stock markets in the US: a timevarying analysis. Energy Res Lett. https:// doi. org/ 10. 46557/ 001c. 13154 Lypny G, Powalla M (1998) The hedging effectiveness of DAX futures. Eur J Finance 4(4):345–355 Marszk A, Lechman E (2018) Tracing financial innovation diffusion and substitution trajectories. Recent evidence on exchange-traded funds in Japan and South Korea. Technol Forecast Soc Chang 133:51–71 Mishra S, Sharif A, Khuntia S, Meo SA, Khan SAR (2019) Does oil prices impede Islamic stock indices? Fresh insights from wavelet-based quantile-on-quantile approach. Resour Pol 62:292–304. https:// doi. org/ 10. 1016/j. resou rpol. 2019. 04. 005 Mohanty SK, Nandha M, Turkistani AQ, Alaitani MY (2011) Oil price movements and stock market returns: evidence from Gulf Cooperation Council (GCC) countries. Global Finance J 22(1):42–55. https:// doi. org/ 10. 1016/j. gfj. 2011. 05. 004 Mongi A, Dhouha HA (2016) Do structural breaks affect portfolio designs and hedging strategies? International evidence from stock-commodity markets linkages. Int J Econ Financ Issues 6(1):252–270 Naeem M, Umar Z, Ahmed S, Ferrouhi EM (2020) Dynamic dependence between ETFs and crude oil prices by using EGARCH-Copula approach. Phys A 557:124885. https:// doi. org/ 10. 1016/j. physa. 2020. 124885 Narayan S (2013) Foreign exchange markets and oil prices in Asia. J Asian Econ 28:41–50 Narayan PK (2020a) Oil price news and COVID-19-Is there any connection? Energy Res Lett 1(1):13176. https:// doi. org/ 10. 46557/ 001c. 13176 Narayan PK (2020b) Has COVID-19 changed exchange rate resistance to shocks? Asian Econ Lett. https:// doi. org/ 10. 46557/ 001c. 17389 Narayan PK (2020c) Did bubble activity intensify during COVID-19? Asian Econ Lett. https:// doi. org/ 10. 46557/ 001c. 17654 Narayan PK, Gupta R (2015) Has oil price predicted stock returns for over a century? Energy Econ. 48:18–23 Narayan PK, Liu R (2011) Are shocks to commodity prices persistent? Appl Energy 88(1):409–416. https:// doi. org/ 10. 1016/j. apene rgy. 2010. 07. 032 Narayan PK, Liu R (2015) A unit root model for trending time-series energy variables. Energy Econ 50:391–402 Narayan PK, Phan DHB (2017) Momentum strategies for Islamic stocks. Pac Basin Finance J 42:96–112. https:// doi. org/ 10. 1016/j. pacfin. 2016. 05. 015 Narayan PK, Popp S (2010) A new unit root test with two structural breaks in level and slope at unknown time. J Appl Stat 37(9):1425–1438 Narayan PK, Narayan S, Prasad A (2008) Understanding the oil price-exchange rate nexus for the Fiji islands. Energy Econ 30:2686–2696 Narayan PK, Phan DHB, Sharma SS (2019) Does Islamic stock sensitivity to oil prices have economic significance? Pac. Basin Finance j. 53:497–512. https:// doi. org/ 10. 1016/j. pacfin. 2018. 04. 003 Narayan PK, Devpura N, Wang H (2020) Japanese currency and stock market—What happened during the COVID-19 pandemic? Econ Anal Policy 68:191–198 Okorie DI, Lin B (2020) Crude oil price and cryptocurrencies: evidence of volatility connectedness and hedging strategy. Energy Econ 87:104703 Olson E, Vivian A, Wohar ME (2019) What is a better cross-hedge for energy: equities or other commodities? Global Finance J 42:100417 Ozdurak C, Ulusoy V (2020) Price discovery in crude oil markets: intraday volatility interactions between crude oil futures and energy exchange traded funds. Int J Energy Econ Policy 10(3):402–413 Partnoy F, Thomas RS (2007) Gap filling, hedge funds, and financial innovation 6–21 Polemis M, Soursou S (2020) Assessing the impact of the COVID-19 pandemic on the Greek energy firms: an event study analysis. Energy Res Lett 1(3) Prabheesh KP (2020) Dynamics of foreign portfolio investment and stock market returns during the COVID-19 pandemic: evidence from India. Asian Econ Lett. https:// doi. org/ 10. 46557/ 001c. 17658 Prabheesh KP, Padhan R, Garg B (2020) COVID-19 and the oil price-stock market nexus: evidence from net oil-importing countries. Energy Res Lett. https:// doi. org/ 10. 46557/ 001c. 13745 Qin M, Zhang Y-C, Su C-W (2020) The essential role of pandemics: a fresh insight into the oil market. Energy Res Lett. https:// doi. org/ 10. 46557/ 001c. 13166 Rahim AM, Masih M (2016) Portfolio diversification benefits of Islamic investors with their major trading partners: evidence from Malaysia based on MGARCH-DCC and wavelet approaches. Econ Modell 54:425–438. https:// doi. org/ 10. 1016/j. econm od. 2015. 12. 033 Rizvi SAR, Arshad S, Alam N (2015) Crises and contagion in Asia Pacific—Islamic v/s conventional markets. Pac Basin Finance J 34:315–326. https:// doi. org/ 10. 1016/j. pacfin. 2015. 04. 002 Sakarya B, Ekinci A (2020) Exchange-traded funds and FX volatility: evidence from Turkey. Central Bank Rev. https:// doi. org/ 10. 1016/j. cbrev. 2020. 06. 002 Salisu A, Adediran I (2020) Uncertainty due to infectious diseases and energy market volatility. Energy Res Lett. https:// doi. org/ 10. 46557/ 001c. 14185 Salisu AA, Adeleke AI (2016) Further application of Narayan and Liu (2015) unit root model for trending time series. Econ Modell 55(C):305–314 Salisu AA, Mobolaji H (2013) Modeling returns and volatility transmission between oil price and US–Nigeria exchange rate. Energy Econ 39:169–176 Salisu AA, Oloko TF (2015a) Modeling oil price–US stock nexus: a VARMA–BEKK–AGARCH approach. Energy Econ 50(C):1–12 Salisu AA, Oloko TF (2015b) Modelling spillovers between stock market and FX market: evidence for Nigeria. J Afr Bus 16(1–2):84–108 Page 19 of 19 Salisuand Obiora Financ Innov (2021) 7:34 Salisu AA, Sikiru AA (2020) Pandemics and the Asia-Pacific Islamic stocks. Asian Econ Lett. https:// doi. org/ 10. 46557/ 001c. 17413 Salisu AA, Ndako UB, Oloko TF, Akanni LO (2016) Unit root modeling for trending stock market series. Borsa Istanbul Rev 16(2):82–91 Salisu AA, Adekunle W, Alimi WA, Emmanuel Z (2019a) Predicting exchange rate with commodity prices: New evidence from Westerlund and Narayan (2015) estimator with structural breaks and asymmetries. Resour Policy 62(C):33–56 Salisu AA, Raheem ID, Ndako UB (2019b) A sectoral analysis of asymmetric nexus between oil price and stock returns. Int Rev Econ Finance 61(C):241–259 Salisu AA, Swaray R, Oloko TF (2019c) Improving predictability of oil-US stock nexus: the role of macroeconomic variables. Econ Model 76:153–171. https:// doi. org/ 10. 1016/j. econm od. 2018. 07. 029 Salisu AA, Ebuh GU, Usman N (2020a) Revisiting oil-stock nexus during COVID-19 pandemic: some preliminary results. Int Rev Econ Financ 69:280–294. https:// doi. org/ 10. 1016/j. iref. 2020. 06. 023 Salisu AA, Vo XV, Lawal A (2020b) Hedging oil price risk with gold during COVID-19 pandemic. Resour Policy. https:// doi. org/ 10. 1016/j. resou rpol. 2020. 101897 Selmi R, Mensi W, Hammoudeh S, Bouoiyour J (2018) Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold. Energy Econ 74:787–801 Sharma SS (2020) A note on the asian market volatility during the COVID-19 pandemic. Asian Econ Lett. https:// doi. org/ 10. 46557/ 001c. 17661 Sharma S, Rodriguez I (2019) The diminishing hedging role of crude oil: evidence from time varying financialization. J Multinatl Financ Manag 52:100593. https:// doi. org/ 10. 1016/j. mulfin. 2019. 100593 Smyth R, Narayan PK (2018) What do we know about oil prices and stock returns? Int Rev Financ Anal 57:148–156 Statista (2020) How Covid 19 has impacted the global start up. Available online: https:// www. stati sta. com/ chart/ 22134/ coron avirusimpactonstart ups/. Assessed 7 Aug 2020, pp 237–256 Swaray R, Salisu AA (2018) A firm-level analysis of the upstream-downstream dichotomy in the oil-stock nexus. Global Finance J 37(C):199–218 Tari MJ (2010). Exchange‐traded funds (ETFs). In: Encyclopedia of quantitative finance, R. Cont (Ed.). https:// doi. org/ 10. 1002/ 97804 70061 602. eqf07 035 Tisdell CA (2020) Economic, social and political issues raised by the COVID-19 pandemic. Econ Anal Policy 68:17–28 Xavier J (2020) ETFs: passing the Covid-19 stress test | ETF Strategy. Retrieved October 11, 2020, from https:// www. etfst rategy. com/ etfspassi ngthecovid19stresstest98547/ Yang MJ, Lai YC (2009) An out-of-sample comparative analysis of hedging performance of stock index futures: dynamic versus static hedging. Appl Financ Econ 19(13):1059–1072 Yang CC, Brockett PL, Wen MM (2009) Basis risk and hedging efficiency of weather derivatives. J Risk Finance Zhang YJ, Wu YB (2019) The time-varying spillover effect between WTI crude oil futures returns and hedge funds. Int Rev Econ Financ 61:156–169 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.