Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry

Asad, Muzaffar; Tabash, Mosab I.; Sheikh, Umaid A.; Al-Muhanadi, Mesfer Mubarak; Ahmad, Zahid Article Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Asad, Muzaffar; Tabash, Mosab I.; Sheikh, Umaid A.; Al-Muhanadi, Mesfer Mubarak; Ahmad, Zahid (2020) : Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 7, Iss. 1, pp. 1-30, https://doi.org/10.1080/23311975.2020.1849889 This Version is available at: https://hdl.handle.net/10419/245011 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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/ Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Cogent Business & Management ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/oabm20 Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry Muzaffar Asad, Mosab I. Tabash, Umaid A. Sheikh, Mesfer Mubarak Al- Muhanadi & Zahid Ahmad | To cite this article: Muzaffar Asad, Mosab I. Tabash, Umaid A. Sheikh, Mesfer Mubarak Al- Muhanadi & Zahid Ahmad | (2020) Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry, Cogent Business & Management, 7:1, 1849889, DOI: 10.1080/23311975.2020.1849889 To link to this article: https://doi.org/10.1080/23311975.2020.1849889 © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 18 Jan 2021. Submit your article to this journal Article views: 655 View related articles View Crossmark data Page 1 of 30 Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry Muzaffar Asad, Mosab I. Tabash, Umaid A. Sheikh, Mesfer Mubarak Al-Muhanadi and Zahid Ahmad Cogent Business & Management (2021), 7: 1849889 BANKING & FINANCE | RESEARCH ARTICLE Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry Muzaffar Asad 1 , Mosab I. Tabash 2 , Umaid A. Sheikh 3* , Mesfer Mubarak Al-Muhanadi 4 and Zahid Ahmad 5 Abstract: The primary objective of this research article is to investigate the asymmetrical linkages between gold-oil-exchange rates and Bombay stock indexes by utilizing a nonlinear ARDL approach covering the period from April 2003 to May 2020. Time-series data is divided into three different types of regimes such as before the crisis regime, after the crisis regime, and over the entire period. Seasonality effects within the data series are identified through utilizing different types of unit root analysis such as Philips Peron (PP), augmented dickey-fuller test (ADF), and Kwiatkowski Philips Schmidt Shin (KPSS) test statistics followed by Zivot ABOUT THE AUTHORS Muzaffar Asad has completed his PhD in entrepreneurship from Malaysia and is now associated with the University of Bahrain as Assistant Professor. Previously, he has served Foundation University Islamabad as Associate Professor and the University of Central Punjab, Lahore as Assistant Professor. He is a renowned trainer of entrepreneurship and leadership. Mosab I. Tabash is the program director for Master of Business Administration at college of business, Al Ain University, UAE. Umaid A. Sheikh is an expert in econometrics and is having interest in exploring symmetrical and asymmetrical linkages between macroeconomic and stock indexes. Mesfer Mubarak Al-Muhanadi is Assistant Professor and Chairman of Management and Marketing Department in College of Business Administration, University of Bahrain. He has completed his PhD in Banking and Finance from University of Salford, United Kingdom. malmuha- [email protected] Zahid Ahmad is working as a Professor in the Faculty of management sciences, University of central Punjab. He also holds a PhD degree in Accounting and Finance PUBLIC INTEREST STATEMENT In long run and before the global financial crisis, only negative shocks associated with exchange rate fluctuation, gold prices, and oil prices are value relevant for investors as investors did not give much importance to episodes of currency devaluations, positive shocks associated with gold prices and oil prices. Positive shocks to exchange rate fluctuations means that dollar prices appreciated against local rupee value, which can be a favorable outcome for economies relying on exports rather than import centric economies. However, after the economic crunch, both positive and negative fluctuations in exchange rates remain no longer value relevant for investors. This research article has found that the global financial crisis has not only effected the asymmetric association between gold prices, oil prices, stock indexes, and dollar values against the local Indian rupee but the relationship between these variables is also dependent upon specific regime and is nonlinear in nature. Muzaffar Asad Mosab I. Tabash Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 2 of 30 Received: 27 December 2019 Accepted: 22 October 2020 * Corresponding author: Umaid A. Sheikh, Accounting and Finance, University of Central Punjab UCP, Lahore, Pakistan E-mail: [email protected] Reviewing editor: David McMillan, University of Stirling, Stirling, UK Additional information is available at the end of the article © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Andrew (ZA) unit root for identification of structural break unit root test. Nonlinearity within time-series frequencies has been identified through the implementation of BDS test statistics. For longer horizons and before the economic recession period, only gold prices, oil prices, and currency values have an asymmetrical association with Bombay stock indexes as positive shocks to these variables have no impact on stock indexes. However, after the crisis regime and for the longer term, negative shocks to exchange rate fluctuations and oil prices remain statistically insignificant, and only an asymmetrical relationship is established between oil prices and stock indexes. This shows that the regime is more important while classifying the impact of oil prices, gold prices, and appreciation, or depreciation in local currency on Bombay stock indexes. This research article has established an asymmetrical association between stock indexes and gold-oil-exchange rates and concluded asymmetries between them, which are well thought out to be symmetrical by previous researches. Subjects: Economics; Macroeconomics; Microeconomics; International Economics; Development Economics Keywords: Bombay stock exchange; NARDL model with dynamic multipliers; unit root test with structural breaks; oil prices; gold prices; exchange rate fluctuations; macroeconomic volatility 1. Introduction The South Asian economies have undergone the worst economic recession since 2008 because of the worldwide economic crisis of 2008. Such countries required sufficient resources to stabilize against the aftereffects of the 2008 financial crisis and to rebound economically with maximum momentum. The major work onto the consequences and solutions to the 2008 international financial downturn, which is highly divisive by policymakers and scholars, has been widely discussed by (Asad & Farooq, 2009; Athukorala & Chongvilaivan, 2010; Muthukumaran et al., 2011; Al- Rjoub & Azzam, 2012; Inoguchi, 2014; Mollick & Nguyen, 2015; MengYun et al., 2018; Anisak & Mohamad, 2019; Husain, Tiwari, Sohag, & Shahbaz, 2019; Makin, 2019, Tabash et al., 2020). Furthermore, the literature remains deficient regarding the asymmetrical impact of Gold-Oil- Exchange rate volatility on Indian stock indexes during three regimes such as the pre-2008 crisis regime, post-economic recession, and over the entire sample period. Mollick and Nguyen (2015) utilized the arbitrage pricing model to observe the relationship between stock price variations of the US energy sector and its determinants such as oil price volatility, currency value fluctuations, and yield spread. Results have revealed an asymmetrical association between variations in stock prices and their fundamental determinants during the global financial crisis of 2008. Anisak and Mohamad (2019) also observed that the agriculture sector of Indonesia remained resilient during the Asian financial crisis of 2008. Based upon earlier study discussions on Eastern Asian financial institutions’ resilience after the global financial crisis, Shakil et al. (2018) have examined the transmission of volatility shocks from foreign stock markets towards Singapore, Korean, Malaysian, and Thailand’s equity markets. Results estimated the transmission of exogenous volatility shocks from stock markets of developed economies like the US and Japan towards sampled countries. Husain et al. (2019) examined the inter-relationship between precious metal prices, international oil price volatility, and stock price volatility to understand that whether the US stock market has contributed to volatility shocks or acted as a receiver of volatility shocks. Results indicated that precious metal prices have contributed towards volatility shocks whereas crude oil, silver, and steel proved as receivers of volatility shocks. Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 3 of 30 Al-Rjoub and Azzam (2012) investigated the reaction of the Jordon stock exchange during the international economic crisis of 2008 by employing the GARCH modeling approach and by using time-series data over the period from 1992 to 2009. Authors have defined stock market crash according to previous specifications set by Patel and Sarker (1998) and Mishkin and white (2002) who have associated 35% and 20% depreciation in stock indexes, respectively, as an illustration of stock market turmoil. Findings suggested that the international economic crisis contributed to adverse impacts on the stock market and the financial sector was most adversely affected by the crisis. Sheikh, Asad, Ahmed, and Mukhtar (2020) have divided the time-series data into three threshold periods such as before the economic crunch period, after the economic crunch, and over the entire period to investigate the asymmetrical impact of oil price volatility, gold prices, and currency fluctuations on Karachi stock exchange of Pakistan. Muthukumaran et al. (2011) symmetrically examined the impact of the international economic crisis on the Indian stock market and found adverse consequences of the international economic crisis on the Indian stock market. However, limited efforts have been made to investigate the asymmetrical impact of Oil-Gold- Exchange rate volatility on Indian stock indexes during pre-crisis and post-crises regime. 1.1. Research motivation The implications of the international economic crisis were passed on to the Indian financial system via three different networks, namely the exporting industry, currency fluctuations, and the financial market of India (Kumar & Vashist, 2009). Although it has been observed that the Indian economic system, including the insurance sector, stock markets, commercial loans, foreign remittances have not remained unaffected by the economic crisis of 2008 but banking sector of India has not been exaggeratedly subjected to the economic recession in 2008 (Kumar & Vashisht, 2009; Asad et al., 2018). For example, one of the India’s largest banks named as ICCI, owing to its successful financial history, is partly impacted but showed resilience to the great recession in 2008. Before the crisis, the Indian stock exchange appreciated to 21,000 index points, but during the crisis, the Indian stock market witnessed almost 60% depreciation in the stock market indexing and a decrease of some USD 1.3 trillion in market capitalizations (Kumar & Vashisht, 2009). This depreciative trend in stock indexes is partly attributed to international Equity shareholders’ withdrawals of 12 billion US$ from the stock exchange during the global economic crisis. For purpose of stabilizing the financial position of Parent firms, global investors removed those contributions (Kumar et al., 2009). Commercial loans have nearly dried out, particularly for corporate financing and medium-term borrowing from international banks. Due to high-interest rates and limited money supply within the economy, the exchange rates have declined and the situation of firms that rely on imported goods has been further deteriorated (Asad, 2010; Muthukumaran et al., 2011). The significant decrease in India’s demand for exporting goods in the major international markets was yet another shock towards economic structure due to the international crisis. The first industry impacted was Jewels and Gems, which in November had laid off more than 0.3 million workers. The volatility in developed countries’ capital markets has negatively impacted all foreign direct investment inflows and the external commercial borrowing levels. During the global financial crisis, Indian companies attempted to gain only US$ 1800 million for trade loans from foreign countries since 2008 that is 41% smaller as compared to commercial credit generated in the last year (Kumar & Vashisht, 2009). International remittances from Indians residing and working in global markets during the international economic crunch immediately felt the effects of the financial recession, as international remittances depreciated yearly by 0.5%. Throughout the last four months of 2008–2009, the effect is greater, with the influx of external remittances falling more than 29% in contrast with the preceding year. However, in existing literature-limited efforts have been made to explore the asymmetrical impact of macroeconomic volatility on Indian stock indexes, which motivates the researcher to investigate the asymmetric impact of gold prices, oil prices, and INR/USD on Bombay stock indexes during three different regimes. Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 4 of 30 In Figure 1, the horizontal axis of gold prices, oil prices and exchange rate portray a number of years from 2003 to 2020 and the vertical axis displays price-related information for underlying independent variables. According to Figure 1, Bombay stock indexes (BSE-100) have been depreciated from 6000 index points to only 2000 index points, whereas oil prices, gold prices, and exchange rates (USD/INR) have also shown indications of positive and negative shocks during the crisis regime. Figure 1 displays that Indian stock indexes have been adversely affected during the global financial crisis, whereas gold prices, international crude oil prices, and USD to INR moved in the same direction as that of Indian stock indexes during the global financial crisis of 2008. Figure 1 also demonstrates that international oil prices, gold prices, and currency values have both positive and negative shocks, which furthermore motivated the researchers to find out the asymmetrical impact of these variables on Indian stock indexes before the crisis, after the crisis, and over the entire period. Figure 2, shows an asymmetrical relationship between Bombay stock indexes, exchange rate, crude oil prices, and gold prices. However, this nonlinear association Figure 1. International crude oil prices, gold prices, and USD/INR and BSE-100 indexes from April 2003 to April 2020. Figure 2. Scatter plot matrix diagram. Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 5 of 30 between variables will be furthermore validated by the BDS test of nonlinearity in the results section (see Table 4). In the existing literature, some of the researchers have utilized the arbitrage pricing model (Christofi et al., 1993; Günsel et al., 2009; Mollick & Nguyen, 2015; Saumya, 2012; Yan & Yang, 2016), while other have utilized efficient market hypothesis as an underpinning theoretical model in order to explore symmetrical and asymmetrical linkages between Gold-Oil-Exchange rates and stock indexes (Floros & Vougas, 2008; Hatemi-J, 2012; Singhania & Prakash, 2014; Wickremasinghe, 2011). To the best of our knowledge, this is the first research article which is intended to answer the research question about the asymmetrical impact of international oil prices, gold prices, and exchange rate fluctuations on the Bombay stock exchange before and after the international economic recession of 2008 and over the entire duration from April 2003 to May 2020. 1.2. Research gaps We have identified the following four most important research gaps in the existing literature. Firstly, in existing literature number of research articles have only explored linear linkages between stock indexes, exchange rate fluctuations, oil price volatilities, and gold prices by utilizing linear models like VECM (Badry, 2019; Keswani & Wadhwa, 2018; Neveen, 2018; Rajesh, 2019; Sahu et al., 2014; Shiva & Sethi, 2015), VAR (Areli Bermudez Delgado et al., 2018; Ghulam, 2018; Huang et al., 2018), ARDL modeling approach (Ho, 2018; Singhal et al., 2019) and limited efforts have been made to explore the asymmetrical impact of Exchange rates-Gold-Oil price volatility on stock indexes by using NARDL model by (Shin et al., 2014). Secondly, some of the research articles have explored linkages between macroeconomic fluctuations and stock indexes of the United states (Liang et al., 2020), Pakistan (Asad & Farooq, 2009; Sheikh et al., 2020; Tabash et al., 2020), Malaysia (Al-hajj et al., 2018; Almansour et al., 2016), China (Fan et al., 2014; Huang et al., 2018; Lin & Chen, 2019; You et al., 2017; Zhang et al., 2019), Indonesia (Lentina Andriansyah & Messinis, 2019; Simbolon & Purwanto, 2018), Sri-Lanka (Wickremasinghe, 2011), Bangladesh (Choi et al., 2019) and no efforts have been made to explore Gold-Oil-Exchange rates and stock index nexus asymmetrically for Indian stock exchange. Thirdly, some of the research articles have explored the asymmetrical impact of macroeconomic volatility on Indian stock indexes (Pandey & Vipul, 2018) but they have not utilized NARDL modeling in order to decompose underlying independent variables into their positive and negative shocks. The disintegration of oil prices, gold prices, and exchange rates into their positive and negative cumulative signs help to investigate whether investors are reacting only to positive fluctuations in exchange rates, gold prices and oil prices or negative fluctuations associated with these variables are also value relevant. In the existing literature, some of the researchers have utilized only gold prices (Arfaoui & Ben Rejeb, 2017; Shakil et al., 2018; Tuna, 2018) and others have utilized only oil prices (Lardic & Mignon, 2008; Sahu et al., 2014; Mollick & Nguyen, 2015; Wen et al., 2017; Abdel-Latif et al., 2018; Areli Bermudez Delgado et al., 2018; Pandey & Vipul, 2018; Kumar, 2019; A. Mouna, 2019; Narayan, 2019; Charfeddine & Barkat, 2020) in order to examine their linkages with stock market indexes. In this research article we have utilized gold prices, oil prices, and currency value fluctuations (USD/ INR) simultaneously in order to examine asymmetrical linkages with Bombay stock indexes. Moreover, amongst previously published research articles, few researchers have explored asymmetrical linkages of macroeconomic fluctuations with stock indexes (Ajaz et al., 2017; Bahmani- Oskooee & Saha, 2016; Kocaarslan & Soytas, 2019; Kumar, 2019; Liang et al., 2020; Singhal et al., 2019) but did not examine the role of the international economic recession of 2008 in effecting asymmetrical association between gold, oil, currency values, and Bombay stock indexes, and these studies were also outside of the Indian context. Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 6 of 30 1.3. Research objectives This research article is projected to aim for the following three important research intents. (1) To investigate asymmetrical short-run and long-run impact of gold prices on Bombay stock index during the pre-economic recessionary regime, post-recession and over the entire sample duration. (2) To investigate asymmetrical short-run and long-run impact of oil prices on Bombay stock index during the pre-economic recessionary regime, post-recession and over the entire sample duration. (3) To investigate asymmetrical short-run and long-run impact of exchange rate fluctuation 1 on Bombay stock index during the pre-economic recessionary regime, post-recession and over entire sample duration. 1.4. Summarized research findings and remaining structure of document In long run and before the economic recession period of India, investors have only reacted significantly to negative shocks associated with gold prices, exchange rate fluctuations, and oil prices, however in long run after the crisis, investors did not react to positive and negative shocks to the exchange rate fluctuations. This shows that the Indian economy has been relying on imports and can be classified as an import centric economy before the 2008 economic crunch. Negative shocks to exchange rate fluctuations (dollar values against Indian rupee) causes’ local currency appreciation, which may not be more suitable for exporters relying on exported goods for earning revenues, and hence, increases their expenses. However, local currency appreciation yields a positive impact on Bombay stock indexes means that importers of India have been greatly aided due to local currency appreciation. There is an asymmetrical relationship between the Gold-Oil- exchange rate and stock indexes before the crisis; however, after the crisis, the asymmetrical association is only established between oil price volatility and Bombay stock indexes for a longer period. This shows that the regime is extremely important while making financial investments and decisions. The rest of the paper is structured in the following six parts. The first part explains the literature review, in which authors have explained the linear-nonlinear impact of underlying variables on stock indexes. The second and third parts deal with the data and research methodological portion. The research methodological portion explains the procedure for implementation of unit root test, BDS test of nonlinearity, NARDL, and asymmetrical ECT estimation. The fourth, fifth, and sixth parts deal with results, discussion, and conclusion, respectively. Results of BDS test for the determination of non-linearity are reported in table 4. 2. Literature review The literature review of this article is divided into two different segments: the first section explains the symmetric and asymmetrical impact of macroeconomic variability on stock indexes and the second, third, and fourth sections explicates the symmetrical and asymmetrical impact of selected variables such as gold prices, international crude oil prices, and exchange rate on stock indexes, respectively. The main purpose of dividing the literature review into two different types of segments is to find out imperative research gaps, and current research findings from published articles, which have explored stock index-macro economy nexus symmetrically or linearly and asymmetrically or nonlinearly. 2.1. Linear and nonlinear impact of macroeconomic volatility on stock indexes The expediency of nonlinear modeling for estimating and forecasting stock indexes has been debated extensively. McMillan (2012) has utilized financial and nonfinancial variables for predicting variations in UK share market returns through exploring smooth transitional modeling techniques. Findings suggested that the utilization of nonlinear modeling techniques can contribute useful practical knowledge for shareholders, investors, and strategists at the government level. Chronopoulos, McMillan, Papadimitriou, and Tavakoli (2018) explored the symmetrical relationship between investing activities of company executives and forecasted future returns of stocks and Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 7 of 30 indexes, gold prices, oil prices, and exchange rate (USD/INR) have been divided into three regimes. The complete sample period is comprised of 208 observations from April 2003 to May 2020. Preeconomic crisis regime is comprised of 58 observations covering the period from April 2003 to January 2008 and post-crisis regime consists of 151 observations over the period from January 2009 to May 2020. The main purpose of dividing the time-series period into three different regimes is to scrutinize the asymmetrical impact of underlying regressors on stock prices for three multiple regimes. This classification of division is in line with (Ajaz et al., 2017; Andriansyah & Messinis, 2019; Hung, 2019; Neveen, 2018) 3.1. Research methodology The Implementation of the asymmetrical modeling approach by Shin et al. (2014) is very much similar to the symmetrical ARDL model purposed by (Pesaran et al., 2001); however, the main difference is the disintegration of stock indexes and exchange rate into their corresponding positive and negative shocks in case of NARDL model. The decomposition of both regressand, as well as regressor, cannot be possible in a symmetrical ARDL modeling approach. NARDL model can be applied when none of the variables is exhibiting nonstationary trends after first differencing, NARDL model can also be estimated when some of the variables are stationary at a level while other becoming stationary at first differencing, this means that we may have combinations of I (0) and I (1) variables in ARDL or NARDL equation. In this research article, we have utilized 4 different types of unit root tests such as Augmented dickey-fuller (ADF), Philips Peron (PP), KPSS (Kwiatkowski Philips Schmidt Shin), and Zivot Andrew unit root test for structural breakpoint analysis. Moreover, the BDS test is also utilized to estimate nonlinear dependencies in data sets. Estimation of the BDS testing approach furthermore helps us to analyze whether time series are identically and independently distributed or linearly dependent. The null hypothesis of the BDS test stated that time series have a linear dependency, and the alternative hypothesis stated that time series are not linearly dependent. If the value of BDS test statistics is less than critical valuesthen we cannot be able to reject the null hypothesis that time series are linearly dependent, and in this case, the ARDL modeling approach is more appropriate. The general form BDS test is given BDSε;m¼ pN Cε;mC2;1  �m h i pVε;m (4a) Vε;m is defined as the standard deviation of pN Cε;mC2;1  �m h i The general form of symmetrical Error correction term is given Δyt¼a0þ∑p1 i¼1biΔyt1þ∑q1 i¼1ciΔxtiþρyt1þθxt1þεt;(4b) The general form of the linear ARDL model is represented by equation 4c ΔLnBSE 100t¼α0þ∑p1 i¼1b1ΔLnBSE 100tiþ∑q1 i¼1c1;jΔLnGoldpricesti þ∑q2 i¼1c2;jΔLnoilpricestiþ∑q3 i¼1c3;jΔLnexchangeratetiþρLnBSE 100t1 þθ1Goldpricest1þθ2Oilpricest1þθ3Exchangeratet1þεt (4c) Equation 4b and 4c represents symmetrical ECT and linear ARDL modeling approach. In both equations, stock indexes of the Indian stock exchange are selected as regressand and exchange rate fluctuation, oil prices, and gold prices are selected as regressors. Short-run coefficient is represented by b and c, whereas ∆ is utilized as difference operator for both stock indexes and gold price, oil prices, and exchange rate fluctuations. The long run coefficient is represented by ρ and θ. According to (Pesaran et al., 2001) if the value of F statistics is larger than upper bound and lower bound critical values then we can infer long-run co-integrating association (ρ≠ θ�0Þbetween macroeconomic fluctuations and stock indexes otherwise there exists no co-integration (ρ= θ¼0Þ. One of the disadvantages of symmetrical ARDL modeling is that it is unable to disintegrate Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 14 of 30 independent variables into positive and negative shocks so that we cannot be able to examine the nonlinear association between variables. In the case of the NARDL model by (Shin et al., 2014), stock indexes and gold prices, international crude oil prices, and exchange rates can be broken down into positive and negative shocks such as yt¼βþxþ tþβxtþεt;(4d) xt¼x0þxþ tþxt;(4e) In equation 4d, long-run asymmetrical coefficient is denoted by βþxþ t and βxt respectively and εt is epitomized as deviance from long-term equilibrium association, positive and negative shocks to regressors are represented in the following equation xt¼x0þxþ tþxt(4f) Hence, fluctuations in the exchange rate, gold prices, oil prices, and stock index variability can be broken down in the following ways as purposed by (Shin et al., 2014) exchangerrateþ t¼∑ t i¼1 Δexchangerateþ i¼∑ t i¼1 max Δxi;0  � exchangeratet¼∑ t i¼1 Δexchangeratei¼∑ t i¼1 min Δexchangeratei;0  � Goldpricesþ t¼∑ t i¼1 ΔGoldpricesþ i¼∑ t i¼1 max Δxi;0  � Goldpricest¼∑ t i¼1 ΔGoldpricesi¼∑ t i¼1 min Δgoldpricesi;0  � oilpricesþ t¼∑ t i¼1 Δoilpricesþ i¼∑ t i¼1 max Δxi;0  � oilpricest¼∑ t i¼1 Δoilpricesi¼∑ t i¼1 min Δoilpricesi;0  � BSE 100þ t¼∑ t i¼1 ΔBSE 100þ i¼∑ t i¼1 max Δyi;0  � BSE 100t¼∑ t i¼1 ΔBSE 100i¼∑ t i¼1 min ΔBSE 100i;0  � As stated above, asymmetrical ECT is an extension of equation 4b and can be modified as is in equation 4g ΔnBSE 100t¼a0þ∑p1 i¼1biΔBSE 100tþ∑q1 i¼1ciþΔexchangerateþ t1 þ∑q1 i¼1ciΔexchangeratet1þ∑q1 i¼1ciþΔGoldpricesþ t1þ∑q1 i¼1ciΔGoldpricest1 þ∑q1 i¼1ciþΔOilpricesþ t1þ∑q1 i¼1ciΔOilpricest1þρBSE 100t1þθiþexchangerateþ t1 þθiexchangeratet1þθiþGoldpricesþ t1 þθiGoldpricest1þθiþOilpricesþ t1þθiOilpricest1þεt; (4g) Asymmetrical NARDL model is an extension of equation 4c and can be written as Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 15 of 30 ΔLnBSE 100t¼a0þ∑ p1 i¼1 biΔBSE 100tþ∑ q1 i¼1 ciþΔexchangerateþ t1 þ∑ q1 i¼1 ciΔexchangeratet1þ∑ q1 i¼1 ciþΔGoldpricesþ t1þ∑ q1 i¼1 ciΔGoldpricest1 þ∑ q1 i¼1 ciþΔOilpricesþ t1þ∑ q1 i¼1 ciΔOilpricest1þρBSE 100t1þθiþexchangerateþ t1 þθiexchangeratet1þθiþGoldpricesþ t1þθiGoldpricest1 þθiþOilpricesþ t1þθiOilpricest1þεt; (4h) Long-term asymmetrical co-integration existed if the value of F statistics is greater than upper bound and lower bound critical values. Lower bound value is estimated for variables with no seasonality trends at level and upper bound critical values are estimated for variables becoming stationary at first differencing. Hence, regressor and regressand should not be becoming stationary at second difference. Moreover, in case of a mixture of I (0) and I (1) variables, F statistics should be greater than upper bound critical values and if F statistics fall in between upper bound and lower bound then asymmetrical error correction term is utilized for investigating the long-term asymmetrical association between stock indexes and macroeconomic volatility. Wald test statistics are utilized for the asymmetrical impact of macroeconomic fluctuations on stock indexes for shorter ∑ q1 i¼1 cþ k;i�∑ q1 i¼1 ck;i !and longer horizons θþ≠ θ(only in case of the significant and negative value of ECT). Wald test statistics is also computed to examine the symmetrical association between regressors and Bombay stock exchange indexes for shorter ∑ q1 i¼1 cþ k;i¼∑ q1 i¼1 ck;i !and longer horizons (θþ=θ) (Baz et al., 2019; Charfeddine & Barkat, 2020; Jung et al., 2020; Kumar, 2019; Liang et al., 2020; Rajesh, 2019; Salvatore, 2019; Shahzad et al., 2017) 4. Results The results section is divided into five parts: the first section deals with descriptive statistics of sample data. The second section deals with testing for nonlinearity within time series by using the BDS test of nonlinearity. The third and fourth sections deals with the detection of seasonality effects in time-series data and converting stationarity in data into nonstationary, respectively. The fifth sections deal with the estimation of the nonlinear NARDL model with asymmetrical Wald test statistics. Tables 1–3 represents descriptive statistics of three different regimes such as over the entire sample period, pre-crisis, and post-crisis regime. After comparing descriptive statistics in Table 2 with Table 3, it is revealed that the standard deviation of all independent and dependent variables is greater in port crisis regime than SD of gold prices, oil prices, exchange rate fluctuations, and stock indexes in the Pre-crisis regime. In comparison to the pre-economic recession, these fundamental responses and predictor variables more profoundly deviated from their respective mean values in the post-economic recession regime. In comparison, the maximum USD value against the INR during a pre-crisis period is lower than during the post-crisis era, which suggests that India’s rupee has seen a greater post-crisis depreciative tendency. Moreover, gold prices, oil prices, and stock indexes have also experienced greater maximum values after the economic crisis regime. Table 4 confirms that the NARDL model is an appropriate choice for estimating asymmetrical fluctuation of macroeconomic volatility on stock indexes. The null hypothesis of the BDS test is rejected for gold prices, exchange rate fluctuations, oil prices, and stock indexes which confirms the evidence of nonlinearity within time-series data (Brock et al., 1996). The null hypothesis is rejected as the value of BDS test statistics is greater than critical values. This stated that time series are not independent or identically distributed. Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 16 of 30 Tables 5–8 presents the results of unit root estimation by utilizing the Augmented Dickey–Fuller test (ADF), Philips Peron (PP), and Kwiatkowski Philips Schmidt Shin (KPSS) unit root. Estimation of the unit-roots approach confirms that all regressors, as well as regressand, are becoming stationary after differencing 1 time, for example, at the level they are exhibiting seasonality trends, but after differencing 1 time, they all have converted into stationary; moreover, it is also confirmed that none of the variables is integrated of second order or I (2). However, the Zivot Andrew unit root test (see Table 8) shows that all regressors are stationary at level but regressand has become stationary after first differencing. So according to ADF, PP, and KPSS, we have variables integrated in the same order but according to ZA unit root, we are having a mixture of I (0) and I (1) variables. Table 8 and Figure 3 confirms the presence of structural breaks for exchange rate fluctuation, gold prices, oil prices, and stock indexes, however, all regressors are stationary at level but Bombay stock indexes exhibit seasonality trends at the level and became stationary after differencing 1 time. Table 1. Descriptive statistics of time-series data during the entire period Descriptive statistics Exchange rate Gold prices Oil prices BSE_100 Mean 54.34147 1082.986 67.72077 6182.863 Median 49.61500 1201.100 64.37000 5629.575 Maximum 75.47000 1828.500 140.0000 12,236.19 Minimum 39.17000 339.1000 19.56000 852.7800 Std. Dev. 10.57476 410.1265 23.79516 3128.885 Skewness 0.339756 −0.316924 0.353056 0.266743 Kurtosis 1.598457 2.042998 2.410493 2.055644 Jarque-Bera 21.02584 11.41934 7.332974 10.19560 Probability 0.000027 0.003314 0.025566 0.006110 Sum 11,303.02 225,261.2 14,085.92 1,286,035. Sum Sq. Dev. 23,147.88 34,818,170 117,205.4 2.03E+09 Observations 208 208 208 208 Table 2. Descriptive statistics of time series data during Pre-crisis period Descriptive statistics Exchange rate Gold prices Oil prices BSE_100 Mean 44.40078 524.7879 56.03638 2832.181 Median 44.73500 458.2000 58.43500 2419.880 Maximum 47.69000 922.7000 95.98000 6469.480 Minimum 39.17000 339.1000 25.80000 852.7800 Std. Dev. 2.067441 142.1641 18.12358 1397.371 Skewness −0.958633 0.693522 0.156937 0.753435 Kurtosis 3.398398 2.571015 2.343364 2.793109 Jarque-Bera 9.267029 5.094138 1.280078 5.590862 Probability 0.009721 0.078311 0.527272 0.061089 Sum 2575.245 30,437.70 3250.110 164,266.5 Sum Sq. Dev. 243.6357 1,152,007. 18,722.47 1.11E+08 Observations 58 58 58 58 Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 17 of 30 Four different types of structural breakpoints have been identified through estimating the Zivot Andrew unit root test, and for that purpose, four different types of binary dummy variables will be included in the simple regression model (0LS) and most significant dummy variables are retained for NARDL model estimation. In the existing literature, several researchers have adopted the same methodology in case of identification of more than one structural breakpoint (Kocaarslan & Soytas, 2019; Tehreem, 2018; Sheikh et al., 2020). Estimation of multiple unit root tests and BDS tests for nonlinearity confirms the suitability to utilize the NARDL model for the nonlinear co-integrating association between macroeconomic volatility and stock indexes. Tables 9–11 presents the results Table 4. Brock, Dechert, Scheinkman, and LeBaron Test for Nonlinearity Dimension BDS Statistic Std. Error z-Statistic Prob. BDS test for Exchange rate 2 0.185075 0.003165 58.47546 0.0000 3 0.311134 0.005001 62.20946 0.0000 4 0.397015 0.005918 67.08194 0.0000 5 0.455030 0.006128 74.24964 0.0000 6 0.494663 0.005871 84.26181 0.0000 BDS test for Gold prices 2 0.190145 0.003626 52.43238 0.0000 3 0.323702 0.005756 56.24039 0.0000 4 0.417189 0.006841 60.98002 0.0000 5 0.481965 0.007116 67.72864 0.0000 6 0.526958 0.006848 76.95468 0.0000 BDS test for Oil prices 2 0.161639 0.003556 45.45969 0.0000 3 0.269947 0.005639 47.86811 0.0000 4 0.339181 0.006698 50.63557 0.0000 5 0.381222 0.006963 54.75338 0.0000 6 0.406094 0.006695 60.65561 0.0000 Table 3. Descriptive statistics of time-series data during Post-crisis period Descriptive statistics Exchange rate Gold prices Oil prices BSE_100 Mean 58.17618 1296.332 72.36795 7465.197 Median 61.51250 1278.500 69.80000 6715.360 Maximum 75.52500 1828.500 140.0000 12,236.19 Minimum 39.19000 716.8000 19.56000 2619.500 Std. Dev. 10.16663 241.4804 24.20480 2602.872 Skewness −0.216350 0.152359 0.213112 0.244098 Kurtosis 1.715713 2.645960 2.181252 1.901250 Jarque-Bera 11.63195 1.372825 5.360596 9.095149 Probability 0.002980 0.503379 0.068543 0.010593 Sum 8842.780 195,746.2 10,927.56 1,127,245. Sum Sq. Dev. 15,607.42 8,746,918. 87,880.86 1.02E+09 Observations 151 151 151 151 Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 18 of 30 of the NARDL model for the time period before the global economic crisis, after the economic crunch, and over the entire sample period from April 2003 to May 2020. Table 9 reports the results of the NARDL model for estimating the asymmetrical association between international oil price, gold prices, exchange rate fluctuations, and Bombay stock indexes. Results have reported an asymmetrical relationship between all underlying regressors Table 5. ADF unit root test Variables At Level First Differencing Order of Integration Trend Intercept Trend Intercept Stock Indexes −1.20 −2.20 −15.10*** −23.10*** I(1) Exchange rate −1.00 −1.90 −13.26*** −20.06*** I(1) Gold prices −1.30 −1.50 −12.14*** −25.24*** I(1) Oil prices −1.20 −2.35 −10.05*** −18.05*** I(1) Table 6. PP unit root test Variables At Level First Differencing Order of Integration Trend Intercept Trend Intercept Stock Indexes −2.30 −2.89(.19) −18.10*** −21.00*** I(1) Exchange rate −1.90 −2.12(0.77) −19.76*** −23.06*** I(1) Gold prices −2.60 −2.75(.39) −21.14*** −20.14*** I(1) Oil prices −1.85 −2.55(0.81) −16.05*** −18.15*** I(1) Table 7. KPSS unit root test Variables At Level First Differencing Order of Integration Trend Intercept Trend Intercept Stock Indexes 1.63*** 0.60*** 0.23 0.040 I(1) Exchange rate 1.70*** 0.72*** 0.31 0.06 I(1) Gold prices 1.90*** 0.5*** 0.19 0.014 I(1) Oil prices 2.10*** 0.27*** 0.11 0.01 I(1) Table 8. ZA Unit root test Variables At Level First Differencing Breakpoint value and year Trend Intercept Trend Intercept Stock Indexes −1.76 −2.10 −4.20*** −5.60*** 58 th = Jan 2008 Exchange rate −4.35*** −4.60*** ————– ———— 103 rd = October 2011 Gold prices −4.10*** −3.95*** ————– ———— 124 th = July 2013 Oil prices −5.50*** −6.10*** ————– ———— 136 th = July 2014 Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 19 of 30 and Bombay stock indexes for shorter and longer horizons. Before the global financial crisis and for longer horizons, investors have only reacted to negative shocks associated with gold prices, oil prices, and exchange rate fluctuations. However, in the short run and before the economic crunch regime, only positive shocks to gold and oil prices have a direct influence on Bombay stock indexes, and only negative shocks to exchange rate fluctuation have an indirect association with Bombay stock indexes. This means local currency appreciation has a positive influence on Bombay stock indexes in the shorter and longer run during the pre-crisis regime. One of the greatest possibilities of a positive relationship between local currency appreciation and stock indexes is the reliance of the local economy on imports rather than exports (Chkir et al., 2020). An increase in dollar value against the Indian rupee causes producer prices to move in an upward direction and causes depreciation in profitability for firms relying on imported products for manufacturing their finished goods (Jain & Biswal, 2016). However, the depreciation of the dollar against the Indian rupee causes local currency appreciation and this scenario is profitable for organizations relying on exports rather than imports (Xie et al., 2020). Before the global financial crisis, the Indian economy is proved to import centric economy rather than export-oriented. The negative relationship between exchange rate fluctuation and stock indexes is in line with (Arfaoui & Ben Rejeb, 2017; Kumar, 2019). Table 9 also reports results for the nonlinear ECT term, which is negative and significant. This validates long-run asymmetrical association between regressors and regressand. Appropriate NARDL model is selected on the basis of the Akaike information criterion and all residual diagnostics such as the Durbin–Watson test, Breusch Pagan test, and Ramsey Reset test confirmed that the model is free from heteroscedasticity, autocorrelation, and miss-specifications. Tables 10 and 11 reports the results of the NARDL model estimated for the period after the global financial crisis and the entire sample period, respectively. Unlike the impact of exchange Figure 3. Structure breakpoint unit root graphical representation for stock indexes, exchange rate, gold prices and oil prices. Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 20 of 30 rate fluctuations on Bombay stock indexes before the crisis, after the global economic recession, and for longer horizons, both positive and negative shocks to exchange rate fluctuation have no impact on Bombay stock indexes. However, both positive and negative shocks to international gold prices and only positive shocks to international oil price volatility are value relevant for investors for longer horizons. Appreciation in positive (negative) shocks to gold prices causes depreciation (appreciation) in Bombay stock indexes. This means that investors are reacting equally to both positive and negative shocks to gold prices after the economic recession regime and in the long term. In the short run and after the global financial crisis, investors reacted to only negative shocks associated with gold and oil prices, and negative shocks to exchange rate fluctuations remain statistically insignificant. Table 10 furthermore reports that the NARDL model is free from autocorrelation, heteroscedasticity, and misspecification bias. The value of error correction model (−0.21) estimated for a time Table 9. NARDL (1, 0, 4, 3, 5, 1, and 0) model before crisis regime Long run asymmetrical results Variables Coefficient Std.errors t-value p-value BSE_100 index Exchnage rate+ −0.002775 0.015395 −0.180224 0.8582 Exchange rate- −0.077900** 0.033013 −2.359645 0.0250 Gold prices+ 0.000524 0.000403 1.297942 0.2042 Gold prices- 0.006869*** 0.002154 3.188571 0.0033 Oil prices+ 0.002702 0.003005 0.899265 0.3757 Oil prices- −0.019418*** 0.004624 −4.199043 0.0002 Asymmetrical ECT −0.593629 0.066745 −8.893972 0.0000 Dummy2008 3.65*** Long run asymmetries θiþexchangerateþ t1¼θiexchangeratet1−2.57** θiþGoldpricesþ t1¼θiGoldpricest14.185*** θiþOilpricesþ t1¼θiOilpricest1−8.76*** Short run results ∆Exchange rate+ −0.00043 0.00085 0.50 0.9921 ∆Exchange rate- −0.034200*** 0.011952 −2.861391 0.0076 ∆Gold prices+ 0.001178*** 0.000315 −4.225019 0.0002 ∆Gold prices- −0.000266 0.000674 −0.394692 0.6959 ∆Oil prices+ 0.007998*** 0.001868 4.281915 0.0002 ∆Oil prices- −0.00012 0.000948 0.12 0.712 Short run asymmetries P q1 i¼1 ciþΔexchangerateþ t1¼P q1 i¼1 ciΔexchangeratet1 −9.`11*** −7.81*** P q1 i¼1 ciþΔOilpricesþ t1¼P q1 i¼1 ciΔOilpricest1 −12.76*** Residual diagnostics Autocorrelation 0.354(0.79) DW = 1.93 Heteroscedasticity 1.42(0.82) Ramsey reset test 0.23(0.81) R-square value 0.65 Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 21 of 30 period after economic recession regime depict that there exist an asymmetrical association between stock indexes, oil prices, gold prices and exchange rate fluctuations as the model is attaining long-term equilibrium at the particular speed of 21% per month. Wald test statistics confirms that there is the existence of long-run asymmetries for oil prices and stock indexes in long term and asymmetrical association exists between all regressors and stock indexes for shorter horizons. Tables 9–11 are also divided into three parts: the first part explains the asymmetrical long-run association between regressors and regressand and the second part explains short-run asymmetrical impact of gold prices, oil prices, and exchange rate fluctuations on stock indexes. The third part is related to residual diagnostics which explains that all three models of NARDL estimated for a time period of pre-crisis, after the crisis, and for overall timespan is free from model misspecification bias, autocorrelation, heteroscedasticity. Moreover, Table 11 reports that only positive Table 10. NARDL model after crisis regime Long run asymmetrical results Variables Coefficient Std.errors t-value p-value BSE_100 index Exchnage rate+ −0.010804 0.015008 −0.719861 0.4743 Exchange rate- 0.043067 0.031975 1.346917 0.1828 Gold prices+ −0.000452** 0.000212 −2.129680 0.0371 Gold prices- −0.000704*** 0.000259 −2.718700 0.0085 Oil prices+ 0.015074*** 0.005645 2.670187 0.0096 Oil prices- 0.002766 0.001995 1.386205 0.1706 Asymmetrical ECT −0.21*** Dummy2008 −2.24* Long run asymmetries θiþexchangerateþ t1¼θiexchangeratet1−1.38 θiþGoldpricesþ t1¼θiGoldpricest1−1.31 θiþOilpricesþ t1¼θiOilpricest1−2.54** Short run results ∆Exchange rate+ 0.019981** 0.009916 2.014995 0.0482 ∆Exchange rate- −0.012446 0.010529 −1.182098 0.2416 ∆Gold prices+ 0.000136 0.000177 0.767637 0.4456 ∆Gold prices- 0.000659*** 0.000250 2.631954 0.0107 ∆Oil prices+ −0.000282 0.002189 −0.128680 0.8980 ∆Oil prices- 0.004949*** 0.001624 3.047049 0.0034 Short run asymmetries P q1 i¼1 ciþΔexchangerateþ t1¼P q1 i¼1 ciΔexchangeratet1 −8.98*** P q2 i¼1 ciþΔGoldpricesþ t1¼P q2 i¼1 ciΔGoldpricest1 −3.43*** P q1 i¼1 ciþΔOilpricesþ t1¼P q1 i¼1 ciΔOilpricest1 −7.81*** Residual diagnostics Autocorrelation 1.24(0.29) DW = 2.01 Heteroscedasticity 0.88(0.69) Ramsey reset test 1.29(0.25) R-square value 0.71 Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 22 of 30 shocks of exchange rate fluctuations are having a positive impact on stock indexes in the longer run. This means that currency devaluations lead towards an appreciative impact on Bombay stock indexes. As we have already explained while interpreting the result of Table 9, currency devaluation is much favorable for organizations relying on exports rather than import-centric firms (Akbar et al., 2019). Some firms earning profitability by selling their product to external economies and earn foreign exchange in place of their exported goods, while import-oriented businesses rely on imported goods for manufacturing purposes (Kassouri & Altıntaş, 2020). Currency depreciation increases the profitability of exported centric economies and decreases the profitability of those firms utilizing imported raw materials for finished goods. In the long run, short-run, and over the entire period from 2003 to 2020, investors have only reacted to positive shocks of exchange rate fluctuations and did not consider negative shocks to exchange rate fluctuations as value relevant. However, over the entire sample period and for both shorter and longer horizons, only negative shocks to oil prices formulated Table 11. NARDL (1, 0, 4, 0, 3, 1, and 0) model over the entire sample period from 2003 to 2020 Long run asymmetrical results Variables Coefficient Std.errors t-value p-value BSE_100 index Exchange rate+ 0.006275*** 0.002371 2.646684 0.0088 Exchange rate- 0.002953 0.003672 0.804060 0.4224 Gold prices+ −4.35E-05 4.04E-05 −1.075366 0.2836 Gold prices- 9.22E-05* 4.88E-05 1.891065 0.0601 Oil prices+ 0.000482 0.000362 1.330760 0.1849 Oil prices- 0.005237*** 0.001069 4.897936 0.0000 Asymmetrical ECT Long run asymmetries θiþexchangeraeþ t1¼θiexchangeratet1−1.55 θiþGoldpricesþ t1¼θiGoldpricest1−1.89* θiþOilpricesþ t1¼θiOilpricest1−2.55** Short run results ∆Exchange rate+ 0.004658* 0.002626 1.773563 0.0777 ∆Exchange rate- 0.001931 0.003998 0.482940 0.6297 ∆Gold prices+ 1.76E-05 0.000152 0.115870 0.9079 ∆Gold prices- 0.000147 0.000166 0.881196 0.3793 ∆Oil prices+ 0.000717 0.001509 0.474996 0.6353 ∆Oil prices- 0.005127*** 0.001182 4.336547 0.0000 Short run asymmetries P q1 i¼1 ciþΔexchangerateþ t1¼P q1 i¼1 ciΔexchangeratet1 −0.89 P q2 i¼1 ciþΔGoldpricesþ t1¼P q2 i¼1 ciΔGoldpricest1 −1.11 P q1 i¼1 ciþΔOilpricesþ t1¼P q1 i¼1 ciΔOilpricest1 −3.75*** Residual diagnostics Autocorrelation 0.12(0.88) DW = 1.99 Heteroscedasticity 1.75(0.06) Ramsey reset test 1.21(0.55) R-square value 0.81 Asad et al., Cogent Business & Management (2021), 7: 1849889 https://doi.org/10.1080/23311975.2020.1849889 Page 23 of 30 Finance Research Letters, 101323. https://doi.org/10. 1016/j.frl.2019.101323 © 2020 The Author(s). 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