International Journal of Islamic Economics and Finance (IJIEF) Vol. 5(1), January 2022, pages 31-58 Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks Md Fouad Bin Amin1 Corresponding email: fbinamin@ksu.edu.sa Article History Received : May 30th, 2021 Revised : June 20th, 2021 December 1st, 2021 Accepted : December 10th, 2021 Abstract The soundness of financial institutions including banks depends on both internal factors and external factors. The profitability of the banks largely affected by external shocks like oil prices and stock prices. As an oil-exporting country, Saudi economy particularly its banking sector largely rely on the oil prices. This study examines the asymmetric impact of oil prices and stock prices on Saudi Islamic banks’ profitability for the period 2000-2020. Two Saudi Islamic banks’ profitability is examined by the factors like Return on Equity (ROE) and Return on Assets (ROA) with the help of a nonlinear autoregressive distributed lag (NARDL) model. The estimated results are observed to be unbiased and robust. The results of this study show that OILP and STOCKP have significant role in determining the Islamic banks’ profitability in Saudi Arabia. Both higher oil prices and stock prices have positive influence on ROE and ROA of Saudi Islamic banks. This study suggest that development and efficiency of Saudi stock market is important and macroeconomic policy should support the country’s economic diversification. The management of Islamic banks need to focus on effective risk assessment and market monitoring tools to face the fluctuation of oil prices and their stock prices as these factors affect their profitability. Besides, Saudi Islamic banks need to diversify their investment portfolios into more productive and export oriented private sectors such as Small Medium Enterprises (SMEs). This strategic policy will enable Islamic banks to absorb any future shock of oil prices without affecting their profitability. Keywords: Profitability; Oil prices; Stock price JEL Classification: C32; O13; 016; E44 Type of paper: Research Paper @ IJIEF 2022 published by Universitas Muhammadiyah Yogyakarta, Indonesia DOI: https://doi.org/10.18196/ijief.v5i1.11835 Web: https://journal.umy.ac.id/index.php/ijief/article/view/11835 Citation Amin, M. F. B. (2022). Asymmetric impact of oil prices and stock prices on bank’s profitability: Evidence from Saudi Islamic banks. International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58. DOI: https://doi.org/10.18196/ijief.v5i1.11835 1 Department of Economics, Kind Saud University, Riyadh, Saudi Arabia mailto:fbinamin@ksu.edu.sa https://doi.org/10.18196/ijief.v5i1.11835 https://doi.org/10.18196/ijief.v5i1.11835 https://crossmark.crossref.org/dialog/?doi=10.18196/ijief.v5i1.11835&domain=pdf Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 32 I. Introduction 1.1. Background A sound banking system is a prerequisite for the economic growth and development any economy (Habibullah & Eng, 2006; Priscilla & Ezeanyeji, 2019). Islamic countries mostly maintain dual banking systems i.e., conventional bank and Islamic bank (Massah and Al-Sayed, 2015). Islamic bank adopts Shariah principles and becomes a predominant player across the globe. Its performances outrun the counterpart and even proven resilient to face economic and financial crisis over the last few years (Tlemsani & Al Suwaidi, 2016). One of the key factors behind this success of Islamic banks is because of their Shariah compliant products and services. It finances long- term and short-term development projects and experiences sustainable growth in most of the Islamic countries (Thorsten Beck, Demirgüç-Kunt, & Merrouche, 2010; M. A. Hassan, Hafsa, & Muhammad, 2011). In Saudi Arabia, Islamic banking industry is well-developed which comprises of four Islamic banks, namely, Bank Al-Rajhi, Bank Al-Jazira, Bank Al-Bilad, and Bank Al-Inma. These banks aggregately possess 28.0% of total banking assets in the Kingdom. Al-Rajhi is the largest Islamic bank that owns 57.7% of the Islamic banking market share. In other estimation, the rest of three Islamic banks i.e., Al-Bilad, Al-Inma, Al-Jazira collectively hold 28.0% of total banking assets (Khan, Amin, Khokhar, Hassan, & Ahmad, 2018). Despite the world economic uncertainty, Saudi Islamic banking industry had witnessed a remarkable improvement in terms of achieving higher profit margins, lower cost of fund, asset size and quality, product-diversity, and outreach. This industry is growing at a rapid pace that contribute to the largest proportion of financing (82%) with the participation of conventional banks’ offering Islamic banking products and services (Fitchratings, 2021). The conventional banks are attracted by the higher profitability and growth of Islamic banks. The relations of bank’s profit with economic growth via financial sector development is well-established. Besides, the stock market development has significant impact on capital accumulation, productively and growth of banking sector (King & Levine, 1993; Levine & Zervos, 1998) . Bank and capital market are much related where one can be developed at the cost of other (Allen & Gale, 1999). Bank’s profitability is affected by its stock value. The bank faces challenging condition once its value of stock falls. On the other hand, bank’s profitablity depends on external shocks of oil prices and stock prices (Alaagam, 2019; Hesse & Poghosyan, 2009). For instance, the fluctuation in oil prices may harm bank’s profit directly due to the expansion of oil-based lending for businesses and projects and surplus liquidity. The banking sector of oil exporting country may be adversely affected by the decrease of oil Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 33 prices. This leads to fall of exports, government revenues and fiscal balance, GDP growth and equity prices, which creates negative impact on banks’ balance sheets and credit expansion. Besides, increase in oil prices might link to higher domestic demand which restore the higher banking performance by lending to low non-performing loan. In contrast, realizing the mechanism from aggregate supply side, oil price hike is beneficial to the Saudi economy as its product capacity expanded and lead to higher economic growth. This fact is proven by the incident of higher oil prices between 2005 and 2008 where most of the oil-based economies including Saudi Arabia funded long-term investment project for diversifying their domestic economies. Being a major oil-exporting country in MENA region, Saudi economy and its financial sector including banking industry largely rely on oil prices. According to Saudi General Authority of Statistics, the Saudi economy contracted by 7 percent in the 2020Q2 because of 61.8% decrease in oil exports, and both government and private sectors experienced a declining growth rates of 10.1 percent and 3.5 percent, respectively (General Autority of Statistics, 2020). On the other hand, Saudi stock market is appearing as one of key players due to its inclusion in the emerging market (Suhad & Tahar, 2021) and any volatility of this market also affect the banking sector (Alkhareif, 2016) . Previous studies conducted in Saudi Arabia focused on determinants of banks profitability by emphasizing the internal factors i.e., asset, liability, bank size, leverage ratio etc., while some other studies focus on the efficiency and productivity analysis of both conventional and Islamic banks in Saudi Arabia (M. Hassan, Amin, Khokhar, & Khan, 2020; M. Hassan, Khan, Amin, & Khokhar, 2018; Khan et al., 2018; Khokhar, Hassan, Khan, & Amin, 2020). As far as the existing literature is concerned, no study has conducted that examined the external factors like oil prices and stock prices that affect the Islamic banks’ profitability in Saudi Arabia by using non-linear approach. This study is an attempt to fill the gap in the existing literatures. 1.2. Objective This study attempts to examine the non-linear relations of oil prices and stock prices on the profitability of Saudi Islamic banks. The rate of Return on Equity (ROE) and Return of Assets (ROA) are used as the proxies of banks’ profitability. This study includes two Islamic banks, such as Bank Alrajhi and Bank Aljazira for the asymmetric analysis. The study objective is achieved by a nonlinear autoregressive distributed lag nonlinear autoregressive distributed lag (NARDL) model (Y. Shin, Yu, & Greenwood-Nimmo, 2014). This study is an attempt to contribute in the existing literatures by extending the asymmetric analysis with NARDL model. Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 34 Apart from this section, the rest of this paper is structured as follows: section two highlights on relevant past studies in relations to the variables under study, section three focuses on the data and research method, section four concentrates on the result and discussion of the estimated NARDL model and final section makes concluding remarks with policy recommendations. II. Literature Review This section highlights on the existing literatures on the bank’s profitability indicators such as Return on Equity (ROE) and Return on Assets (ROA). Also, the findings past studies that focus on the its relationships among the oil prices, stock prices and GDP. The main purpose of reviewing the extant literature is to make the theoretical foundation for constructing the models. 2.1. Banks’ Profitability The existing studies highlighting on bank’s profitability is presented by the proxy of two common factors such as Return on Equity (ROE) and Return on Assets (ROA). Some studies measure the bank’s profitability with these two indicators and consider as endogenous variables (Anwar & Herwany, 2006; Arora & Arora, 2013; T. Beck, Demirgüç-Kunt, & Levine, 1999; Kosmidou, 2008; Naceur & Goaied, 2008; E. Sharma & Mani, 2012; Staikouras & Wood, 2004; Sufian & Habibullah, 2010). Both ROE and ROA are the components of bank’s income statement which are calculated as the profit after paying the tax. The return on equity (ROE) indicates bank’s management ability and efficiency to utilize the funds of shareholders and it plays a key role in destemming the degree of financial leverage of the institution (Hassan & Bashir, 2003). On the other hand, ROA indicates the profit gained per value of assets which also shows the managerial capacity to gain profits by utilizing banks’ investment and financial (Hassan & Bashir, 2003). It also describes the bank’s ability to gain returns from diversified assets’ portfolio (Rivard & Thomas, 1997; Rosly & Bakar, 2003). Since ROE is influenced by ROA and the latter might be lower, the banks usually apply financial leveraging principles to enhance ROE for achieving the competitive advantages (Hassan & Bashir, 2003). 2.2. Determinants of Banks’ Profitability Empirical studies that measure the bank profitability are categorized into two factors i.e., internal and external. This study focuses on the oil prices, stock prices and GDP as the external factors of Islamic bank’s probability. These are the factors that are not controlled by the bank’s management; however, they have positive effect on the bank’s profitability. Following sub-section reviews related literatures on these external factors of bank’s profitability. Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 35 2.2.1. Oil Prices and Banks’ Profitability The oil-exporters depend on oil export which also creates a positive link with the profitability of their banking industries. Despite the fact, many studies ignore this factor in determining the bank’s profitability (Demirgüç-Kunt & Huizinga, 1999; Hassan & Bashir, 2003). Only few studies focus on oil price as an external factor of bank’s profitability. Hesse and Poghosyan (2009) highlight on MENA region by dividing the banks into three types: Islamic, conventional, and investment banks. Zantioti (2009) and Kpodar and Imam (2010) concentrate on Islamic banks’ profitability and observe that oil prices have positive and significant impact as it improves the banks’ financial condition with higher outreach in the case of net oil exporters in MENA region. Conversely, the net oil importing countries in the same region experience positive effect of GDP on bank’s profitability. This happens because of the increasing flow of deposits and economic activities. Besides, Hesse and Poghosyan (2009) evaluate the indirect impact of oil shocks on the Islamic, conventional and investment banks’ profitability in MENA region. This study also focuses other institutional and macroeconomic variables. Essayyad and Madani (2003) conduct a similar study and observe a positive link between bank’s profitability and oil prices in Saudi Arabia. Kpodar and Imam (2010) also experience the similar findings. 2.2.2. Stock Market and Banks’ Profitability The outcome of existing literatures focusing on the stock market and bank’s profitability are mixed. The positive linkage is expected between the development of stock market and bank’s performance because a well- developed stock market ensures the easy flow of information to banking institutions enabling them to evaluate the potential risk of investment. Besides, a sound stock market creates confidence among the borrower to obtain credit from the bank which also lead to increase the banks’ profit. On the contrary, the stock market can also affect negatively on the bank’s profitability. This might be the case of a competitive market where stock market appears to substitute to the banking sector. Eichengreen and Gibson (2001) examine the determinants of Greek commercial banks’ profitability and observe that two factors i.e. market share and concentration ratios of Greek banks have positive relation with banks’ profitability. Kaya (2002) find a positive link of market share with banks’ profitability in term of ROE. Hassan and Bashir (2003) identify a positive and significant role stock market development and banks’ profitability. In contrary to these findings, Rossi, Borroni, Lippi, and Piva (2018) observe that stock market has negative relation with banks’ ROA and ROE. Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 36 2.2.3. Real GDP Growth and Banks’ Profitability Many studies focus on macroeconomic and financial determinants of bank’s profitability. These are GDP growth, interest rates, inflation, stock market instability (U. Albertazzi & L. Gambacorta, 2009; Athanasoglou, Brissimis, & Delis, 2008; Beckmann, 2007; Lee & Hsieh, 2013). The existing literatures also reveal the mixed results. Some studies find no influence (P. Sharma, Gounder, & Xiang, 2013) while others explore negative impact on banks’ profitability (Ben Ameur & Mhiri, 2013; Ben Naceur & Omran, 2011; Sufian, 2009; Tan & Floros, 2012; Yanikkaya, Gumus, & Pabuccu, 2018). On the contrary, some other studies observe that economic growth enhance bank’s profitability. This is due to the economic boom and increasing demand for banking products and services (U. Albertazzi & L. Gambacorta, 2009; Athanasoglou et al., 2008; Davydenko, 2011; Dietrich & Wanzenried, 2014a; Flamini, McDonald, & Schumacher, 2009; Zeitun, 2012). With this mixed result, this study is an attempt to examine the positive relation of bank’s profit and GDP growth in Saudi Arabia. III. Methodology 3.1. Data The study focuses on annual data (2000-2020) of two largest full-fledged Islamic banks in Saudi Arabia i.e., Bank Alrajhi and Bank Aljazira. Only two Islamic banks are selected due to their early Islamic banking operation in Saudi Arabia whereas other two banks such as, Bank Albilad and Bank Alinma started their Islamic banking in 2004 and 2006, respectively. It considers two profitability indicators of banks i.e., Return of Equity (ROE) and Return of Assets (ROA) as the endogenous variables whereas stock prices of Alrajhi and Aljazira, oil prices and real Gross Domestic Product are considered as the exogenous variables. All the data are extracted from Bloomberg database. 3.2. Model Specification Table 1 shows the description of the variables with the measurement unit and formula of extracting the dependent variables. Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 37 Table 1. Definition of variables Levelling Description Measurement ROE Return of Equity is the ratio of net profit to shareholders’ equity ROE= Net Profit Total Equity ROA Return of Asset is the ratio of bank net profit to total assets ROA= Net Profit Total Assets STOCKP Stock prices (USD) Converted to natural logarithm. OILP Brent Oil price (USD) Converted to natural logarithm. RGDP Real Gross Domestic Product (USD) Real GDP is calculated from GDP Deflator and converted to natural logarithm. Source: Bloomberg (2021) This study adopts a nonlinear autoregressive distribution lag (NARDL) approach to show the long-run and short-run asymmetric relations of ROE and ROA with STOCKP, OILP and RGDP are constructed based on the approaches of by Shin, Yu, & Greenwood-Nimmo (2014) and Shin and Smith (2001): ROE𝑡 = θ0 + θ1STOCKP𝑡 + + θ2STOCKP𝑡 − + θ3OILP𝑡 + + θ4OILP𝑡 − + θ5RGDP𝑡 + 𝑒𝑡 (1) ROA𝑡 = θ0 + θ1STOCKP𝑡 + + θ2STOCKP𝑡 − + θ3OILP𝑡 + + θ4OILP𝑡 − + θ5RGDP𝑡 + 𝑒𝑡 (2) where θ=(θ0, θ1, θ2, θ3, θ4, θ5, and θ6) indicates the cointegrating vector of long-run coefficients; and et is the error term which is assumed to follow normal and independent distribution with zero mean value and constant variance. STOCKP𝑡 + = ∑ ∆STOCKP𝑖 + = ∑ max(∆ STOCKPi, 0) 𝑡 𝑖=1 𝑡 𝑖=1 (3) STOCKP𝑡 − = ∑ ∆STOCKP𝑖 − = ∑ max(∆ STOCKPi, 0) 𝑡 𝑖=1 𝑡 𝑖=1 (4) OILP𝑡 + = ∑ ∆OILP𝑖 + = ∑ max(∆ OILPi, 0) 𝑡 𝑖=1 𝑡 𝑖=1 (5) OILP𝑡 − = ∑ ∆OILP𝑖 − = ∑ max(∆ OILPi, 0) 𝑡 𝑖=1 𝑡 𝑖=1 (6) The long-run relationship of ROE and ROA with STOCKP and OILP increase are θ1 and θ3, and decrease are θ2, and θ4 which are assumed to be positive and negative, respectively. Since θ2 and θ4 are predicted to be positive which are supposed to change in identical direction, for example, any increase in STOCKP impact positive long-run changes in ROE and ROA as related to the effect of STOCKP decrease on ROE and ROA of an identical extent i.e., θ1 > θ2. Hence, the long-run relationship as shown in Eq.1 and Eq.2 indicate the non-linear and long-run STOCKP change pass-through to the ROE and ROA. ΔROEt = θ + ß0ROEt−1 + ß1STOCKPt−1 + + ß2STOCKPt−1 − + ß3OILPt−1 + + ß4OILPt−1 − + ß5RGDPt + ∑ µi∆ROEt−i + ∑ (ωi +∆STOCKPt−i + +ωi −∆STOCKPt−i − ) q i=0 p i=1 + ∑ (ξi +∆OILPt−i + +ξi −∆OILPt−i − ) q i=0 + ∑ τi∆RGDPt−i + si=0 +εt (7) ΔROAt = θ + ß0ROAt−1 + ß1STOCKPt−1 + + ß2STOCKPt−1 − + ß3OILPt−1 + + ß4OILPt−1 − + ß5RGDPt + ∑ µi∆ROAt−i + ∑ (ωi +∆STOCKPt−i + +ωi −∆STOCKPt−i − ) q i=0 p i=1 + ∑ (ξi +∆OILPt−i + +ξi −∆OILPt−i − ) q i=0 + ∑ τi∆RGDPt−i + si=0 +εt (8) Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 38 Two of the exogenous variables are shown in asymmetric form while RGDP is presented as liner form; p, q and s indicate the lag orders. The long-run parameters (θ1= - ß1/ ß0), (θ2= -ß2/ ß0), (θ3= - ß3/ ß0), and (θ4= -ß4/ ß0) show the long-run effect of STOCKP and OILP (exogenous variables) increases and decreases, respectively, on ROE and ROA (endogenous variables). Besides, ∑ ωi + 𝑞 𝑖=0 and ∑ ωi − 𝑞 𝑖=0 and ∑ ξi + 𝑞 𝑖=0 indicate the short-run effect of STOCKP and OILP increases and decreases on ROE and ROA, respectively. Thus, Eq.7 and Eq.8 present the asymmetric long-run and short-run effect of STOCKP and OILP on ROE and ROA. 3.3. Method As mentioned outset that this study applies NARDL approach which is developed by Y. Shin et al. (2014). While many studies use ARDL approach introduced by M Hashem Pesaran, Shin, and Smith (1999) and later on developed by M.H. Pesaran, Shin, and Smith (2001), this study focuses on NARDL approach to examine the non-linear relationships among the variables. Following stages are maintained in estimating NARDL model: i) unit root test for detecting the absence of I (2) and presence of I (0) or I (1) or both; ZA tests to detect the structural breaks, ii) general-to-specific principles similar to OLS technique on Eq.7 and Eq. 8 to construct final NARDL model via trimming the insignificant lags, iii) NARDL bound test method to identify the cointegrating variables (Y Shin, Yu, & Greenwood-Nimmo, 2011) with the null hypothesis (no cointegration) (Hn: ß0=ß1+= ß2- = ß3+= ß4- = ß5 =0) is inspected against the alternative hypothesis (presence of cointegration) (Ha: ß0 ≠ ß1+≠ ß2- ≠ ß3+≠ ß4-≠ ß5 ≠0), iv) long-run and short-run asymmetric relations between the endogenous and exogenous variables by applying Wald test, v) non-linear cumulative dynamic multiplier (CDM) effects by the following equations: 𝑚𝑘 += ∑ 𝛿𝑅𝑂𝐸𝑡+𝑖 𝛿𝑆𝑇𝑂𝐶𝐾𝑃𝑡−1 + 𝑘 𝑗=0 , 𝑚𝑘 −= ∑ 𝛿𝑅𝑂𝐸𝑡+𝑖 𝛿𝑆𝑇𝑂𝐶𝐾𝑃𝑡−1 − 𝑘 𝑗=0 , k=0,1,2,3,… (9) 𝑚𝑘 += ∑ 𝛿𝑅𝑂𝐴𝑡+𝑖 𝛿𝑆𝑇𝑂𝐶𝐾𝑃𝑡−1 + 𝑘 𝑗=0 , 𝑚𝑘 −= ∑ 𝛿𝑅𝑂𝐴𝑡+𝑖 𝛿𝑆𝑇𝑂𝐶𝐾𝑃𝑡−1 − 𝑘 𝑗=0 , k=0,1,2,3,… (10) 𝑚𝑘 += ∑ 𝛿𝑅𝑂𝐸𝑡+𝑖 𝛿𝑂𝐼𝐿𝑃𝑡−1 + 𝑘 𝑗=0 , 𝑚𝑘 −= ∑ 𝛿𝑅𝑂𝐸𝑡+𝑖 𝛿𝑂𝐼𝐿𝑃𝑡−1 − 𝑘 𝑗=0 , k=0,1,2,3,… (11) 𝑚𝑘 += ∑ 𝛿𝑅𝑂𝐴𝑡+𝑖 𝛿𝑂𝐼𝐿𝑃𝑡−1 + 𝑘 𝑗=0 , 𝑚𝑘 −= ∑ 𝛿𝑅𝑂𝐴𝑡+𝑖 𝛿𝑂𝐼𝐿𝑃𝑡−1 − 𝑘 𝑗=0 , k=0,1,2,3,… (12) Note that, k→∞, 𝑚𝑘 +→ θ1, and θ3, and 𝑚𝑘 −→ θ2 and θ4. Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 39 IV. Results and Analysis 4.1. Descriptive Statistics Table 2 displays the descriptive statistics of variables under this study where it indicates that ROE is less volatile than ROA while oil price is less volatile than stock prices. The real GDP of Saudi Arabia appears to be fourth least volatile variable where stock prices are highest volatile variable throughout the study period between 2000-2020. Table 2. Descriptive Statistics Variable Mean Median Maximum Minimum Std. Dev. ROAR 3.247640 3.070900 7.292900 0.198114 1.855891 ROER 0.229315 0.220321 0.418235 0.152878 0.075460 ROAJ 0.11995 0.092962 0.470678 0.006136 0.103784 ROEJ 0.022664 0.022448 0.032926 0.015099 0.004794 STOCKP 64.60654 64.15582 150.1119 17.00530 31.23058 OILP 55.65333 48.29000 92.40000 31.23000 20.03583 RGDP 14.51190 14.49900 14.76630 14.12686 0.225578 Source: Bloomberg (2021) 4.2. Stationary and Structural Break Test Table 3 displays the result of both Dickey-Fuller (ADF) and Phillips-Perron (PP) test (Dickey & Fuller, 1979; Perron, 1989) with the integration order of variables under study which indicate that all the five variables have mixed order of I (0) and I(1) and most importantly none of the single variable is falling under integration order of I(0). Afterwards, this study conducts the Zivot- Andrews (ZA) test (Zivot & Andrews, 1992) to identify the possible structural break point. In table 3, two structural break points have been identified. Based on these result, two dummy variables (2008 and 2014) are created which are included in the NARDL model as exogenous variables. The justifications of considering two dummies in 2008 and 2014 are for controlling the external shocks of global financial crisis of 2008 and fall of world oil prices on the Islamic banking profitability (ROE and ROA) in Saudi Arabia. Some studies find that the financial crisis of 2008 have negative impact on the banks’ performances (Gulati & Kumar, 2016; Tzeremes, 2015). Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 40 Table 3. Unit Root With Structural Break Test Results ADF PP-Test Zivot Andrews I(0) I(1) I(0) I(1) t-stat Break Point ROAR -1.29[1] -3.04*** [0] -2.50[1] -3.02**[4] -2.12***[1] 2008 ROER -5.02***[1] -2.13***[0] -3.92**[0] -4.15***[2] -4.44*[2] 2008 ROAJ -1.12[1] -2.24*** [0] -1.70[1] -3.08**[4] -3.11***[1] 2008 ROEJ -4.02***[1] -2.16***[0] -2.99***[0] -3.90***[2] -4.14**[2] 2008 STOCKP -3.62[0] -3.71***[0] -1.75[1] -3.55***[1] -3.54***[1] 2014 OILP -2.22[1] -3.98***[1] -2.53[5] -6.86***[16] -3.50** 2014 LRGDP -3.78[1]** -4.74**[0] -4.18[1]** -4.63**[5] -4.21***[1] 2008 Note: An intercept and trend option is used for ADF followed by the null hypothesis: the series contain a unit root. In the case of ADF test, numbers in [ ] indicate optimal lags and for PP test, number in [ ] is Bandwidth: (Newey–West automatic) using Bartlett kernel. SC is applied for selecting the optimal lag order. The subscript R and J stand for Alrajhi and Aljazira, respectively. Superscript *, **, and *** indicate the rejection of the null hypothesis at 10%, 5%, and 1%, respectively. 4.3. Brock, Dechert and Scheinkmakn (BDS) Test Apart from the stationary and the structural break test results, this study also conducts Brock, Dechert and Scheinkmakn (BDS) test for detecting the nonlinear dependencies of both endogenous and exogenous variables as recommended by Broock, Scheinkman, Dechert, and LeBaron (1996). The results in table 4 suggest rejecting the null hypothesis of the error terms which are assumed to follow independent and identical distribution of across various dimensions. The rejection of null hypothesis indicates the existence of nonlinearity among the variables and hence justifies to estimate NARDL model. Table 4. BDS Non-Linearity Test Results Embedding Dimension=m Series m=2 m=3 m=4 m=5 m=6 ROAR 0.080083*** 0.091148*** 0.040173* 0.073897** 0.087153*** ROER 0.11*** 0.15421*** 0.19445*** 0.209906*** 0.232351*** ROAJ 0.072465*** 0.106602*** 0.119336** 0.096786* 0.0606* ROEJ 0.057368*** 0.072339*** 0.083339*** 0.084906*** 0.098842*** STOCKP 0.061468*** 0.124135*** 0.1656*** 0.196757*** 0.218591*** OILP 0.09831*** 0.157826*** 0.190061*** 0.185654*** 0.157062*** RGDP 0.189889*** 0.314598*** 0.404203*** 0.460978*** 0.487706*** Note: The subscript R and J stand for Alrajhi Bank and Aljazira Bank, respectively. Superscript *, **, and *** indicate the rejection of null hypothesis at 10%, 5%, and 1%, respectively. Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 41 4.4. Selection of Optimal Lag Order One of the requirements prior to estimating NARDL model is to select an appropriate lag orders of endogenous and exogenous variables. The Schwarz Criteria (SC) lag length criteria has been chosen for the estimation of final NARDL model. Figures 1-4 depict four set of appropriate NARDL models which are derived from top twenty models from each set. -5.20 -5.15 -5.10 -5.05 -5.00 -4.95 -4.90 A R D L (1 , 2 , 1 , 1 , 0 , 2 ) A R D L (1 , 1 , 2 , 1 , 1 , 2 ) A R D L (1 , 2 , 1 , 1 , 1 , 2 ) A R D L (1 , 2 , 1 , 2 , 0 , 2 ) A R D L (1 , 1 , 1 , 1 , 0 , 2 ) A R D L (1 , 2 , 2 , 1 , 0 , 2 ) A R D L (1 , 2 , 2 , 2 , 0 , 1 ) A R D L (1 , 2 , 1 , 0 , 2 , 0 ) A R D L (1 , 2 , 0 , 1 , 0 , 2 ) A R D L (1 , 2 , 1 , 2 , 0 , 1 ) A R D L (1 , 2 , 1 , 1 , 2 , 0 ) A R D L (1 , 1 , 2 , 1 , 0 , 2 ) A R D L (1 , 1 , 2 , 1 , 2 , 2 ) A R D L (1 , 2 , 0 , 0 , 2 , 0 ) A R D L (1 , 2 , 1 , 1 , 2 , 2 ) A R D L (1 , 2 , 1 , 1 , 2 , 1 ) A R D L (1 , 2 , 2 , 1 , 1 , 2 ) A R D L (1 , 1 , 2 , 2 , 1 , 2 ) A R D L (1 , 2 , 1 , 2 , 1 , 2 ) A R D L (1 , 2 , 2 , 2 , 0 , 2 ) Schwarz Criteria (top 20 models) Figure 1. ROER, RGDP OILP, STOCKP Figure 2. ROAR, RGDP OILP, STOCKP Figure 3. ROEJ, RGDP OILP, STOCKP Figure 4. ROAJ RGDP OILP, STOCKP -0.4 0.0 0.4 0.8 1.2 1.6 2.0 2.4 A R D L (1 , 2 , 2 , 2 , 1 , 2 ) A R D L (1 , 2 , 2 , 2 , 2 , 2 ) A R D L (1 , 1 , 2 , 2 , 1 , 2 ) A R D L (1 , 2 , 1 , 2 , 1 , 2 ) A R D L (1 , 2 , 2 , 2 , 0 , 2 ) A R D L (1 , 1 , 2 , 2 , 2 , 2 ) A R D L (1 , 2 , 1 , 2 , 0 , 2 ) A R D L (1 , 2 , 1 , 2 , 2 , 2 ) A R D L (1 , 2 , 2 , 2 , 1 , 1 ) A R D L (1 , 1 , 2 , 2 , 0 , 2 ) A R D L (1 , 2 , 2 , 2 , 2 , 0 ) A R D L (1 , 2 , 2 , 2 , 2 , 1 ) A R D L (1 , 1 , 1 , 2 , 0 , 2 ) A R D L (1 , 2 , 2 , 2 , 1 , 0 ) A R D L (1 , 1 , 1 , 2 , 1 , 2 ) A R D L (1 , 1 , 1 , 2 , 2 , 2 ) A R D L (1 , 1 , 1 , 2 , 1 , 1 ) A R D L (1 , 1 , 1 , 2 , 2 , 1 ) A R D L (1 , 2 , 1 , 2 , 1 , 1 ) A R D L (1 , 2 , 2 , 1 , 1 , 2 ) Schwarz Criteria (top 20 models) -11.0 -10.9 -10.8 -10.7 -10.6 -10.5 -10.4 -10.3 -10.2 -10.1 A R D L (1 , 2 , 0 , 2 , 2 , 2 ) A R D L (1 , 1 , 0 , 2 , 1 , 2 ) A R D L (1 , 2 , 0 , 2 , 1 , 2 ) A R D L (1 , 2 , 1 , 2 , 2 , 2 ) A R D L (1 , 1 , 0 , 2 , 2 , 2 ) A R D L (1 , 1 , 1 , 2 , 1 , 2 ) A R D L (1 , 2 , 2 , 2 , 2 , 2 ) A R D L (1 , 2 , 1 , 2 , 1 , 2 ) A R D L (1 , 1 , 2 , 2 , 2 , 2 ) A R D L (1 , 1 , 1 , 2 , 2 , 2 ) A R D L (1 , 1 , 2 , 2 , 1 , 2 ) A R D L (1 , 2 , 2 , 2 , 1 , 2 ) A R D L (1 , 1 , 0 , 2 , 2 , 1 ) A R D L (1 , 1 , 2 , 2 , 1 , 1 ) A R D L (1 , 1 , 0 , 2 , 1 , 1 ) A R D L (1 , 2 , 2 , 2 , 1 , 1 ) A R D L (1 , 2 , 0 , 2 , 2 , 1 ) A R D L (1 , 1 , 1 , 2 , 2 , 1 ) A R D L (1 , 2 , 0 , 2 , 1 , 1 ) A R D L (1 , 1 , 1 , 2 , 1 , 1 ) Schwarz Criteria (top 20 models) -7.5 -7.0 -6.5 -6.0 -5.5 -5.0 -4.5 A R D L (1 , 2 , 1 , 2 , 2 , 2 ) A R D L (1 , 2 , 2 , 2 , 2 , 2 ) A R D L (1 , 2 , 1 , 2 , 2 , 1 ) A R D L (1 , 2 , 1 , 2 , 1 , 2 ) A R D L (1 , 2 , 2 , 2 , 2 , 1 ) A R D L (1 , 2 , 2 , 2 , 1 , 2 ) A R D L (1 , 2 , 0 , 2 , 2 , 1 ) A R D L (1 , 2 , 0 , 2 , 2 , 2 ) A R D L (1 , 2 , 2 , 1 , 2 , 1 ) A R D L (1 , 2 , 0 , 1 , 2 , 1 ) A R D L (1 , 2 , 2 , 1 , 2 , 2 ) A R D L (1 , 2 , 0 , 1 , 2 , 2 ) A R D L (1 , 1 , 2 , 2 , 1 , 2 ) A R D L (1 , 2 , 1 , 1 , 2 , 1 ) A R D L (1 , 2 , 1 , 1 , 2 , 2 ) A R D L (1 , 1 , 2 , 2 , 2 , 2 ) A R D L (1 , 0 , 0 , 2 , 2 , 2 ) A R D L (1 , 1 , 1 , 2 , 1 , 2 ) A R D L (1 , 1 , 1 , 2 , 2 , 2 ) A R D L (1 , 0 , 2 , 2 , 2 , 2 ) Schwarz Criteria (top 20 models) Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 42 4.5. Bound Test for Cointegration In this study, a baseline model is constructed for identifying cointegrating relations among endogenous and exogenous variables where two tests are applied i.e., F-test, and T-test. The general principle is that if the estimated value of F–statistics and T-statistics are greater than their respective critical values (upper limit), null hypothesis of no cointegration is rejected. The results of table 5 show the evidence of rejection of null hypothesis at 1% significant level, thus confirming that in the long run variables move together. Table 5. NARDL Bound testing for cointegration Model Long-run relationship Decision tBDM Fpss ROER, RGDP OILP, STOCKP -4.86*** 15.78*** Cointegration ROAR, RGDP OILP, STOCKP -4.10** 47.55**** Cointegration ROEJ, RGDP OILP, STOCKP -3.90** 16.13*** Cointegration ROAJ, RGDP OILP, STOCKP -6.25*** 23.35*** Cointegration Significance level t-statistics F-statistics Lower bound Upper bound Lower bound Upper bound 1% -3.43 -4.79 4.53 6.37 5% -2.86 -4.19 3.13 4.61 10% -2.57 -3.86 2.58 3.86 Note: Number of parameters (K) appear in original model K=5, Critical Value for Finite sample, N=30; The subscript R and J stand for Alrajhi and Aljazira, respectively. Superscript *, **, and *** indicate the rejection of null hypothesis at 10%, 5%, and 1%, respectively. 4.6. Results of NARDL Estimation 4.6.1. Short-run Relation Table 6 displays the NARDL estimated results for two models i.e., ROER and ROEJ. In the short-run for ROER, both positive and negative shocks of OILP have significant impact on ROE R where the positive shocks dominate over the negative one, suggesting that a 1% increase in OILP will increase the ROE R by 0.002% whereas 1% decrease in OILP will increase the ROE R by 0.001%. This finding is similar to Zantioti (2009) that focuses on net oil exporting countries in MENA region. Again, in the short-run for ROEJ, positive shocks of OILP has no impact on ROEJ, but negative shock of OILP has significant impact on ROEJ, implying that 1% decrease in OILP will increase the ROEJ by 0.001%. The reason might be the case of increasing net profit of banks and decreasing the value of total equity. It also indicates that bank’s expansion of oil-based lending and issue of surplus liquidity. Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 43 Table 6. Estimated Results of NARDL Models Based on ROE ROER, LRGDP OILP, STOCKP ROEJ, LRGDP OILP, STOCKP Coeff Std. Error t-stat Prob Coeff Std. Error t-stat Prob Panel A: Long-run Estimation C 3.024 2.041 1.482 0.198 1.932 0.308 6.280 0.008 ROE(-1) -0.877 0.180 -4.865 0.005 -0.382 0.508 0.752 0.506 RGDP(-1) 0.191 0.144 1.333 0.240 0.135 0.022 6.217 0.008 OILP_POS(-1) 0.001 0.001 0.645 0.547 0.001 0.000 4.226 0.024 OILP_NEG(-1) 0.000 0.000 0.200 0.850 0.000 0.000 -6.784 0.007 STOCKP_POS(-1) 0.001 0.000 2.170 0.082 0.000 0.000 -0.800 0.482 STOCKP_NEG(-1) 0.002 0.000 3.930 0.011 0.002 0.000 5.964 0.009 Panel B: Short-run Estimation D(RGDP) 0.557 0.094 5.949 0.002 0.287 0.018 15.922 0.001 D(RGDP(-1)) 0.178 0.058 3.077 0.028 0.013 0.004 3.611 0.037 D(OILP_POS) 0.002 0.000 4.407 0.007 - - - - D(OILP_NEG) -0.001 0.000 -4.359 0.007 -0.001 0.000 -18.791 0.000 D(OILP_NEG) (-1) - - - - -0.000 0.000 -13.600 0.001 D(STOCKP_POS) - - - - 0.000 0.000 6.375 0.008 D(STOCKP_POS(-1)) - - - - 0.001 0.000 21.181 0.000 D(STOCKP_NEG) 0.001 0.000 9.341 0.000 0.001 0.000 21.181 0.000 D(STOCKP_NEG(-1)) 0.001 0.000 5.936 0.002 0.000 0.000 -7.092 0.006 ECT(-1) -0.877 0.064 -13.763 0.000 -0.821 0.024 16.063 0.001 Panel C: Model Diagnostics BG LM 5.42(0.10) 6.77 (0.08) RESET 0.39 (0.56) 0.79(0.51) ARCH 2.34(0.12) 7.19(0.07) Jarque Bera-Normality Test 1.03 (0.59) 0.84(0.65) Note: Two dummy variables are excluded in the final NARDL model because of their insignificant values. The subscript R and J stand for Alrajhi and Aljazira, respectively. Superscript *, **, and *** indicate the rejection of null hypothesis at 10%, 5%, and 1%, respectively Interestingly, positive shocks of STOCKP has no impact on ROER, but negative shock of STOCKP has significant impact on ROE R. It indicates that 1% decrease in STOCKP will decrease the ROER by 0.001%. Both positive and negative shocks in STOCKP have significant impact on ROEJ, indicating that, a 1% increase in STOCKP will increase the ROEJ by 0.001% whereas 1% decrease in STOCKP will decrease the ROEJ by 0.001%. These outcomes are consistent with Eichengreen and Gibson (2001), Hassan and Bashir (2003) and Kaya (2002). The RGDP has significant impact on ROER and ROEJ, where the magnitude of the impact is observed higher (0.55%) in case of ROER than (0.29%) for the ROEJ. These results are in line with the studies Dietrich and Wanzenried (2014b), Zeitun (2012), Muhamad, Amir, and Abdelhakim (2013). The coefficient of the error correction term (ECT) of ROER and ROEJ are is -88% and -82% indicating that market disequilibrium is adjusted at a speed of 88% and 82% per period, respectively. In other words, the mark equilibrium can be achieved with around one year. Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 44 Table 7. Estimated Results of NARDL Models Based on ROA ROAR, LRGDP OILP, STOCKP ROAJ, LRGDP OILP, STOCKP Coeff Std. Error t-stat Prob Coeff Std. Error t-stat Prob Panel A: Long-run Estimation C 3352.483 1433.714 2.338 0.144 26.649 2.617 10.182 0.010 ROE(-1) 0.429 0.695 0.617 0.600 -0.743 0.119 -6.251 0.025 RGDP(-1) 6.249 101.250 -2.333 0.145 1.869 0.182 -10.245 0.009 OILP_POS(-1) 1.546 0.591 2.617 0.020 0.015 0.002 7.152 0.019 OILP_NEG(-1) -0.370 0.181 -2.041 0.051 -0.005 0.000 -14.357 0.005 STOCKP_POS(-1) 0.116 0.039 -2.945 0.099 0.003 0.001 2.711 0.113 STOCKP_NEG(-1) -0.135 0.025 5.489 0.032 -0.025 0.005 5.301 0.034 Panel B: Short-run Estimation D(RGDP) 2.750 4.993 30.596 0.001 1.868 0.045 41.102 0.001 D(RGDP(-1)) 3.846 3.089 33.613 0.001 0.469 0.026 18.318 0.003 D(OILP_POS) 0.925 0.029 31.516 0.001 0.013 0.000 55.855 0.000 D(OILP_POS(-1)) 0.893 0.027 33.352 0.001 - - - - D(OILP_NEG) -0.303 0.009 -32.136 0.001 -0.006 0.000 -62.052 0.000 D(OILP_NEG(-1)) -0.181 0.006 -27.982 0.001 -0.003 0.000 -26.537 0.001 D(STOCKP_POS) 0.164 0.005 34.384 0.001 0.004 0.000 29.633 0.001 D(STOCKP_POS (-1)) - - - 0.003 0.000 19.824 0.003 D(STOCKP_NEG) -0.363 0.011 -31.785 0.001 0.004 0.000 21.029 0.002 D(STOCKP_NEG(-1)) -0.323 0.011 -29.836 0.001 -0.005 0.000 -13.164 0.006 ECT(-1) 0.429 0.014 31.599 0.001 -0.743 0.015 -50.895 0.000 Panel C: Model Diagnostics BG LM 7.05 (0.25) 8.29 (0.21) RESET 5.53(0.09) 0.13 (0.91) ARCH 0.18(0.67) 0.29 (0.54) Jarque Bera-Normality Test 2.15 (0.34) 1.35 (0.51) Note: Two dummy variables are excluded in the final NARDL model because of their insignificant values. The subscript R and J stand for Alrajhi and Aljazira, respectively. Superscript *, **, and *** indicate the rejection of null hypothesis at 10%, 5%, and 1%, respectively Table 7 shows the short-run relations of ROAR and ROAJ with exogenous variables. In the short-run (ROAR), the positive and negative shocks of OILP have significant influence on ROAR, implying that a 1% increase in OILP will increase the ROA R by 0.92% whereas 1% decrease in OILP will increase the ROA R by 0.30% which clearly show the dominance of positive effect is more than the negative one. Again, both the positive and negative shocks of STOCKP have significant effect on ROAR, showing that a 1% increase in STOCKP will increase the ROAR by 0.16% while 1% decrease in STOCKP will increase the ROA R by 0.36% a dominance of negative shocks over the positive one. In the short-run (ROAJ), both positive and negative changes of OILP have significant impact on ROAJ, suggesting that a 1% increase in OILP will increase the ROAJ by 0.01% but 1% decrease in OILP will increase the ROAJ by 0.01%. On the other hand, the positive and negative shocks of STOCKP have significant impact on ROAJ with the dominance of the latter one, implying that 1% increase in STOCKP will increase the ROAJ by 0.003% but 1% decrease in OILP will increase the ROAJ by 0.005%. Besides, RGDP has significant impact Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 45 on both ROAR and ROAJ, and the magnitude of the impact is observed higher (2.75%) in case of ROAR than (1.87%) for the ROAJ. The coefficient of the error correction term (ECT) of ROAR and ROAJ are is - 43% and -74% indicating that market disequilibrium is adjusted at a speed of 43% and 74% per period, respectively. In other words, the mark equilibrium is achieved within one and half year to two years. 4.6.2. Long-run Relation The long-run cointegrating equations are presented in table 8 and table 9 which are based on the estimated NARDL results of tables 6 and 7. Table 8 displays the long-run relations of ROE with the exogenous variables. In the long-run, there is both positive and negative significant impact of OILP on ROEJ with the dominance of latter over the earlier one, in contrast to ROE R where OILP has no significant impact. It indicates that 1% increase in OILP is related to the increase in ROEJ by 0.004%, and 1% decrease in OILP is associated to the increase in ROEJ by 5.78%, assuming the influence of other variables constant. Moreover, there is both positive and negative impact of STOCKP on ROER with the dominance of latter implying that that 1% increase in STOCKP is related to the increase in ROER by 7.433 %, and 1% decrease in STOCKP is associated to the decrease in ROER by 0.002%, assuming the effect of other variables constant. On the other side, there is only negative and significant impact of STOCKP on ROEJ indicating that 1% decrease in STOCKP is linked to the decrease in ROEJ by 0.01%, assuming the outcome of other variables constant. In addition, RGDP has positive and significant impact on the ROEJ showing that 1% increase in RGDP is related to the increase in ROEJ by 0.353%. This result is consistent with Athanasoglou et al. (2008) Dietrich and Wanzenried (2014b). Table 8. Long-run Relation Based on ROE ROER ROEJ Variable Coeff F-stat P-value Decision Coeff F-stat P-value Decision RGDP(-1) 0.218 0.082 0.780 Absence 0.353 1.966 0.011 Presence OILP_POS 6.841 1.527 0.245 Absence 0.004 0.097 0.021 Presence OILP _NEG(-1) 9.168 0.001 0.971 Absence -5.780 12.047 0.006 Presence STOCKP_POS 7.433 25.097 0.002 Presence -7.454 1.676 0.120 Absence STOCKP _NEG(-1) 0.002 18.338 0.002 Presence 0.006 9.085 0.013 Presence Note: The long-run relation is obtained by θ1= - ß1/ ß0, θ2= -ß2/ ß0, θ3= - ß3/ ß0, θ4= -ß4/ ß0, for positive and negative shocks of the OILP and STOCKP, respectively. The subscript R and J stand for Alrajhi Bank and Aljazira Bank, respectively. Superscript *, **, and *** indicate the rejection of the null hypothesis at 10%, 5%, and 1%, respectively. Table 9 shows the long-run relations of ROA with the exogenous variables. In the long-run, positive and negative shocks of OILP have significant impact on both ROAR and ROAJ. It suggests that a 1% increase in OILP will increase the ROAR by 3.604 % and will increase ROAJ by 0.020% whereas 1% decrease in Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 46 OILP will increase the ROA R by 0.862%. and will increase ROAJ by 0.001%, assuming the effect of others constant. Again, the positive and negative shocks of STOCKP have significant impact on ROAR but only negative shock of STOCKP has significant impact on ROAJ. It implies that a 1% increase in STOCKP will increase the ROAR by 0.27 % whereas 1% decrease in STOCKP will increase the ROA R by 0.31% and will increase the ROAJ by 0.03%, with the assumption of ceteris paribus. Moreover, RGDP has significant impact on ROAJ implying that a 1% increase in GDP will increase the ROAJ by 2.51%. . Table 9. Long-run Relation Based on ROA ROAR ROAJ Variable Coeff F-stat P-value Decision Coeff F-stat P-value Decision RGDP(-1) 14.54 0.064 0.813 Absence 2.513 17.696 0.006 Presence OILP_POS(-1) 3.604 36.038 0.004 Presence 0.020 18.544 0.005 Presence OILP _NEG(-1) -0.862 42.077 0.003 Presence -0.001 28.445 0.002 Presence STOCKP_POS 0.270 15.513 0.017 Presence 0.004 0.1070 0.755 Absence STOCKP _NEG(-1) 0.314 102.82 0.000 Presence 0.033 33.855 0.001 Presence Note: The long-run relation is obtained by θ1= - ß1/ ß0, θ2= -ß2/ ß0, θ3= - ß3/ ß0, θ4= -ß4/ ß0, for positive and negative shocks of the OILP and STOCKP, respectively. The subscript R and J stand for Alrajhi Bank and Aljazira Bank, respectively. Superscript *, **, and *** indicate the rejection of the residuals null at 10%, 5%, and 1%, respectively 4.6.3. Asymmetric Relationships The Wald test statistics are applied to detect the long-run and short-run asymmetric relations with the null hypothesis of symmetry. Table 10 shows the absence of both long-run and short-run asymmetric relations of OILP and STOCKP in the case ROEJ, and there exist only long-run asymmetric relations of OILP for ROER. In contrast, long-run asymmetric relations of OILP and STOCKP with ROA exist in the case of both models. Again, this study confirms short-run asymmetric relations of OILP and STOCKP with ROAR. while this relation of STOCKP presence with ROAJ. Table 10. Wald test for Long-run and Short-run Asymmetry ROER ROEJ ROAR ROAJ Long-run asymmetry Long-run asymmetry Variable F-stat P-value F-stat P-value F-stat P-value F-stat P-value OILP 2.999 0.059 1.508 0.248 16.059 0.016 79.029 0.000 STOCKP 0.829 0.3839 0.057 0.815 64.826 0.016 17.800 0.006 Short-run asymmetry Short-run asymmetry OILP 0.199 0.1409 0.508 0.142 79.756 0.001 1.007 0.354 STOCKP 0.249 0.123 0.571 0.752 28.988 0.006 65.402 0.000 Note: The subscript R and J stand for Alrajhi Bank and Aljazira Bank, respectively. Superscript *, **, and *** indicate the rejection of the residuals null at 10%, 5%, and 1%, respectively. Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 47 4.6.4. Asymmetric Adjustment Process Figure 4-8 exhibit the cumulative dynamic asymmetric multiplier outcome for a total of 15 years with the reaction of ROE and ROA on the positive and negative changes in STOCKP and OILP. The solid black line shows the adjustment process of ROE and ROA with the positive and negative changes in STOCKP and OILP while the light dash red line which also known as the asymmetric line is situated between lower and upper band under the area of 95% confidence interval. The general rule of detecting the presence of asymmetric relation between the variables is that the zero straight line must crosses between the two boundary (lower and upper) which also indicate the statistically significant result. Figure 4. ROER -STOCKP Figure 5. ROER –OIL Figure 6. ROAJ - STOCKP Figure 7. ROAJ - OILP Figure 4- figure 7 present the scenario which is in line with the findings reported in table 10. The cumulative dynamic asymmetric multiplier graphs (Figure4 – Figure 7) shows that the effect of positive and negative change of STOCKP and OILP on ROA and ROE takes around 3-4 years to achieve long-run equilibrium. -.0028 -.0024 -.0020 -.0016 -.0012 -.0008 -.0004 .0000 .0004 .0008 1 3 5 7 9 11 13 15 Multiplier for STOCKP(+) Multiplier for STOCKP(-) Asymmetry Plot (with C.I.) -.002 -.001 .000 .001 .002 .003 .004 .005 .006 1 3 5 7 9 11 13 15 Multiplier for OILP(+) Multiplier for OILP(-) Asymmetry Plot (with C.I.) -.04 -.03 -.02 -.01 .00 .01 1 3 5 7 9 11 13 15 Multiplier for STOCKP(+) Multiplier for STOCKP(-) Asymmetry Plot (with C.I.) .000 .005 .010 .015 .020 .025 .030 .035 1 3 5 7 9 11 13 15 Multiplier for OILP(+) Multiplier for OILP(-) Asymmetry Plot (with C.I.) Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 48 4.7. Robustness Check Since NARDL model follow the same assumptions as Ordinary Least Squared (OLS) model, it is important to check the assumptions of residuals. The residuals are to be free from normality issue, serial correlation and heteroscedastic distribution. In addition, all the models are to be rightly specified. As can be observed from tables 4.5 and 4.6, the residuals from all the estimated NARDL models have no serial correlation, with normal and homoscedastic distribution as evident from Breusch-Godfrey LM test, Jarque- Bera test and White test, respectively. Moreover, the Ramsay reset test indicates that all the models have exact functional forms. At the last stage of model diagnostics, the CUSUM and CUSUMSQ tests are conducted as suggested by Pesaran (1997) for checking the NARDL models’ stability which are presented in Figure 8 and Figure 9. Figure 8. ROER, RGDP OILP, STOCKP Figure 9. ROAR, RGDP OILP, STOCKP Figure 10. ROEJ, RGDP OILP, STOCKP -8 -6 -4 -2 0 2 4 6 8 2016 2017 2018 2019 2020 CUSUM 5% Significance -0.4 0.0 0.4 0.8 1.2 1.6 2016 2017 2018 2019 2020 CUSUM of Squares 5% Significance 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 2019 2020 CUSUM of Squares 5% Significance -6 -4 -2 0 2 4 6 2019 2020 CUSUM 5% Significance -0.4 0.0 0.4 0.8 1.2 1.6 2018 2019 2020 CUSUM of Squares 5% Significance -6 -4 -2 0 2 4 6 2018 2019 2020 CUSUM 5% Significance Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 49 Figure 11. ROAJ, RGDP OILP, STOCKP 4.8. Analysis As discussed in previous section, the higher oil prices and stock prices lead to increase the two Islamic banks’ profitability in Saudi Arabia. As an oil- exporting country, Islamic banks enjoys the benefit of higher oil prices. The banks are inclined to make investment decisions on the oil-based projects. Besides, Islamic banks’ stock prices are generally affecting their profitability. This is because of the development of Saudi financial sector ensures a sound and well-functioning stock market with easy and available information helpful for financial institutions like banks to assess potential risk and make right investment decision. Saudi Islamic banks have created higher confidence level among the investors and it also provides them lucrative dividends. As a result, Islamic banks’ stock prices become higher over the years which lead to gain higher profitability in terms of ROA and ROE. Lastly, the positive link between real GDP growth with the profitability of Islamic Banks’ which is confirmed by this study indicate that economic growth flourish Islamic banking sector by creating higher demand for Islamic banking product and services both in the short-run and long-run. In past few years, economic growth along with national development plan enable financial institutions particularly Islamic banks to take part in diversified investment projects to achieve Saudi Vision 2030. V. Conclusion and Policy Recommendations 5.1. Conclusion This study examines the asymmetric impact of oil prices and stock prices on two largest Islamic banks’ profitability for the period 2000-2020. It considers two determinates of banks’ profitability i.e., ROE and ROA which are affected the three external factors such as oil prices, stock prices and real GDP. It applies a nonlinear autoregressive distributed lag nonlinear autoregressive distributed lag (NARDL) model to achieve the objective. -6 -4 -2 0 2 4 6 2019 2020 CUSUM 5% Significance 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 2019 2020 CUSUM of Squares 5% Significance Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 50 In the case of ROE, the short-run negative shocks of OILP have significant impact on both ROER, where positive shocks dominate over the negative one, and ROEJ where positive shocks of OILP has no impact and negative shock of OILP has significant impact on ROEJ. On the other hand, positive shocks of STOCKP has no impact on ROER, but negative shock of STOCKP has significant impact on ROER. Both positive and negative shocks in STOCKP have significant impact on ROEJ. In the case of both models, RGDP has significant impact on ROER and ROEJ. In the case of ROE, the long-run positive and negative shocks of OILP have significant impact of on ROEJ with the dominance of latter one, in contrast to ROE R where OILP has no significant impact. Besides, both positive and negative shocks of STOCKP have significant impact on ROER with the dominance of latter, however, only negative shocks of STOCKP have significant impact on ROEJ. In this study, the long-run and short-run asymmetric relations of OILP and STOCKP are not confirmed in the case ROEJ, whereas the long-run asymmetric relations of only OILP is confirmed for ROER. The study also finds that RGDP has positive and significant impact on the ROEJ in the long-run. In the case of ROA, the short-run positive and negative shocks of OILP have significant influence on both ROAR, where the dominance of positive effect is more than the negative one, and ROAJ, where the changes in both effect are the same. On the other hand, the positive and negative shocks of STOCKP have significant effect on both ROAR and ROAJ showing a dominance of negative shocks over the positive one. Besides, RGDP has significant impact on both ROAR and ROAJ. In the case of ROA, the long-run positive and negative shocks of OILP have significant impact on both ROAR and ROAJ. Besides, positive and negative shocks of STOCKP have significant impact in the case of ROAR while only negative shock of STOCKP has significant impact on ROAJ. In this study, the long-run and short-run asymmetric relations of OILP and STOCKP are not confirmed in the case ROEJ, whereas the long-run asymmetric relations of OILP is only confirmed for ROER. On the other hand, the long-run asymmetric relations of OILP and STOCKP is established for both ROAR ad ROAJ, while the short-run asymmetric relations of OILP and STOCKP is confirmed with ROAR and STOCKP with ROAJ. Based on the above finding, it is clear that OILP and STOCKP have significant role in determining the Islamic banks’ profitability in Saudi Arabia. Although both positive and negative shocks of OILP and STOCKP are significant in most cases, the higher oil prices and higher stock prices are observed to be Amin │ Asymmetric Impact of Oil Prices and Stock Prices on Bank’s Profitability: Evidence from Saudi Islamic Banks International Journal of Islamic Economics and Finance (IJIEF), 5(1), 31-58 │ 51 dominant factors affecting the profitability of two largest Islamic banks in Saudi Arabia. In addition, real GDP growth as an important external factor affect the Islamic banks’ profitability. 5.2. Recommendation A few policy recommendations are provided based on the empirical analysis. The findings of this study recommends that policymakers should pay attention to increase the sound and development and efficiency of Saudi stock market to achieve the higher profitability of Islamic banking sectors which may attract foreign investments and boost the Saudi economy. This finding is also important for the portfolio managers to make decision on acquiring Islamic banks’ stock. It would be profitable to buy Islamic banks’ stock when the trend of oil prices is higher. Besides, macroeconomic policy should focus economic diversification for preparing any external shocks in the global market. Based on the evidence of this study that any negative shock of oil prices will affect Islamic banks’ profitability, the management of Islamic banks need to pay attention of the risk assessment, market monitoring, and particularly tying Islamic banking capitalization to oil price and stock price shocks might help facing the market fluctuation as these two factors affect their profitability. Since oil price is one of the key determinants of Saudi Islamic banks’ profitability, there is an urgent need for strategic policy to absorb any future shocks. In this regard, Islamic banks can play a pivotal role and support the government to effectively implement the Saudi economic diversification plan termed as Saudi Vision 2030. Saudi Islamic banks and other related financial institutions need to diversify their investment portfolios into more productive and export oriented sectors. These institutions can invest more on Small and Medium Enterprises (SMEs) to help achieve higher efficiency, productivity, and competitiveness of the industries. Thus, the diversified investments policy of Islamic financial institutions into private sector can not only facilitate rapid growth of export oriented high-value added industries but also generate huge employment opportunities and attract foreign investment in transport, communication, tourism, IT and other manufacturing industries. 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