Shadow banking, risk-taking and monetary policy in emerging economies: A panel cointegration approach

Zhou, Sheunesu; Tewari, Devi D. Article Shadow banking, risk-taking and monetary policy in emerging economies: A panel cointegration approach Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Zhou, Sheunesu; Tewari, Devi D. (2019) : Shadow banking, risk-taking and monetary policy in emerging economies: A panel cointegration approach, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 7, Iss. 1, pp. 1-17, https://doi.org/10.1080/23322039.2019.1636508 This Version is available at: https://hdl.handle.net/10419/245263 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 12 Jul 2019. Submit your article to this journal Article views: 2067 View related articles View Crossmark data Citing articles: 4 View citing articles FINANCIAL ECONOMICS | RESEARCH ARTICLE Shadow banking, risk-taking and monetary policy in emerging economies: A panel cointegration approach Sheunesu Zhou 1 *and D. D. Tewari 1 Abstract: This study investigates the nexus between shadow banking, bank risk and monetary policy in emerging economies. The importance of this topic arises from its impact on the relationship between price and financial stability objectives of the regulator. In essence, the existence of financial market channels of monetary policy distorts the dichotomy between price and financial stability objectives of central banks. We employ panel cointegration techniques and find a negative association between monetary policy and shadow banking. Specifically, an increase in the central bank policy rate results in a decrease in shadow bank asset growth. In addition, we find a positive association between shadow banking and bank risk. Monetary policy effectiveness increases when bank risk is high. In sum, our results show that shadow banks are an element of the bank risk-taking channel of monetary policy. We suggest policy coordination between monetary and macroprudential policy, and close monitoring of shadow banking activities to reduce risky undertakings in the financial sector. Subjects: Macroeconomics; Monetary Economics; International Economics Sheunesu Zhou ABOUT THE AUTHORS Sheunesu Zhou recently graduated with a Dcom. in Economics from the University of Zululand in South Africa. He is a specialist in Financial Economics and Macroeconomics. Dr. Zhou has wider research interests in financial markets and macroeconomic policy formulation, and the application of econometric methods in economic policy analysis. Currently, he is working a lecturer in Economics and Finance at the university of Zululand and is involved with the Faculty of Commerce, Administration and Law postgraduate research supervision and mentoring. D. D. Tewari isDedicated Economist, having more than 25 years of teaching, research, consulting and managerial experience. Has taught at the Indian Institute of Management, Ahmedabad, India; University of Natal and University of KwaZuluNatal, Durban, University of Zululand, South Africa; HEC Montreal, Canada; and, School of Economics at the University of Shangdong, China. Major areas of research include among others natural resource economics; educational economics; financial economics and monetary economics. PUBLIC INTEREST STATEMENT The conduct of monetary policy cannot be isolated from developments in financial markets. This study attempts to investigate the linkages between monetary policy, bank risktaking and shadow banking. The focus on shadow banking is necessitated by susceptibility of shadow banking activities to higher risk compared to formal banking channels. Furthermore, this study allows us to investigate if indeed there is a policy trade-off between price and financial stability. The findings of this study show that in the context of emerging economies, a hike in the policy rate results in a decrease in shadow banking. The effect of monetary policy on shadow banking is exacerbated by high risk, implying that the pass-through effect of monetary policy to shadow banks is strong when banks are relatively fragile or unstable. Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Received: 04 February 2019 Accepted: 23 June 2019 First Published: 28 June 2019 *Corresponding author: Sheunesu Zhou, Economics, University of Zululand, South Africa E-mail: [email protected] Reviewing editor: Juan Sapena, Faculty of Economics and Business, Catholic University of Valencia, Valencia, Spain Additional information is available at the end of the article Page 1 of 17 Keywords: monetary policy; shadow banking; bank liquidity; panel cointegration; risktaking JEL classifications: C33; E44; G23 1. Introduction The burgeoning of literature focused on the relationship between the financial sector and the monetary sector in the aftermath of the Global Financial Crisis (GFC) suggest the presents of additional channels of monetary policy through the financial sector (GAMBACORTA, 2009; XIAO, 2018). Indeed, the effect of financial dominance cannot be denied with the experience of the GFC. SMETS (2014) for instance argues that the degree of importance attached to financial sector developments is critical to the conduct of monetary policy and can determine monetary policy effectiveness. However, empirical support for these propositions largely derives from advanced economies, with little or no evidence from emerging economies and developing countries. This paper uses cross-country data to investigate the role of shadow bank growth in the monetary policy transmission mechanism. We contribute to the literature on monetary policy transmission by considering the interaction between shadow banking, monetary policy and bank risk. Studies investigating monetary policy transmission demonstrate the existence of several channels of monetary policy. Traditional channels of monetary policy include the interest rate channel, the exchange rate channel and the asset prices channel (BOIVIN, KILEY, & MISHKIN, 2010;CECCHETTI, SCHOENHOLTZ, & FACKLER, 2015). The bank lending and balance sheet channels are classified as credit channels of monetary policy (IRELAND, 2010). Credit channels show the pass-through effect of monetary policy changes on bank credit. Recent studies have suggested the presents of other channels of monetary policy (ANGELONI & FAIA, 2013; CHEN, REN, & ZHA, 2018; DAJCMAN & TICA, 2017). BORIO & ZHU (2012)andVANHOOSE(2008) show that capital regulations levied on formal banking institutions impact the transmission of monetary policy. In fact, monetary policy pass through is high for low-capitalised banks. Furthermore, BORIO & ZHU (2012) argue for the existence of a risk-taking channel of monetary policy in which financial agents, including banks respond to changes in monetary policy rates by adjusting their risk appetite. In addition, several studies establish the presents of a shadow banking channel of monetary policy (FUNKE, MIHAYLOVSKI, & ZHU, 2015; NELSON, PINTER, & THEODORIDIS, 2018; VERONA, MARTINS, & DRUMOND, 2013; XIANG & QIANGLONG, 2014;XIAO,2018). However, these contributions fail to reconcile shadow banking and risk-taking, instead they treat bank risk-taking as a separate channel, without accounting for the role played by shadow banking (ASHRAF, 2017; ASHRAF, ARSHAD, & HU, 2016;DENICOLÒ,DELL’ARICCIA, LAEVEN, & VALENCIA, 2010;GAMBACORTA,2009). We argue that risk-taking by commercial banks is directly linked to shadow banking activities. Our study is also related to studies on the determinants of shadow banking 1 (BARBU, BOITAN, & CIOACA, 2016). ADRIAN & ASHCRAFT (2016) proffer three main theoretical reasons for shadow banking growth. Firstly, shadow banking is a form of regulatory arbitrage. This view contends that shadow banking activities are a response to stringent regulatory measures in the formal banking sector. Regulation can come in the form of micro-prudential requirements, monetary policy or macro-prudential policy. For instance, increased capital requirements of Basel III could have led to the upsurge in shadow bank activity post the GFC. In other studies, tight monetary policy has been found to be a positive driver of shadow banking (CHEN et al., 2018; FUNKE et al., 2015; NELSON et al., 2018). In both SUNDERAM (2014) and ADRIAN & ASHCRAFT (2016), shadow banking also arises due to innovations in the money supply, where the need for money like instruments increases participation of financial agents in the use of new financial products and processes. According to SUNDERAM (2014) shadow banking acts as a substitute for bank deposits, a proposition which we test in this paper. The third reason for the growth of shadow banking is problems relating to incomplete markets in financial markets. Such asymmetric information in financial markets is described in DU, LI, & WANG (2017), who notes that credit market imperfections and financial repression contribute to the growth of shadow banking. Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 Page 2 of 17 This paper is mainly aimed at analysing the effect of monetary policy on shadow banking in emerging market economies within a single equation framework using a panel of 15 emerging economies. The study uses a loan demand and supply framework to develop a theoretical model in which shadow banking is determined by gross domestic product (GDP), inflation and the policy rate. Our analysis is closely related to BARBU et al. (2016)’s study, which focuses on analysing the determinants of shadow banking in the Euro. We depart from their analysis by focusing on the interaction between shadow banking, monetary policy and bank risk. The study contributes to the literature in three ways, firstly, we consider a panel of emerging economies, which have seen a surge in shadow bank growth in the past two decades. Second, we develop a theoretical framework for the determination of shadow banking in emerging economies using a loan demand and loan supply framework. The third contribution comes from analysing the linkages between shadow banking and bank risk-taking, within the monetary policy transmission mechanism. The rest of the study proceeds as follows: Section 2 provides a brief review of the empirical literature on the determination of shadow banking and Section 3 focuses on the theoretical model used in the study. In Section 4 and 5, we provide a description of the methodology used in the study and the results from our analysis, respectively. Section 6 concludes the paper. 2. Empirical literature Empirical literature on shadow banking is still scarce, more so is literature on the determinants of shadow banking. We review in this section literature related to determination of aggregate shadow banking and literature on the determinants of individual shadow banking instruments or processes. BARBU et al. (2016) provide the first study that investigates the macroeconomic determinants of shadow banking. Their study analyses determinants of shadow banking for the Euro area using data on the flow of funds as a proxy for shadow banking in a sample of 15 European countries. Their study establishes a negative relationship between economic growth, short-term interest rates, money supply and shadow banking. As a corollary, the contractionary monetary policy which increases the short-term rates leads to a decrease in shadow banking activity. Stock market developments and long-term interest rates are found to be positively related to shadow banking. SUNDERAM (2014) develops a model of money creation in which both bank deposits, treasury bills and shadow bank assets respond to money demand. An increase in money demand results in a decrease in demand for treasury bills, and hence an increase in treasury bill yields. They argue that shadow bank assets increase as a substitute to treasury bills as they are both money like claims. Furthermore, their study suggests that the need to hold reserves acts as a tax for issuance of deposits leading banks to substitute deposits with shadow bank liabilities in the event of a policy rate hike. The implication of their results is that banks engage in shadow banking activities either to substitute or complement their deposits. The finding is supported by various studies which point to the importance of bank liquidity in driving shadow bank activities (AGOSTINO & MAZZUCA, 2011; NACHANE & GHOSH, 2002). Shadow bank liabilities are therefore important in indirectly driving bank credit and have the potential to stabilise banks’balance sheets in the event of increased bank withdrawals under a tight monetary policy stance. Several studies investigate the determinants of securitisation activity (AGOSTINO & MAZZUCA, 2011; CARDONE-RIPORTELLA, SAMANIEGO-MEDINA, & TRUJILLO-PONCE, 2010;FARRUGGIO&UHDE,2015). CARDONE-RIPORTELLA et al. (2010) use bank-specific characteristics to investigate the drives of shadow banking in Spain. Their study employs both logistic regression and descriptive statistics to analyse the impact of different variables on securitisation. They do not establish the existence of the regulatory arbitrage hypothesis. Instead, they find that securitisation is driven by the search for liquidity and the profit incentive. Their findings are supported by TANG & WANG (2015), who find that shadow banks were more profitable that formal banks in China, concluding that banks engage in shadow banking activities to increase their earnings. FARRUGGIO & UHDE (2015) investigates the determinants of securitisation for the Euro region. They use data from 1997 to 2010 for a sample of 75 Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 Page 3 of 17 banks divided into securitising and non-securitising banks. They find market factors, bank-specific factors and macroeconomic factors to influence securitisation decisions. Specifically, economic growth and high competition among banks are found to drive securitisation. Other factors include bank size, bank capitalisation, regulatory and institutional environment. PANETTA & POZZOLO (2018) use a sample covering 1991 to 2007 for banks from over 100 countries. They employ the method of proportional hazard regression and find that banks securitise as a result of tight regulation, low operating expenditure and as a hedge against both liquidity and credit risks. Their findings validate the mainstream belief that regulatory arbitrage is the main driver of shadow banking activities. In a related study, NACHANE & GHOSH (2002) analyses the determinants of off-balance activities of banks and find that bank size and liquidity are important factors impacting the decision whether to securitise or not in India. Specifically, they argue that well capitalised and highly liquid banks have no incentive to engage in off-balance sheet activities. Bank size negatively influences securitisation decisions. Liquidity and tax incentives both have a negative influence on securitisation, showing that banks could be risk averse as they increase their pool of liquid liabilities. A related study by DUCA (2014) analyses the drivers of shadow banking in both the short run and in the long run. They use credit creation by money market funds as a proxy for shadow banking. Their study uses single equation time series regression and finds that information costs and bank capital regulation have significant effects on the growth of shadow banking in the long run. An interesting finding from this study is that short-run reductions in shadowbanking followed increases inbank liquidity and increased risk in financial markets. DUCA (2014) argues for vulnerability and pro-cyclical behaviour of shadow bank liabilities, which have serious consequences for financial and macroeconomic stability. The study also relates to empirical papers which link shadow banking to monetary policy. Shadow banking is found to reduce the effectiveness of monetary policy (XIANG & QIANGLONG, 2014; XIAO, 2018). XIAO (2018) documents a positive relationship between the Federal reserve (fed) funds rate and growth in shadow bank assets for the US. Their study uses disaggregated data for five shadow bank entities including, broker-dealers, finance companies, funding corporations, ABCP issuers, captive and other financial institutions. They argue that a positive shock on the monetary policy rate induces an increase in shadow bank deposit creation. NELSON et al. (2018) use an autoregressive model with time-varying parameters to show that a contractionary monetary policy increases shadow banking growth but reduces growth of commercial bank assets. XIANG & QIANGLONG (2014), FUNKE et al. (2015), WANG & ZHAO (2016) and VERONA et al. (2013) analyse the impact of shadow banking on monetary policy using Dynamic Stochastic General Equilibrium (DSGE) modelling. XIANG & QIANGLONG (2014) and FUNKE et al. (2015) find that a contractionary monetary policy stance results in a decrease in commercial bank credit but leads to an increase in shadow bank credit. WANG & ZHAO (2016) also find that the net worth of commercial banks decreases due to contractionary monetary policy action. On the contrary, the net worth of shadow banks increases as a result of a hike in the policy rate. 3. Theoretical model The theoretical model developed here follows the work of STEIN (1998), EHRMANN, GAMBACORTA, PAGÉS, SEVESTRE, & WORMS (2001) and ABDUL KARIM, AZMAN-SAINI, & ABDUL KARIM (2011). STEIN (1998) develops a model in which banks pay a premium to access market finance in the event of a monetary policy shock. They provide a foundation for investigation of the bank lending channel of monetary policy by both EHRMANN et al. (2001) and ABDUL KARIM et al. (2011) for the Euro area and Malaysia, respectively. Assume the following identity for a bank balance sheet: At¼L tþKt#(1) Where Atare bank assets, Ltare bank liabilities and Ktrepresents bank capital. In practice, bank assets comprise cash, loan portfolio, short term and long-term securities and also property and Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 Page 4 of 17 equipment. However, the highest proportion of bank assets comprises loans ðLiÞand securities Si ðÞ. We follow ABDUL KARIM et al. (2011) and restate the simplified identity as follows: LiþSi¼DiþKiþSBi#(2) whereDiare deposits and Kiis the bank’s capital. SBicaptures shadow bank liabilities, which include financing from all other non-core bank activities. Unlike in ABDUL KARIM et al. (2011) where other sources of finance refer only to unsecured money market funding, we recognise the importance of the wider wholesale markets, including the repo market, which has been thriving in emerging countries like South Africa. In our model shadow bank liabilities SBican be substituted for bank deposits, Dias in SUNDERAM (2014). In the event of a contractionary monetary policy shock, banks increase their use of market finance, resulting in increased SBi. In addition, shadow banking impacts the left hand side of Equation (2) through securities holdings. Thus, we allow securities holdings by banks to include both safe bonds issued by the government and municipalities and money market instruments and other short-term assets, including assets backed securities. The later represents banks’financing of shadow banks, who are the issuers of such assets. Sican, therefore, be decomposed as follows: Si¼SLþSs where SLrepresents government securities. Ssis the short-term component of banks securities holdings and is linearly related to the bank short-term lending rate, rl. Thus Ss¼φrlwhere φ<0. Thus in the short term, an increase in the bank lending rate encourages bank loan supply, at the same time reducing funds available for short-term shadow bank asset holdings. The equation above can be expressed as follows: Si¼S0φrl#(3) Where S0is a constant term accounting for long term and other securities. The level of bank deposits is also a decreasing function of the policy rate, Di¼αrp#(4) Where αisnegativeforalli:As in ABDUL KARIM et al. (2011), Capital is a function of loans: Ki¼kLi#(5) Bank loan demand is determined by output y, the price level pand the interest on loans rlas in the following equation, Ld i¼βiyþβ2pβ3rl#(6) The supply of bank loans can be derived by combining Equation (2–5) and solving for Li. Simple manipulation will result in the following: Ls i¼DiþKiþSBiSi#(7) Ls i¼DiþkLs iþSBiθ0θ1rl ðÞ Where Si¼θ0θ1rl¼S0φrl, Ls i1kðÞ¼DiþSBiþθ1rlθ0 Ls i¼Di 1kðÞ þSBi 1kðÞ þθ1rl 1kðÞ θ0 1kðÞ #(8) If we let ρi¼1 1kðÞ be the coefficient of Di;γi¼1 1kðÞ be the coefficient of SBi;θ0 1kðÞ be the coefficient of rl; and ϕ0be a constant described by θ0 1kðÞ , we can rewrite Equation (8) as: Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 Page 5 of 17 Ls i¼ρiDiþγiSBiþϕirlϕ0#(9) EHRMANN et al. (2001) show that the parameter of Dican be decomposed into two factors, one that is independent of bank characteristics and another factor that is dependent on bank level characteristics such as capitalisation, liquidity and size. Let xirepresents bank-specific characteristics. In our model, a higher value for ximplies sound financial conditions for bank iand consequently low risk, and xiwill be treated as a risk variable. If ρiis the coefficient of Di, it can be decomposed into two parts, firstly ρowhich describes the influence of deposits on loan supply that is independent of bank characteristics and ρ1which describes the influence of deposits on loan supply that is dependent on individual bank characteristics as follows: ρi¼ρoρ1xi Equation (9) becomes: Ls i¼ρoρ1xi ðÞDiþγiSBiþϕirlϕ0#(10) Equilibrating loan demand (Equation 6) and loan supply (10), and substituting Diwith αrp, shadow banking is determined by output, inflation, the policy rate and bank lending as follows 2 : SBt¼ψ0þψ1ytþψ2ptþψ3rltψ4rptþψ5xirptþωt#(11) Where ψ0is a constant and parameters ψ1ψ5account for the impact of each variable on shadow banking. The error term ωtaccounts for entity-specific reasons for participation in shadow bank activities. Equation (11) shows that shadow banking is determined by output, the price level, the prevailing loan interest rates, bank liquidity and the monetary policy stance. The variable xirptis an interaction term capturing bank risk effect on the influence of monetary policy on shadow banking. Thus the model predicts a decrease in shadow banking with a contractionary monetary policy. However, the less risk, the bank, the lower the impact of monetary policy on shadow banking. 4. Methodology The methodology followed in this paper follows the literature on non-stationary panels (BALTAGI, 2008). Ignoring the non-stationarity of panel data could lead to spurious regression and hence unusable results. The present study employs data from 15 emerging economy countries for the period 2002 to 2017. 4.1. Non-stationarity in panel data Pooled OLS estimates for cointegrated variablesarebiasedduetothepresentsofendogeneity and serial correlation. To mitigate this shortcoming, the study employs non-stationary panel methods for parameter estimation in the name of the panel Fully Modified OLS and panel Dynamic OLS methods. The panel FMOLS of Pedroni (2001) and Philips and Moon (1999) follows from the time series version FMOLS estimator of Philips and Hansen (1990). The estimator corrects for bias and endogeneity in the OLS estimator using non-parametric methods. The panel DOLS method of KAO & CHIANG (2001) follows from the time series DOLS methodology of Saikkonen (1991), which adds lags and leads of differenced independent variables to correct for bias and endogeneity. KAO & CHIANG (2001) show that the limiting distribution of the DOLS estimator isthesameastheFMOLSestimator.However, through Monte Carlo simulation, they find that theDOLSestimateissuperiortoboththeOLS and FMOLS estimates in terms of bias correction. In addition, they also show that bias in both the FMOLS and the DOLS estimators is reduced as the panel time series dimension grows compared to short T panels. Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 Page 6 of 17 4.2. Panel unit root tests and cointegration Panel unit root tests are important in determining the order of integration of the variables in a panel framework. BALTAGI (2008) provides an outline of the first generation and second generation panel unit root tests. This study adopts two main unit roots tests from IM, PESARAN, & SHIN (2003) and Pesaran (2007). IPS 2003 suggests a unit root test that averages individual ADF type test statistics when the error term is serially correlated but with different correlation properties across units. Pesaran (2007) suggests a unit root test that is robust to the presents of crosssectional dependence. They propose a test in which the Dickey Fuller (DF) or Augmented Dickey Fuller (ADF) regressions are augmented using cross-sectional averaged lags of levels and first differenced individual series. Thus the unit root tests are based on cross-sectional augmented ADF statistics (CADF). Panel cointegration tests are applied to ensure that variables are cointegrated before carrying out regression estimations. The most popular cointegration tests are KAO & CHIANG (2001) cointegration test and Pedroni (2004)’s test. Due to the short time series dimension of our data, the study employs KAO & CHIANG (2001)’s cointegration test which is more suitable for shorter macro panels. 4.3. Model The model estimated here derives from the theoretical model in section (3). However, we augment the basic model with other variables from theory and employ bank credit data instead of the lending rate. Reinstated below is the basic model of shadow bank determination: SBSit ¼ψ0þψ1lgdpit þψ2inflit ψ3bcredit ψ4prpit þψ5xitprpit þωit#(12) Where SBit is shadow banking, lgdpit is output,inflit is the price level, bcredit is the bank credit and prpit is the policy rate of the central bank. ωtis an error term assumed to be independently and identically distributed (iid). The ψ1ψ5are parameters to be estimated. For the purpose of this study, we estimate two basic models, firstly we replace bank lending rate with bank credit and analyse the effect of the policy rate when controlling for bank credit. In the second model, we also control for bank liquidity and stock market prices. Furthermore to control for the effect of bank risk, we formulate an interaction term between the policy rate and the bank zscore prrisk ¼xitprpit  . 4.4. Data and variable description Data are obtained from various sources including the Financial Stability Board, the Bank for International Settlement, the World Bank data portal and IMF International financial statistics. Data used is of annual frequency covering the period 2002 to 2017. Our period sample is constrained by availability of shadow banking data from the FSB, which only starts in 2002. Preliminary data transformations in the form of log-linear transformations are done for data that is not in percentages in its original form. We use a sample of 14 emerging economy countries plus Singapore, which the MSCI classifies as an advanced economy. The countries used in the study are shown in Table 1 below. Equation (12) shows that shadow banking determined by output, inflation rate, the policy rate, deposit rate, bank risk and also the level of bank credit. In Table 2, we provide a concise description of all variables used in the model and their economic implications. The study uses data on assets Table 1. Country sample Argentina China Mexico Saudi Arabia Singapore Brazil Indonesia Philippines Turkey Thailand Chile Malaysia South Africa Peru India Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 Page 7 of 17 Table 9. Long-run coefficients Dependent variable: sbs Independent variables Coefficients PFMOLS (1) PFMOLS (2) PFMOLS (3) PFMOLS (4) PDOLS (5) PDOLS (6) PDOLS (7) PDOLS (8) dp 0.85*** (8.21) 0.74*** (6.76) 0.79*** (50.79) 0.71*** (39.10) 0.72*** (12.9) 0.96*** (9.71) 0.73 (0.75) 0.81 (0.70) infl 1.50*** (3.29) 1.75*** (3.83) 1.63*** (24.19) 1.79*** (3.73) 1.28*** (4.56) 6.62*** (4.77) 5.48*** (4.24) 4.93*** (3.81) reer −1.22*** (−2.06) −0.87 (−1.48) −1.06*** (−12.19) −0.70*** (−7.24) −1.85*** (−3.44) pr −1.12 (−1.03) −3.00*** (−2.16) −0.37** (–2.41) −2.76*** (−12.04) −2.17*** (−3.53) −3.80** (−2.01) −6.04*** (−3.73) −6.02*** (−3.57) pr risk 0.26 (2.23) 0.26*** (13.81) 0.22* (1.95) 0.49** (2.38) 0.38* (1.72) bcred 0.41*** (3.17) 0.41*** (3.27) 0.001 (1.33) lzscore 0.17*** (2.08) 0.02*** (13.31) ep −1.10*** (−3.77) −1.03*** (−3.39) liquidity 0.002*** (15.24) 0.003*** (15.94) 0.001 (0.68) ***, ** and * represent 1%, 5% and 10% significance level, respectively. The lags and leads (nlag/nleads) for the DOLS method are set at (1/1) for models (5) and (6), and (3/1) for the models (7) and (8) respectively. The PFMOLS uses the pooled estimator for all models. Results are robust to using the weighted estimator, which uses cross section-specific long-run covariances to reweight the data before carrying out the estimations. Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 Page 14 of 17 contraction decreases shadow bank growth. Whilst the result is contrary to previous studies, we argue that it suggests the dominance of commercial banks in shadow banking activities of emerging countries. Furthermore, we find that reduction in bank risk increases shadow banking and reduces that pass-through effect of monetary policy to shadow banks. We interpret this to imply the effect of bank risk-taking on both monetary policy and shadow banking. In addition, the study reveals the short-term behaviour of the trade-off between bank credit and shadow banking. Therefore, monetary policy authorities should factor in financial market developments in conducting monetary policy. Our results have important implications for regulation, pointing firstly to the need to consider risk factors in analysing monetary policy effectiveness. Pass -through strength of monetary policy rates through the non-bank financial sector and the banking sector is affected by the resilience of the financial sector. The impact of monetary changes is most felt in countries with a relatively unstable financial sector. Further studies can explore this channel of monetary policy using disaggregated data. Funding The authors received no direct funding for this research. Author details Sheunesu Zhou 1 E-mail: [email protected] D. D. Tewari 1 E-mail: [email protected] 1 Department of Economics, University of Zululand, Empangeni, South Africa. Citation information Cite this article as: Shadow banking, risk-taking and monetary policy in emerging economies: A panel cointegration approach, Sheunesu Zhou & D. D. Tewari, Cogent Economics & Finance (2019), 7: 1636508. Notes 1. The Financial Stability Board (FSB) defines shadow banking as, “credit intermediation involving entities and activities (fully or partly) outside the regular banking system”. The study adopts this definition. 2. Complete Mathematical derivation is available on request. The model is however simple to follow. References Abdul Karim, Z., Azman-Saini, W., & Abdul Karim, B. (2011). Bank lending channel of monetary policy: Dynamic panel data study of Malaysia. Journal of Asia-Pacific Business,12, 225–243. doi:10.1080/ 10599231.2011.570618 Adrian, T., & Ashcraft, A. B. (2016). Shadow banking: A review of the literature. In G. Jones (Ed.), Banking crises (pp. 282–315). London: Pulgrave Macmillan. Agostino, M., & Mazzuca, M. (2011). Empirical investigation of securitisation drivers: The case of Italian banks. The European Journal of Finance,17, 623–648. doi:10.1080/1351847X.2010.505727 Agung, I. G. N. (2011). Time series data analysis using eViews. Clementi Loop: John Wiley & Sons (Asia). Angeloni, I., & Faia, E. (2013). Capital regulation and monetary policy with fragile banks. Journal of Monetary Economics,60, 311–324. doi:10.1016/j. jmoneco.2013.01.003 Ashraf, B. N. (2017). Political institutions and bank risk-taking behavior. Journal of Financial Stability,29, 13–35. doi:10.1016/j.jfs.2017.01.004 Ashraf, B. N., Arshad, S., & Hu, Y. (2016). Capital regulation and bank risk-taking behavior: Evidence from Pakistan. International Journal of Financial Studies,4, 16. doi:10.3390/ijfs4030016 Baltagi, B. (2008). Econometric analysis of panel data. Chichester: John Wiley & Sons. Barbu, T. C., Boitan, I. A., & Cioaca, S. I. (2016). Macroeconomic determinants of shadow banking– Evidence from EU countries. Review of Economic and Business Studies,9, 111–129. doi:10.1515/rebs-20160037 Bispham, F. D. (2005). Estimation and inference with nonstationary panel data. Hull: University of Hull. Boivin, J., Kiley, M. T., & Mishkin, F. S. (2010). How has the monetary transmission mechanism evolved over time?In Handbook of monetary economics (Vol. 3, pp. 369–422). Elsevier. Borio, C., & Zhu, H. (2012). Capital regulation, risk-taking and monetary policy: A missing link in the transmission mechanism? Journal of Financial Stability,8, 236–251. doi:10.1016/j.jfs.2011.12.003 Cardone-Riportella, C., Samaniego-Medina, R., & TrujilloPonce, A. (2010). What drives bank securitisation? The Spanish experience. Journal of Banking & Finance,34, 2639–2651. doi:10.1016/j. jbankfin.2010.05.003 Cecchetti, S. G., Schoenholtz, K. L., & Fackler, J. (2015). Money, banking, and financial markets. New York, NY: McGraw-Hill/Irwin. Chen, K., Ren, J., & Zha, T. (2018). The nexus of monetary policy and shadow banking in China. American Economic Review, 108(12), 3891–3936. Dajcman, S., & Tica, J. (2017). The broad credit and bank capital channels of monetary policy transmission in the core and peripheral Euro Area. Zbornik Radova Ekonomskog Fakulteta U Rijeci: Časopis Za Ekonomsku Teoriju I Praksu,35, 249–275. De Nicolò, G., Dell’ariccia, G., Laeven, L., & Valencia, F. (2010). Monetary policy and bank risk taking. Washington DC: International Monetary Fund. Du, J., Li, C., & Wang, Y. (2017). A comparative study of shadow banking activities of non-financial firms in transition economies. China Economic Review, 46, S35-S49. Duca, J. V. (2014). What drives the shadow banking system in the short and long run. Federal Reserve Bank of Dallas Research Department working paper, 1401. Ehrmann, M., Gambacorta, L., Pagés, M. A. R. T. I. N. É. Z., Sevestre, J., & Worms, A. (2001). Financial systems and the role of banks in monetary policy transmission in the euro area. ECB Working Paper. Farruggio, C., & Uhde, A. (2015). Determinants of loan securitization in European banking. Journal of Banking & Finance,56,12–27. doi:10.1016/j. jbankfin.2015.01.015 Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 Page 15 of 17 Funke, M., Mihaylovski, P., & Zhu, H. (2015). Monetary policy transmission in China: A DSGE model with parallel shadow banking and interest rate control. BOFIT Discussion Papers,2015,1. Gambacorta, L. (2009). Monetary policy and the risk-taking channel. International Banking and Financial Market Developments,3, 43. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics,115(1), 53–74. doi:10.1016/S03044076(03)00092-7 Ireland, P. N. (2010). Monetary transmission mechanism. In Monetary economics (pp. 216–223). London: Palgrave Macmillan. Kao, C., & Chiang, M. H. (2001). On the estimation and inference of a cointegrated regression in panel data. InNonstationary panels, panel cointegration, and dynamic panels (pp. 179–222). Emerald Group Publishing Limited. Maddala, G. S., & Lahiri, K. (2009). Introduction to econometrics. New York, NY: John Wiley & Sons. Mazelis, F. (2014). Monetary policy effects on financial intermediation via the regulated and the shadow banking systems (pp. 649). Berlin: Humboldt University, Collaborative Research Center. Nachane, D., & Ghosh, S. (2002). Determinants of offbalance sheet activities: An empirical analysis of public sector banks. Economic and political weekly (pp. 421–427). Nelson, B., Pinter, G., & Theodoridis, K. (2018). Do contractionary monetary policy shocks expand shadow banking? Journal of Applied Econometrics,33, 198–211. doi:10.1002/jae.2594 Panetta, F., & Pozzolo, A. F. (2018). Why do banks securitise their assets? Bank-level evidence from over one hundred countries in the pre-crisis period. Rome: Bank of Italy, Economic Research and International Relations Area. Pedroni, P. (2001). Fully modified OLS for heterogeneous cointegrated panels. In Nonstationary panels, panel cointegration, and dynamic panels (pp. 93–130). Emerald Group Publishing Limited. Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory,20(3), 597–625. Pesaran, M. (2004). General diagnostic tests for cross section dependence in panels, CESifo Working Paper, No. 1229, Center for Economic Studies and Ifo Institute (CESifo), Munich. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics,22(2), 265–312. Phillips, P. C., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I (1) processes. The Review of Economic Studies,57(1), 99– 125. Phillips, P. C., & Moon, H. R. (1999). Linear regression limit theory for nonstationary panel data. Econometrica, 67(5), 1057–1111. Saikkonen, P. (1991). Asymptotically efficient estimation of cointegration regressions. Econometric Theory,7 (1), 1–21. Smets, F. (2014). Financial stability and monetary policy: How closely interlinked? International Journal of Central Banking,10, 263–300. Stein, J. C. (1998). An adverse-selection model of bank asset and liability management with implications for the transmission of monetary policy. RAND Journal of Economics,29, 466–486. doi:10.2307/2556100 Sunderam, A. (2014). Money creation and the shadow banking system. The Review of Financial Studies,28, 939–977. doi:10.1093/rfs/hhu083 Tang, J., & Wang, Y. (2015). Effects of shadow banking on return—empirical study based on Chinese commercial banks. International Journal of Financial Research,7,207. doi:10.5430/ijfr.v7n1p207 Vanhoose,D.D.(2008). Bank capital regulation, economic stability, and monetary policy: What does theacademicliteraturetellus?Atlantic Economic Journal,36,1–14. doi:10.1007/s11293-007-9100-z Verona, F., Martins, M., & Drumond, I. (2013). (Un) anticipated monetary policy in a DSGE model with a shadow banking system. International Journal of Central Banking,9,78–124. Wang, -S.-S., & Zhao, F. (2016). Monetary supply transmission in a DSGE model with a shadow banking system in China. International Journal of Applied Mathematics, 46(3). Xiang, Q., & Qianglong, Z. (2014). Shadow banking and monetary policy transmission. Economic Research Journal,5, 008. Xiao, K. (2018). Monetary transmission in shadow banks. New York: Columbia Business School. Retrieved from http://utahwfc.org/uploads/2018_paper_04d.pdf Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1636508 https://doi.org/10.1080/23322039.2019.1636508 Page 16 of 17 © 2019 The Author(s). 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