Macroeconomics of Systemic Risk: Transmission Channels and Technical Integration

Mohamad Rizan; Muhammad Zulkifli Salim; Saparuddin Mukhtar; Kevin Daly

doi:10.3390/risks10090174

Rizan, Mohamad;Salim, Muhammad Zulkifli;Mukhtar, Saparuddin;Daly, Kevin

2022-09-01 00:00:00

risks Article Macroeconomics of Systemic Risk: Transmission Channels and Technical Integration 1 2 , 3 , 1 2 Mohamad Rizan , Muhammad Zulkiﬂi Salim * , Saparuddin Mukhtar and Kevin Daly Faculty of Economcis, State University of Jakarta, East Jakarta 13220, Indonesia School of Business, Western Sydney University, Penrith, NSW 2151, Australia Department of Banking Research and Regulation, Financial Services Authority, Jakarta 10710, Indonesia * Correspondence: z.salim@westernsydney.edu.au Abstract: The avenue to ﬁnd a balanced assessment of systemic ﬁnancial institutions needs the integration of macro and micro granular datasets. This paper investigates how macroeconomic shocks affect systemic risk through several transmission channels. Employing Indonesia datasets over 2008–2019, we regressed three market models: CoVaR, MES, and SRISK using ﬁxed effect, random effect, GARCH(1,1), and ﬁnite mixture models. The ﬁndings show that stock beta, market index, and exchange rate volatility amplify the systemic risk while the liquidity spread outcome varies due to different of model variables and the deepness of the country’s ﬁnancial market. We propose a practical systemic risk assessment framework and samples of technical integration to capture the overall risk endogenously and externally expose the systemically important ﬁnancial institutions. Keywords: systemic risk; interconnectedness; ﬁnancial crises; macroeconomics; market model JEL Classiﬁcation: G21; G210; G28; G280 Citation: Rizan, Mohamad, Muhammad Zulkiﬂi Salim, Saparuddin Mukhtar, and Kevin Daly. 2022. Macroeconomics of 1. Introduction Systemic Risk: Transmission Systemic risk methodologies nowadays mainly focus on individual Systemically Channels and Technical Integration. Important Financial Institutions (SIFIs) and investigate their failure to impact the ﬁnancial Risks 10: 174. https://doi.org/ system using publicly available data. The European Central Bank advised the importance 10.3390/risks10090174 of two side interaction between the bank as the individual ﬁnancial institution to the Academic Editor: Mogens Steffensen economy (ECB 2009). De Bandt and Hartmann (2000), in their paper on systemic risk survey, showed that few researchers considered the analysis of macroeconomic indicators Received: 1 June 2022 that might be behind contagious default. Bisias et al. (2012), under a different survey, Accepted: 19 August 2022 listed that the macroeconomic indicators used in systemic risk analytics are now starting to Published: 1 September 2022 develop after the 2008 ﬁnancial crisis. Sample studies of various blocks of systemic risk Publisher’s Note: MDPI stays neutral variables stem from macroeconomics like exchange rate (Mayordomo et al. 2014; Yesin with regard to jurisdictional claims in 2013) and GDP growth (Festic ´ et al. 2011; Hirtle et al. 2016; Schleer and Semmler 2015). published maps and institutional afﬁl- However, despite rising efforts using macroeconomic indicators to study systemic risk, it iations. is estimated separately using the stress test scenarios. The absence of an integrated study of macro-micro assessment of systemic risk raises questions: (1) how macroeconomics affects systemic risk, (2) what variables could bring externality to the SIFIs, and (3) how to integrate the macro and micro granular data into a uniﬁed framework and technical Copyright: © 2022 by the authors. calculation of systemic risk? A comprehensive understanding of economic impacts to Licensee MDPI, Basel, Switzerland. address the ﬁnancial contagion issue will provide regulators and policymakers with a This article is an open access article holistic approach. distributed under the terms and This study aims empirically to investigate how the macro variables propagate SIFIs conditions of the Creative Commons impacts via speciﬁc transmission channels. In addition, the study also provides some Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ technical calculation samples to integrate the macroeconomic variables into their systemic 4.0/). risk assessment. The paper ﬁlls the gap by combining the Basel indicator-based model and Risks 2022, 10, 174. https://doi.org/10.3390/risks10090174 https://www.mdpi.com/journal/risks Risks 2022, 10, 174 2 of 27 macroeconomic variables to assess the Systemically Important Banks (SIBs) from two-side (micro and macro perspective). The results would be useful for the policy-makers and bank supervisor in their efforts to determine the SIFIs and their impact on the ﬁnancial system-wide. The manuscript employs three robust empiric approaches to systemic risk quantiﬁca- tion: CoVaR (Adrian and Brunnermeier 2016), Marginal Expected Shortfall—MES (Acharya et al. 2012), and SRISK (Brownlees and Engle 2017). The models are regressed using ﬁxed effect, random effect generalized least square (GLS), random effect maximum likelihood estimator (MLE), GARCH(1,1), and ﬁnite mixture models (FMM) utilize Indonesia banking datasets over 2008–2019. Further, this manuscript also proposes a practical update of the assessment framework together with technical integration calculation to better capture the overall risk. Outcomes of research will contribute to the systemic risk study and improve the overall quantiﬁcation risk of the ﬁnancial system. The ﬁndings are as follows: (1) stock beta, market index volatility, and exchange rate volatility does amplify the transmission of systemic risk. In addition, the change in anchor interest rate by policymakers is also signiﬁcantly proven by statistics though the effect varies among the market models. The difference could come from the employed methods variables and the impact is also different for each interest rate time horizon. Furthermore, related to the liquidity spread, the outcome is a mixture of models. (2) proposes some practical improvement steps in the systemic risk assessment framework to better capture the potential macroeconomic shocks. This study also suggests samples of technical integration calculation and ratios reﬂecting on the added steps. The integrated macro and micro granular data could portray the overall risk endogenously and externally expose the systemically important ﬁnancial institutions. As the discussion progresses, the paper is structured as follows: Section 2 discusses the literature review, containing previous papers and highlighting the possible channel of macroeconomic shocks connected to the systemic risk study. Section 3 contains data details and the methodology framework used. Section 4 presents the analytical results and interpretation, and Section 5 accommodates the conclusion and policy recommendations. 2. Literature Review 2.1. Macroeconomics and Financial Crises Studies on systemically important banks and systemic risk often incorporate a mixture of variables both micro-level, or bank balance sheets, and macroeconomic data. The European Central Bank advised the importance of two side interactions with banks as the individual ﬁnancial institutions to the economy (ECB 2009). In their paper, De Bandt and Hartmann (2000) highlighted the discrepancies of using the macroeconomic indicators behind contagious default. However, after their survey in 2000, there was an increasing number of systemic risk academics who used macroeconomic indicators to build the ﬁnancial stress index in some countries and model the systemic risk. Similarly, Bisias et al. (2012), under a different survey, listed the macroeconomic indicators used in systemic risk analytics as asset-price boom, property price, macroprudential regulation, GDP stress test, risk topography, and several others. Some papers investigate the macroeconomics connection to systemic risk such as de Mendonça and da Silva (2018) who used DCoVaR to analyse the Brazilian bank over 2011–2015, highlighting the importance of bank liquidity, proﬁtability, leverage, and interest rate to assess systemic risk. The study noticed that leverage increases the systemic risk because banks become more vulnerable to the shocks. Additionally, higher returns and increase of monetary policy rate also amplify the systemic risk. On the other hand, more proportion in liquidity total assets could lower the systemic risk. Tram et al. (2021) elaborated on the connection of macroeconomics and systemic risk using SES by regressing it is using OLS, REM, FEM, and SGMM models. Using 29 Vietnamese ﬁnancial institutions’ data from 2010–2018, they found that the economic growth and interest rate have a positive correlation and exchange rate has a negative correlation to systemic risk. Ramos-Tallada (2015) elaborated on the characteristics of bank Risks 2022, 10, 174 3 of 27 lending channels to monetary shocks, such external ﬁnance premiums, and money market rates in combination with the micro banks granularity like liquidity ratio, capital ratio, size, and foreign ownership. He concluded that lending supply is signiﬁcantly sensitive to the money market rate and the external ﬁnance premium is more sensitive to monetary shocks after crises. Laséen et al. (2017) assessed the effect of interest rate on the systemic risk and welfare by employing the New-Keynesian model. The ﬁndings show that surprising monetary tightening policies through raising interest rates does not necessarily reduce systemic risk when the ﬁnancial sector is fragile. As we are aware that various blocks of systemic risk variables coming from macroeconomics should be considered, we summarize some others, such as exchange rate (Mayordomo et al. 2014; Yesin 2013) and GDP growth (Festic ´ et al. 2011; Hirtle et al. 2016; Schleer and Semmler 2015). From a different point of view but closely linked to banking crises, Akhter and Daly (2017), in their Australian bank study, used stock market proxies and T-bonds for Australian banking, and Ali and Daly (2010) studied macroeconomic determinants of credit risk in the US and Australia using default rates, GDP, 6-mth T-bill, industrial production, and debt to GDP ratio. In addition, after the turmoil of the 2008 global ﬁnancial crisis regulators and policy- makers in some countries constructed a ﬁnancial stress index (FSI) to capture the condition of the whole economy using selected macroeconomic indicators. Some results of academics working on this area will be useful for our study to select variables that could represent market sentiment for the SIBs assessment model. Illing and Liu (2006) developed a daily ﬁnancial stress index for the Canadian ﬁnancial system. They grouped 11 macroeconomic indicators covering banking, foreign exchange, debt, and equity market. The indicators were selected and analysed using GARCH estimation to extract volatility measures from their normal price. Another study conducted by Hollo et al. (2012) proposed a composite indicator of systemic stress (CISS) to measure ﬁnancial system stress. They used 15 in- dicators and classiﬁed them into ﬁve economy segments: money market, equity market, bond market, and foreign exchange market for the Eurozone area. Oet et al. (2015) built a ﬁnancial stress index for the Cleveland United States system to identify systemic risk conditions. They proposed six market partitions: credit, funding, real estate, securitiza- tion, foreign exchange, and equity markets. MacDonald et al. (2018) applied multivariate GARCH and calculated banking sector variables, money market, equity market, and bond market. In assessing the Eurozone economies, their results were able to capture the market dependencies and volatilities where banking and money market showed important stress transmission. Based on the above facts, although in the beginning, as stated by De Bandt and Hartmann (2000), few academics used macroeconomic variables for systemic risk analysis, recent investigastion has shown us that using those variables is increasingly becoming a trend for predicting ﬁnancial distress. From our proposed study perspective, results from previous ﬁnancial stress studies should give valuable insight into selecting macroeconomic indicators to complement the banks’ data and build an integrated SIBs analysis. Table 1 provides some of the macroeconomic indicators used in the previous research applicable for systemic risk-linked study. Risks 2022, 10, 174 4 of 27 Table 1. Summary of selected study ﬁndings. Geographical Study Speciﬁc Indicators Estimation Method Results Areas Covered The leverage increases the Bank liquidity, systemic risk, higher returns, proﬁtability, leverage, DCoVaR regressed de Mendonça and da Brazilian bank and rise of monetary policy total deposit, ROA, using D-GMM and Silva (2018) over 2011–2015. rate amplify the systemic risk. policy rate, exchange S-GMM The bank liquidity lowers the rate, and GDP systemic risk. Leverage, ROA, Vietnam High exchange rate and low SES regressed using monetary policy rate, ﬁnancial interest rate decrease systemic Tram et al. (2021) OLS, REM, FEM and variation of exchange institutions over risk, while economic growth S-GMM rate, and GDP growth. 2010–2018 increase the systemic risk. 17 countries in Net unhedged foreign Unhedged foreign Foreign currency, CHF Yesin (2013) Eurozone area currency liabilities contribute currency loan mismatch index over 2007–2011. to systemic risk Non-performing loan, Credit growth harm bank credit growth, GDP Panel regression FE 5 EU member performance while Festic ´ et al. (2011) growth, foreign debt, and REM. countries credit/asset ratio increases the inﬂation, FDI. dynamic of NPL. Extreme shocks from foreign The default rates, GDP, banks are contagious to Distance to default 8 Australian 6-mth T-bill, industrial Australian banks. Contingency Akhter and Daly (2017) using logit regression banks from production, debt to funding and extra liquidity model 2006–2014 GDP ratio measures are needed to monitor. Banking sector beta, liquidity spread, corporate bond spread, covered interest rate Accurate characterization of Credit weights, Canada form Illing and Liu (2006) spread, inverted yield stress is the prerequisite to GARCH 1980–2004 curve, weighted dollar forecast ﬁnancial crises crashes, stock market crashes, covered bond T-bill spread DCoVaR, DCoES, Asymmetric DCoVaR, 95 US banks Net Shapley Value outperform Size, Gross Shapley Value, Mayordomo et al. holding other models. Derivatives Interconnectedness, Net Shapley Value. (2014) companies from holding is a leading indicator and substitutability, Analysed using 2002–2011 of systemic risk contribution. Correlation index and Granger causality. Money market rate, GDP growth positively liquidity ratio, capital correlated credit in demand. ratio, long term Brazilian bank Compulsory bank reserve has Ramos-Tallada (2015) borrowing, size, and VAR, OLS, FEM, GMM between a negative impact to reduce ownership, NPL, 1995–2012. excess demand. Money market volatility, GDP, rate affects the lending supply. inﬂation 2.2. Basel Committee on Banking Supervision Guideline The ﬁrst guideline to determine the SIBs was issued by the Basel Committee on Banking Supervision (BCBS) in 2011 (BCBS 2011). The purpose was to provide a baseline method for policy-makerss to assess and to list the global systemically important banks (G-SIBs). The rationale for adopting additional policy measures for G-SIBs is based on the “negative externalities” created by SIBs, which current regulatory policies did adequately Risks 2022, 10, 174 5 of 27 address during the 2008 global ﬁnancial crises (BCBS 2012). In their press release, BCBS admitted that the guideline was unable to measure precisely speciﬁc characteristics of SIBs. However, it is tailored to portray the important aspects of SIBs. Despite of its simplicity of using the ratio Basel claim it more robust than currently available-model-based measurement approaches and methodologies that rely on only a small set of indicators or market variables (BCBS 2018). The BCBS G-SIBs methodology separates 13 indicators into ﬁve categories, including the amendment of the scale of the substitutability, adding an indicator to represent trading volume, and extending the calculation to insurance subsidiaries (BCBS 2018). To make the reports comparable between each BCBS member, the data are converted to euros using the exchange rate published on the BCBS website. Furthermore, to calculate the score for a given indicator, the bank’s reported value for the related indicator is divided by the corresponding total sample. As the purpose of the result is to create the list of G-SIBs, Basel takes the most signiﬁcant 75 banks as determined by the Basel III leverage ratio exposure measure. BIS allows the local policy-makers to adjust the methodology to integrate speciﬁc country characteristics and negative externalities (BCBS 2012) to cascade the global context to the local scope. Our study explores how macroeconomic shocks affect systemic risk through several transmission channels. Employing Indonesia datasets from 2008–2019, we regressed three market models: CoVaR, MES, and SRISK using ﬁxed and random effect, ﬁnite mixture model, and GARCH. The ﬁndings show that stock beta, market index, and exchange rate volatility amplify the systemic risk while liquidity spread outcome varies depending on different model variables and the deepness of the country’s ﬁnancial market. We propose practical systemic risk assessment framework and samples of technical integration to capture the overall risk endogenously and externally expose the systemically important ﬁnancial institutions. The results will be beneﬁcial for policy-makers to monitor and mitigate systemic risk through a more holistic approach. 3. Data and Methodology 3.1. Source of Data The datasets represent all commercial banks listed on the Jakarta Stock Exchange (JSX) in the period 2008–2019. The samples are classiﬁed following OJK (2021) based on its core capital. The excel sheet comprises market data such as share prices, transaction volume, outstanding shares, stock index, and market capitalization on a daily frequency. We also collected the bank granular balance sheet including the total assets and total equity quarterly. Further, in line with research aims, we also gathered representative macroeconomic statistics such as the exchange rate, TED, credit, liquidity, yield spread, T-bill delta, 7D repo rate, JSX sector and LQ45 excess return, and JSX VIX. The market data was sourced from Eikon Thomson Reuters, Bank Indonesia, and author calculations. To run the analysis, Matlab coding provided by Belluzo (2020) on the GitHub website was used. The datasets are 27 actively traded banks listed in the Jakarta Stock Exchange (JSX) during the period of 2008–2019. The sample banks are available in the Table 2. Risks 2022, 10, 174 6 of 27 Table 2. Sample, Tickers, and Groups. No. Ticker Bank KBMI 1 BBCA PT. Bank Central Asia Tbk. 4 2 BBRI PT. Bank Rakyat Indonesia (Persero) Tbk. 4 3 BMRI PT. Bank Mandiri (Persero) Tbk. 4 4 BBNI PT. Bank Negara Indonesia (Persero) Tbk. 4 5 MEGA PT. Bank Mega Tbk. 3 6 MAYA PT. Bank Mayapada Internasional Tbk. 3 7 BNLI PT. Bank Permata Tbk. 3 8 BDMN PT. Bank Danamon Indonesia Tbk. 3 9 PNBN PT. Bank Pan Indonesia Tbk. 3 10 NISP PT. Bank OCBC NISP Tbk. 3 11 BNGA PT. Bank CIMB Niaga Tbk. 3 12 BTPN PT. Bank BTPN Tbk. 3 13 BNII PT. Bank Maybank Indonesia Tbk. 3 14 BJBR PT. Bank Pembangunan Daerah Jawa Barat Tbk. 2 15 BBTN PT. Bank Tabungan Negara (Persero) Tbk. 3 16 BSIM PT. Bank Sinarmas Tbk. 1 17 BJTM PT. Bank Pembangunan Daerah Jawa Timur Tbk. 2 18 SDRA PT. Bank Woori Saudara Indonesia Tbk. 2 19 BACA PT. Bank Capital Indonesia Tbk. 1 20 AGRO PT. BRI Agroniaga Tbk. 1 21 CCBI PT. Bank China Construction Indonesia Tbk. 1 22 BBKP PT. Bank Bukopin Tbk. 2 23 BABP PT. Bank MNC Internasional Tbk. 1 24 BKSW PT. Bank QNB Indonesia Tbk. 1 25 INPC PT. Bank Artha Graha Internasional Tbk. 1 26 BNBA PT. Bank Bumi Arta Tbk. 1 27 BVIC PT. Bank Victoria Internasional Tbk. 1 3.2. Model Estimation 3.2.1. CoVaR CoVaR was proposed by Adrian and Brunnermeier (2016) in an effort to measure the marginal systemic impact of institutions on other institutions within the network conditional on being in distress. The model idea originated based on the value at risk (VaR) introduced by Jorion (2007). However, if VaR quantiﬁes institution risk in isolation, the CoVaR offered to capture the cross-sectional tail dependency of institutions in the ﬁnancial system. VaR, as we know it, is the most bank losses with a conﬁdence level of 1 (this paper use of 5%). As the investor focuses on the negative returns of investment, represented as VaR, i i i then at , its probability is denoted as Pr (X < VaR ) = , where X is the return loss of institution i for the deﬁned VaR . CoVaR corresponds to the VaR of the institution returns conditions to certain events occurring in the ﬁnancial system, such as the ﬁnancial crises of which represented as C X of ﬁrms i. jjC(X ) To follow Adrian and Brunnermeier (2016), CoVaR cascades the impact of insti- tution i to the VaR of institution j (or ﬁnancial system) with conditions of a certain event with C X . jjC(X ) j i i Pr X j C X CoVaR j C X = a The portion of j’s systemic risk caused by i. CoVar is the difference of the VaR condition of ﬁrm i in the ﬁnancial distress and VaR when the ﬁrm i is in its median state or normal situation (when not in crises). Then, the endowment of ﬁrm i to the system wide systemic risk is represented by DCoVaR. i i i i jjX =VaR jjX =VaR jji q median DCoVaR = CoVaR CoVaR q q q Risks 2022, 10, 174 7 of 27 where: jji DCoVaR = the difference CoVaR of institution j attributed to ﬁrm i at time q with its median i i jjX =VaR CoVaR = VaR of institution j attributed to ﬁrm i return losses at time q ﬁnancial distress; i i jjX =VaR median CoVaR = VaR of institution j attributed to ﬁrm i return losses at time q in normal conditions (when not in crises). 3.2.2. Marginal Expected Shortfall The model was proposed by Acharya et al. (2012) and Acharya et al. (2017) as the perspective of systemic risk is straightforward. The intuition is when the individual ﬁrm’s capital is low, then, others should step in to take over the economy intermediation. However, when others also experience difﬁculties, it triggers system-wide failure. To measure the institution contribution, they applied the value at risk (VaR) and expected shortfall (ES). VaR is deﬁned as most of the institution’s losses with a conﬁdence level of 1 , the parameter of is set at 5% so VaR is the most of the institution’s losses with 95% conﬁdence. As they focus on the left tail (negative) payoff of investment, represented asVaR, then the probability of this state is Pr(R < VaR ) = a The ES is the expected loss under the condition that the loss is greater than the VaR or the average of returns on days when the portfolio’s loss exceeds its VaR limit. Acharya et al. (2017) focus on ES rather than VaR for some reasons. VaR does not capture negative payoff below the thresholds 5% and the sum of two portfolio VaR could be higher than the sum of individual VaR. ES = [RjR VaR ] a a where: R = equity returns of institution; VaR = institutions losses at certain conﬁdence level; a = signiﬁcance level. Further, to calculate the contribution of institution losses into groups or system-wide contributions, the next step is decomposing the institutions return R into the sum of each group’s return r . The weight of group i to total system wide is y , that is R = y r . i i i i i Following the deﬁnition of ES then, ES = S y E[r jR VaR ] a i a i i The sensitivity of overall risk to exposure y to each group i dESa = E[r jR VaR ] MES i a dyi where: y = the weight of group i in the total portfolio; MES = ﬁrm i’s marginal expected shortfall when the ﬁrm doing poorly. 3.2.3. SRISK Acharya et al. (2012), and Brownlees and Engle (2017) measured ﬁrm contribution to the systemic failure as a function of size, leverage, and risk. During ﬁnancial crises the bankruptcy of an institution cannot be absorbed by others as they also suffer undercapi- talization. To estimate the ﬁrm systemic risk contribution, they applied the balance sheet and market data over the Long Run Marginal Expected Shortfall (LRMES) period. SRISK Risks 2022, 10, 174 8 of 27 encompasses the equity volatility, return correlation and distribution, leverage, and size of the ﬁrms. The systemic institutions are ranked according to the SRISK score and the total will be the whole ﬁnancial system capital shortage. The capital shortfall of ﬁrm i on day t is deﬁned as CS = kA W i,t i,t i,t CS = k(D + W ) W i,t i,t i,t i,t where: CS = capital shortfall of institution i at time t; i,t W = market value of equity; i,t D = book value of debt; i,t A = book value of assets; i,t k = prudential capital fraction which is set to 8%. Based on the formula, when the capital shortfall is negative, the ﬁrms have positive or surplus working capital and can operate normally, but the opposite is true when capital shortfall is positive, and the ﬁrms are under distress. Brownlees and Engle (2017) deﬁne systemic risk as a decline in the ﬁnancial market below a threshold C, over a timeframe h. The estimation of capital shortfall uses the bivariate daily equity returns of ﬁrms and market index where volatilities follow asymmetric GARCH and DCC correlation processes. To simulate the crisis, the market index is assumed to fall by 40 percent over six months projection, volatilities, and correlation to change over time to calculate the tail dependence. The ﬁrm capital shortfall causes negative externalities only if it occurs when the whole system is already under distress, the multiperiod market return of period t + 1 and t + h as R . m,t+1:t+h SRISK = E (CS |R < C) i,t t i,t+h m,t+1:t+h = k E (D |R < C) (1 k)E (W |R < C) t t i,t+h m,t+1:t+h i,t+h m,t+1:t+h where: R = the multiperiod market return of period t + 1 and t + h; m,t+1:t+h C = the market decline threshold. A further assumption made by Brownlees and Engle (2017) was that debtors are unable to renegotiate their debts during the crises, as all ﬁrms need liquidity to ﬁnance their operation, SRISK = kD (1 k) W (1 LRMES ) i,t i,t i,t i,t = W [kLVG + (1 k) LRMES 1] i,t i,t i,t where: LVG = leverage ratio (D + W )/W i,t i,t i,t. LRMES = expectation of ﬁrm equity multi period returns conditional on the systemic event. Acharya et al. (2012) approximated it as 1 exp(18 MES). The contribution or systemic share of ﬁrm i SRISK is calculated SRISKi, t SRISK = i,t Sj 2 J SRISKj, t where J = ﬁrms with positive SRISK. Following the steps of Adrian and Brunnermeier (2016), Acharya et al. (2012), and i i Brownlees and Engle (2017), each model provides us with daily DCoVaR . MES , and q,t SRISK data. In order to obtain monthly and quarterly results, the daily outcomes are i,t. averaged for each month and quarter. Risks 2022, 10, 174 9 of 27 4. Results 4.1. Statistics Summary Our discussion consists of three major analysis blocks. Firstly, to generate the systemic risk contribution of each of the banks, we used CoVaR, MES, and SRISK over the sample period. Secondly, we regressed the estimation results derived from step 1 to some of the macroeconomic variables such as beta, exchange rate, fed fund rate, t bill delta, JKSE volatility index, liquidity spread, and TED spread. Thirdly, in the latter part of the paper, we will propose possible technical integration to the BCBS (2018) indicator-based approach to capture macroeconomics effects on systemic risk. The preliminary data process involves sorting and adjusting the data composition where the statistics summary of the data is displayed in Table 3. The pairwise correlation between variables was also analysed and exhibited in Table 4. Table 3. Descriptive Statistics. N Mean Min Max SD Variance Kurtosis Beta 50,328 0.631 3.821 6.539 0.572 0.327 6.614 DCOVAR 50,328 0 0.000 0 0 0 5.983 MES 50,328 0.014 0.000 0.117 0.01 0 5.954 5 7 6 12 SRISK 50,328 7.32 10 0.000 3.61 10 2.41 10 5.82 10 47.347 4 3 4 3 6 EXC RATE 50,328 2.796 1.27 10 9.45 10 1.52 10 1.52 10 2.33 10 FFR 50,328 6.078 4.250 7.75 1.159 1.343 1.633 TBILL 50,328 0.004 65.220 67.33 13.029 169.746 6.188 DELTA JKSE VIX 50,328 10.407 0.010 92.02 10.346 107.047 10.39 LIQ SPR 50,328 1.244 0.110 3.13 0.591 0.349 2.91 TED SPR 50,328 4.443 1.900 6.54 1.186 1.406 1.783 The correlation matrix for all models in Figure 1 reveal the highest correlation is for VaR and DCoVaR at 0.96, followed by MES and DCoVaR at 0.93. The number conveys that SIFIs ranking shortlist association between methodology that we can expect to be the same. On the other hand, the lowest one for SRISK and MES at 0.29, while SRISK and DCoVaR correlation is 0.30. Risks 2022, 10, 174 10 of 27 Risks 2022, 10, x FOR PEER REVIEW 10 of 27 Figure 1. Correlation matrix all models. Figure 1. Correlation matrix all models. Table 4. Pairwise Correlation. 4.2. Systemic Risk Based on Market Model 4.2.1. CoVaR Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) CoVaR was used to measure the financial institutions’ systemic risk contribution, as (1) Beta 1.000 introduced by Adrian and Brunnermeier (2016). The model was developed based on VaR 0.045 (2) COVAR 1.000 by Jorion (2007) and measures the most investors can lose over a certain investment hori‐ (0.000) zon. Based on data analysis as shown in Table 5, CoVaR SIBs rankings over the sample (3) DCOVAR 0.678 * 0.030 * 1.000 (0.000) (0.000) window time are dominated by KBMI 4 or big banks where Indonesia regulator classified (4) MES 0.848 * 0.071 * 0.644 * 1.000 them as banks with capital of more than Rp 70 trillion. They represent major players in (0.000) (0.000) (0.000) the Indonesian banking market with more products and services offered to customers. 0.012 (5) SRISK 0.478 * 0.335 * 0.358 * 1.000 (0.000) (0.005) (0.000) (0.000) Table 5. CoVaR. (6) DCOVAR_1 0.040 * 0.007 0.022 * 0.076 * 0.005 1.000 (0.000) (0.108) (0.000) (0.000) (0.292) 2008 2009 2010 2011 2012 2013 0.014 Banks (7) MES_1 0.003 0.003 0.001 0.006 0.181 * 1.000 % to Sys Rank % to Sys Rank % to Sys Rank % to Sys Rank % to Sys Rank % to Sys Rank (0.559) (0.465) (0.001) (0.811) (0.160) (0.000) BCA 30.0% 2 25.4% 1 26.6% 1 21.7% 2 30.9% 1 25.1% 1 (8) SRISK_1 0.005 0.004 0.000 0.004 0.003 0.008 0.002 1.000 BRI (0.295) 15(0.343) .8% (0.967) 3 9. (0.406) 0% (0.537) 4 (0.057) 9.7% (0.696) 4 10.1% 5 6.4% 6 10.7% 3 0.060 (9) EXC_RATE 0.050 * 0.006 0.041 * 0.146 * 0.005 0.004 0.006 1.000 BMRI 30.9% 1 17.0% 2 19.7% 2 22.4% 1 16.9% 2 22.5% 2 BNI (0.000) 6.(0.000) 1% (0.149) 4 9. (0.000) 2% (0.000) 3 (0.262) 8.5% (0.361) 5 (0.181) 10.2% 4 8.1% 4 8.7% 4 0.025 0.015 0.205 (10) FFR 0.113 * 0.017 * 0.082 * 0.004 0.001 0.007 1.000 MEGA 1.1% 8.0% 5 1.8% 2.0% 2.1% 1.7% * * * (0.000) (0.000) (0.000) (0.000) (0.001) (0.375) (0.844) (0.123) (0.000) BDMN 1.5% 1.6% 2.0% 1.7% 1.4% 2.1% 0.015 (11) TBILL_DELTA 0.001 0.001 0.006 0.000 0.009 * 0.002 0.004 0.000 0.002 1.000 PNBN 0.9% 1.2% 1.0% 1.5% 1.1% 1.1% (0.899) (0.001) (0.739) (0.201) (0.943) (0.040) (0.649) (0.328) (0.970) (0.664) BJBR 3.5% 5.7% 6 10.5% 3 10.3% 3 11.4% 3 7.3% 5 0.058 0.021 0.057 0.630 (12) JKSE_VIX 0.162 * 0.039 * 0.100 * 0.007 0.002 0.004 0.119 * 1.000 BTN 0.0% 1.2% 3.0% 2.2% 2.3% 3.1% * * * * (0.000) (0.000) (0.000) (0.000) (0.000) (0.127) (0.632) (0.320) (0.000) (0.000) (0.000) BSIM 0.4% 0.6% 5.0% 6 3.2% 1.2% 0.8% 0.011 (13) LIQ_SPR 0.031 * 0.005 0.009 * 0.060 * 0.030 * 0.002 0.000 0.000 0.249 * 0.340 * 0.014 * 1.000 BJTM 0.1% 0.1% 0.1% 0.2% 7.1% 5 6.2% 6 (0.000) (0.311) (0.042) (0.000) (0.000) (0.600) (0.951) (0.952) (0.000) (0.000) (0.012) (0.002) SDRA 1.4% 2.9% 2.1% 2.9% 2.4% 2.3% 0.013 0.040 0.252 (14) TED_SPR 0.107 * 0.019 * 0.094 * 0.002 0.003 0.008 0.818 * 0.004 0.105 * 0.208 * 1.000 BACA * 2.1% 3.6% * 3.5% 4.1% * 2.6% 2.5% (0.004) (0.000) (0.000) (0.000) (0.000) (0.732) (0.457) (0.089) (0.000) (0.000) (0.388) (0.000) (0.000) * p < 0.1. Risks 2022, 10, 174 11 of 27 4.2. Systemic Risk Based on Market Model 4.2.1. CoVaR CoVaR was used to measure the ﬁnancial institutions’ systemic risk contribution, as introduced by Adrian and Brunnermeier (2016). The model was developed based on VaR by Jorion (2007) and measures the most investors can lose over a certain investment horizon. Based on data analysis as shown in Table 5, CoVaR SIBs rankings over the sample window time are dominated by KBMI 4 or big banks where Indonesia regulator classiﬁed them as banks with capital of more than Rp 70 trillion. They represent major players in the Indonesian banking market with more products and services offered to customers. Table 5. CoVaR. 2008 2009 2010 2011 2012 2013 Banks % to % to % to % to % to % to Rank Rank Rank Rank Rank Rank Sys Sys Sys Sys Sys Sys BCA 30.0% 2 25.4% 1 26.6% 1 21.7% 2 30.9% 1 25.1% 1 BRI 15.8% 3 9.0% 4 9.7% 4 10.1% 5 6.4% 6 10.7% 3 BMRI 30.9% 1 17.0% 2 19.7% 2 22.4% 1 16.9% 2 22.5% 2 BNI 6.1% 4 9.2% 3 8.5% 5 10.2% 4 8.1% 4 8.7% 4 MEGA 1.1% 8.0% 5 1.8% 2.0% 2.1% 1.7% BDMN 1.5% 1.6% 2.0% 1.7% 1.4% 2.1% PNBN 0.9% 1.2% 1.0% 1.5% 1.1% 1.1% BJBR 3.5% 5.7% 6 10.5% 3 10.3% 3 11.4% 3 7.3% 5 BTN 0.0% 1.2% 3.0% 2.2% 2.3% 3.1% BSIM 0.4% 0.6% 5.0% 6 3.2% 1.2% 0.8% BJTM 0.1% 0.1% 0.1% 0.2% 7.1% 5 6.2% 6 SDRA 1.4% 2.9% 2.1% 2.9% 2.4% 2.3% BACA 2.1% 3.6% 3.5% 4.1% 2.6% 2.5% AGRO 0.2% 1.1% 0.5% 0.5% 0.5% 0.6% CCBI 1.5% 4.4% 0.7% 0.4% 0.3% 0.7% BBKP 1.7% 2.2% 2.1% 3.2% 2.0% 2.2% MNC 1.2% 4.3% 1.7% 2.1% 1.8% 1.0% Others—10 banks 1.5% 2.5% 1.3% 1.3% 1.3% 1.5% 2014 2015 2016 2017 2018 2019 Banks % to % to % to % to % to % to Rank Rank Rank Rank Rank Rank Sys Sys Sys Sys Sys Sys BCA 19.0% 2 24.7% 1 14.9% 3 20.1% 1 20.6% 1 19.7% 1 BRI 9.9% 4 11.3% 3 7.0% 5 9.3% 5 7.9% 5 7.5% 5 BMRI 19.4% 1 20.9% 2 14.0% 4 16.9% 2 18.9% 2 15.2% 3 BNI 11.4% 3 8.6% 5 6.7% 6 10.5% 3 10.2% 4 9.6% 4 MEGA 2.2% 1.6% 1.3% 3.2% 2.4% 1.9% BDMN 2.0% 1.9% 1.3% 3.7% 1.8% 1.8% PNBN 1.4% 1.1% 0.9% 1.3% 1.6% 1.4% BJBR 9.7% 5 8.9% 4 23.5% 1 9.4% 4 11.9% 3 16.9% 2 BTN 2.9% 1.4% 2.6% 2.2% 3.2% 2.5% BSIM 1.5% 1.4% 0.8% 5.0% 2.5% 2.6% BJTM 7.9% 6 6.3% 6 17.2% 2 6.5% 6 7.0% 6 6.0% 6 SDRA 2.8% 2.2% 1.7% 2.6% 3.3% 5.7% 7 BACA 3.4% 3.2% 1.9% 2.8% 3.4% 2.5% BBKP 2.3% 1.8% 2.3% 2.6% 2.5% 2.7% MNC 1.3% 1.2% 1.1% 1.0% 0.8% 0.8% Others—12 banks 2.9% 3.5% 2.6% 2.8% 2.2% 3.1% Source: Salim and Daly (2021). 4.2.2. Marginal Expected Shortfall (MES) MES, as proposed by Acharya et al. (2017), ranked banks based on their systemic contribution, which are shown in Table 6. As we can see, it shortlisted more banks than Risks 2022, 10, 174 12 of 27 CoVaR. The ranking movement is also noticeable when compared with the other two theoretical models. The volatility brings difﬁculties to bank supervisors as they need to calculate the systemic capital charge and capital injection takes time to examine before getting approval. Referring to the same table, the KBMI 4 bank still contributes mostly to Indonesia systemic risk exposure. Table 6. Marginal expected shortfall. 2008 2009 2010 2011 2012 2013 Banks % to % to % to % to % to % to Rank Rank Rank Rank Rank Rank Sys Sys Sys Sys Sys Sys BCA 10.77% 3 8.00% 4 7.12% 5 5.29% 8 9.79% 1 6.77% 6 BRI 16.51% 1 6.99% 6 8.00% 2 6.52% 6 5.62% 7 8.45% 2 BMRI 15.55% 2 7.50% 5 8.33% 1 7.06% 5 7.76% 3 7.75% 3 BNI 9.88% 4 13.02% 1 6.89% 6 10.18% 1 1.20% 10.51% 1 BDMN 6.67% 6 6.77% 7 7.75% 3 4.56% 6.50% 5 6.93% 4 PNBN 8.37% 5 6.60% 9 6.74% 7 8.01% 2 9.74% 2 6.91% 5 BTPN 1.20% 5.10% 11 3.31% 5.77% 7 4.05% 4.23% Maybank 1.38% 5.01% 12 3.61% 4.16% 3.34% 2.56% BJBR 0.64% 0.95% 6.42% 8 4.99% 5.80% 6 3.14% BTN 0.09% 2.92% 7.63% 4 4.65% 4.28% 5.77% 7 BSIM 0.19% 0.28% 0.45% 7.63% 3 2.57% 0.71% SDRA 4.41% 5.81% 10 4.56% 5.28% 9 3.80% 3.89% AGRO 2.83% 6.79% 7 3.41% 2.41% 3.92% 2.18% BBKP 5.66% 7 6.77% 8 5.32% 9 7.17% 4 7.00% 4 5.22% 8 MNC 2.44% 9.24% 2 1.19% 0.07% 3.45% 3.78% BAG 0.98% 4.98% 3.59% 5.16% 10 1.78% 2.08% BNBA 3.94% 3.47% 2.11% 2.27% 2.25% 1.65% BVIC 3.47% 8.75% 3 3.38% 3.88% 4.45% 4.00% Others—9 banks 8.92% 5.51% 13.17% 7.22% 13.43% 12.30% 2014 2015 2016 2017 2018 2019 Banks % to % to % to % to % to % to Rank Rank Rank Rank Rank Rank Sys Sys Sys Sys Sys Sys BCA 5.27% 7 7.49% 4 4.07% 6.27% 4 4.45% 5.21% 9 BRI 7.90% 2 9.89% 2 6.01% 6 8.14% 3 6.36% 6 5.77% 7 BMRI 7.02% 3 8.72% 3 5.58% 8 5.83% 6 7.33% 3 5.50% 8 BNI 12.10% 1 10.80% 1 8.33% 1 12.23% 2 11.22% 1 10.36% 2 MEGA 1.93% 1.39% 0.48% 6.16% 5 3.07% 2.04% BDMN 6.75% 4 6.69% 5 6.03% 5 17.08% 1 6.83% 5 6.05% 5 PNBN 6.54% 5 5.14% 8 4.94% 1.81% 5.99% 7 6.88% 4 BTPN 3.84% 2.63% 1.97% 3.96% 3.70% 3.91% BJBR 4.68% 4.75% 5.10% 9 0.61% 2.61% 0.28% BTN 6.43% 6 3.10% 7.27% 3 3.04% 8.08% 2 5.21% 10 BJTM 2.57% 3.19% 7.01% 4 0.73% 1.91% 1.79% SDRA 4.12% 2.65% 2.03% 2.79% 1.97% 3.92% BACA 1.62% 5.18% 7 4.12% 2.38% 2.55% 2.17% BNGA 2.55% 1.67% 3.20% 2.26% 3.69% 3.95% AGRO 3.25% 3.16% 7.94% 2 3.85% 3.46% 8.88% 3 BBKP 4.96% 4.13% 5.78% 7 5.06% 7 5.95% 8 5.82% 6 MNC 3.73% 5.65% 6 4.15% 2.55% 1.31% 1.35% BVIC 2.10% 4.04% 2.92% 3.89% 6.96% 4 12.75% 1 Others—9 banks 12.64% 9.71% 13.06% 12.60% 12.56% 8.70% Source: Salim and Daly (2021). 4.2.3. SRISK Brownlees and Engle (2017) introduced the SRISK by integrating bank size and degree of leverage. The total SRISK shows the total amount of capital shortage shareholders need to provide or inject during the period of crisis. If the banks SRISK score is zero they have Risks 2022, 10, 174 13 of 27 enough capital to escape the crisis, where the market decline is assumed at 40%, and the prudential capital threshold at 8%. Table 7 exhibit SRISK estimation results are as follows: Table 7. SRISK. 2008 2009 2010 2011 2012 2013 Banks % to % to % to % to % to % to Rank Rank Rank Rank Rank Rank Sys Sys Sys Sys Sys Sys BMRI 31.14% 1 0.00% 0.00% 0.00% 0.00% 0.00% BNI 29.17% 2 16.13% 3 0.00% 7.43% 3 0.00% 39.87% 1 BNLI 11.30% 4 24.24% 2 31.85% 2 27.93% 2 0.00% 0.00% PNBN 0.00% 0.00% 0.00% 2.47% 70.17% 1 22.02% 3 BNGA 24.61% 3 44.70% 1 67.64% 1 49.54% 1 0.00% 0.00% BJBR 0.00% 13.67% 4 0.00% 0.00% 3.83% 0.00% BTN 0.00% 0.00% 0.00% 0.00% 0.00% 26.72% 2 BJTM 0.00% 0.00% 0.00% 5.75% 4 0.00% 0.00% BBKP 2.81% 0.00% 0.00% 4.04% 18.48% 2 4.55% BAG 0.88% 1.26% 0.51% 2.45% 1.95% 2.56% BVIC 0.00% 0.00% 0.00% 0.39% 5.57% 3 4.29% OTHERS—16 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% BANKS 2014 2015 2016 2017 2018 2019 Banks % to % to % to % to % to % to Rank Rank Rank Rank Rank Rank Sys Sys Sys Sys Sys Sys BNI 0.00% 23.91% 2 26.65% 2 26.11% 2 40.78% 1 49.14% 1 BNGA 19.62% 2 26.94% 1 12.77% 4 0.00% 11.45% 4 10.52% 4 BTPN 0.00% 0.00% 0.00% 0.00% 0.00% 1.97% Maybank 0.00% 7.52% 5 0.00% 0.00% 0.26% 1.44% BJBR 16.16% 3 10.77% 4 0.00% 0.00% 0.00% 0.00% BTN 43.27% 1 13.48% 3 28.09% 1 0.00% 28.55% 2 20.15% 2 BBKP 1.84% 6.62% 6 13.36% 3 52.75% 1 13.36% 3 10.94% 3 BAG 9.18% 4 5.30% 7 3.81% 14.51% 3 2.38% 1.79% BNBA 0.93% 0.32% 0.46% 0.00% 0.00% 0.00% BVIC 8.19% 5 5.13% 8 4.37% 6.63% 4 3.22% 3.35% BACA 0.81% 0.00% 0.58% 0.00% 0.00% 0.00% AGRO 0.00% 0.00% 0.00% 0.00% 0.00% 0.70% PNBN 0.00% 0.00% 9.90% 0.00% 0.00% 0.00% OTHERS—14 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% BANKS Source: Salim and Daly (2021). To capture the macroeconomics market impacts to individual banks we calculate the stock beta for each entity as exhibited in the Table 8. Stock beta represents the likelihood of stock volatility to the benchmark index. As we can see the KBMI 4 banks group possess higher beta in all sample window. On average the beta was above 1 convey that the group more volatile compared to the JKSE index. On the opposite the other banks groups beta means less than 1 reﬂect the shareholders of these banks will suffer 0.3–0.5 less volatile than the overall market. We noted the volatility decreasing as the banks products and activities varies among the bank groups. OJK (2016) segregate the banking activities in Indonesia based on its capital where the tier from top KBMI 4 goes down up to KBMI 1 as the basic activities. The volatile capital market in Indonesia during the 2014 as the impact of uncertainties on Federal Reserve quantitative easing and tapering off that impact emerging economies. For more details explanation and extensive discussion of the market model results please refer to Salim and Daly (2021) Risks 2022, 10, 174 14 of 27 Table 8. Beta of sample groups (2012–2019). Mean Max Min St.Dev Kurtosis Skewness 2012 KBMI 4 1.175 1.571 0.803 0.177 0.724 0.162 KBMI 3 0.578 0.664 0.484 0.043 0.698 0.161 KBMI 2 0.588 0.880 0.440 0.087 0.728 0.928 KBMI 1 0.325 0.617 0.202 0.063 3.372 0.945 2013 KBMI 4 1.214 1.765 0.718 0.228 0.664 0.347 KBMI 3 0.510 0.841 0.317 0.113 0.231 0.756 KBMI 2 0.507 0.907 0.216 0.149 0.162 0.268 KBMI 1 0.248 0.456 0.145 0.060 0.861 1.101 2014 KBMI 4 1.715 2.356 1.419 0.163 0.723 0.528 KBMI 3 0.560 0.763 0.395 0.080 0.678 0.101 KBMI 2 0.538 0.794 0.410 0.061 0.854 0.493 KBMI 1 0.332 0.584 0.181 0.064 0.477 0.323 2015 KBMI 4 1.463 1.855 1.061 0.160 0.342 0.358 KBMI 3 0.495 0.753 0.277 0.081 0.013 0.204 KBMI 2 0.447 0.761 0.267 0.089 0.328 0.496 KBMI 1 0.343 0.629 0.173 0.086 0.214 0.628 2016 KBMI 4 1.446 1.893 0.987 0.206 0.742 0.059 KBMI 3 0.602 1.137 0.388 0.125 1.399 0.831 KBMI 2 0.569 1.360 0.315 0.156 4.291 1.696 KBMI 1 0.452 1.248 0.200 0.165 5.292 1.917 2017 KBMI 4 1.506 1.845 1.116 0.154 0.377 0.324 KBMI 3 0.658 1.260 0.018 0.165 3.426 0.653 KBMI 2 0.560 1.116 0.022 0.177 2.134 0.533 KBMI 1 0.550 0.994 0.240 0.139 0.604 0.766 2018 KBMI 4 1.390 1.825 0.631 0.240 1.738 1.151 KBMI 3 0.511 0.810 0.117 0.103 3.205 1.402 KBMI 2 0.322 0.604 0.098 0.099 0.230 0.361 KBMI 1 0.363 0.509 0.200 0.065 0.674 0.210 2019 KBMI 4 1.596 1.959 1.170 0.145 0.210 0.542 KBMI 3 0.704 1.048 0.462 0.115 0.009 0.515 KBMI 2 0.609 1.430 0.375 0.126 7.732 1.466 KBMI 1 0.418 0.637 0.300 0.071 0.217 0.651 4.3. Regression Results To test macroeconomics variables to systemic risk we employ the equation used by de Mendonça and da Silva (2018) and adjusted it to reﬂect our speciﬁc variables: DCoVaR = DCoVaR + BETA + EXC_R + FFR + TBILL + JKSEVIX + LIQSPR + TEDSPR + " t1 MES = MES + BETA + EXC_R + FFR + DTBILL + JKSEVIX + LIQSPR + TEDSPR + " t1 SRISK = SRISK + BETA + EXC_R + FFR + DTBILL + JKSEVIX + LIQSPR + TEDSPR + " t1 note that, DCoVaR , MES , SRISK = DCoVaR, MES, and SRISK of bank t at t – 1; t1 t1 t1 BETA = bank stock beta; EXC_R = exchange rate; FFR = central bank funding rate; TBILL = 3-month T bill rate; JKSEVIX = Jakarta Stock Exchange volatility index; LIQSPR = Liquidity spread. The difference of 3-month repo and 3-month T bill rate; TEDSPR = TED spread. The difference of 3-month USD LIBOR and 3-month T bill rate. Based on balanced panel data over daily observations of more than 50,000 variables the analysis executes using the ﬁxed effects, random effects generalized least square (GLS), and Risks 2022, 10, 174 15 of 27 random effects maximum likelihood estimator (MLE) models. The summary of estimation value is presented in Table 9. To check best ﬁt model, we run the Hausman test where the outcome of Ho is statistically signiﬁcant at 0.002 for DCoVaR reﬂects the random effect is consistent. On the other hand, using the same test we fail to reject Ho for MES at 0.993 and SRISK at 1 infer to choose the ﬁxed effects model over the random effects (Table 10). The outcome of SRISK raise question and suspicious because of highly correlated of " with the regressor. However, refer to the Section 4.2.3 remember that the Indonesia banks have enough capital even during crisis reﬂected in SRISK = 0 which read as autocorrelation existence in the calculation. Further, for DCoVaR employs Breusch Pagan Lagrangian test as displayed in Table 11. for random effect reject the Ho with consequences to run the pooled OLS. As proven by the Breusch Pagan Lagrangian test, for we should run the pooled OLS for the DCoVaR. To choose the most robust model, the ﬁrst step is to check that the assump- tions of the Ordinary Least Square (OLS) hold. Stata results detected heteroscedasticity, autocorrelation, non-normality distribution of error terms (see Appendix A). Therefore, to ﬁx the above problems, we ﬁt the ARCH(1) and GARCH(1) models to avoid bias on estimation. The test of OLS assumptions is available in the Appendix A. The estimation results of the ARCH(1,1) and GARCH(1,1) models for DCoVaR are presented in the Table 12. Risks 2022, 10, 174 16 of 27 Table 9. Fixed and Mixed Effect Results Summary. . Panel A. DCoVaR Panel B. MES Panel C. SRISK (1) (2) (3) (4) (5) (6) (7) (8) (9) FE RE GLS RE MLS FE RE GLS RE MLS FE RE GLS RE MLS FE_DCoVaR RE.GLS_DCoVaR RE.MLS_DCoVaR FE_MES RE.GLS_DCoVaR RE.MLS_DCoVaR FE_MES RE.GLS_DCoVaR RE.MLS_DCoVaR DCOVAR_1 0 *** 0 *** 0 (23.99) (23.96) (0.7) MES_1 - - - 0 *** 0 *** 0 *** (5.56) (5.56) (5.56) SRISK_1 - - - - - - 0.01 0.01 0.01 (0.71) (0.71) (0.71) Beta 0 *** 0 *** 0 0.02*** 0.02 *** 0.02 *** 20.50 *** 20.49 *** 20.49 *** (55.49) (55.62) (253.85) (254.34) (254.35) (88.44) (88.52) (88.53) EXC_RATE 0 *** 0 *** 0 *** 0 *** 0 *** 0 *** 0 *** 0 *** 0 *** (5.05) (5.05) (13.46) (16.74) (16.74) (16.74) (38.97) (38.97) (38.98) FFR 0 0 0 *** 0 *** 0 *** 0 *** 1.91 *** 1.91 *** 1.91 *** (0.47) (0.48) (8.86) (9.7) (9.7) (9.7) (16.12) (16.12) (16.12) TBILL_DELTA 0 *** 0 *** 0 *** 0 *** 0 *** 0 *** 0.02 *** 0.02 *** 0.02 *** (30.6) (30.59) (33.44) (63.62) (63.62) (63.63) (3.28) (3.28) (3.28) JKSE_VIX 0 *** 0 *** 0 *** 0 *** 0 *** 0 *** 0.05 *** 0.05 *** 0.05 *** (46.88) (46.87) (52.02) (96.6) (96.6) (96.61) (5.09) (5.09) (5.09) LIQ_SPR 0 *** 0 *** 0 *** 0 0 0 1.41 *** 1.41 *** 1.41 *** (3.05) (3.04) (6.21) (0.01) (0.01) (0.01) (9.82) (9.82) (9.82) TED_SPR 0 *** 0 *** 0 *** 0 *** 0 *** 0 *** 0.14 *** 1.44 *** 1.43 *** (4.28) (4.27) (4.45) (18.03) (18.03) (18.03) (12.94) (12.94) (12.94) 36.28 _cons 0 *** 0 *** 0 0.01 *** 0.01 *** 0.01 *** 36.28 *** 36.28 *** *** (33.58) (5.35) (0.4) (23.75) (10.48) (10.32) (41.87) (13.5) (14.06) Observations 50327 50327 50327 50327 50327 50327 50327 50327 50327 R-squared 0.1 0.1 0.59 0.59 0.17 0.17 t-values are in parentheses. *** p < 0.01. Panel A: DCovaR, Panel B: MES, Panel C: For SRISK coefﬁcient are in exponent xe05. Risks 2022, 10, 174 17 of 27 Table 10. Hausman speciﬁcation test. (DCoVaR) Coef. Chi-square test value 24.406 p-value 0.002 (MES) Coef. Chi-square test value 1.089 p-value 0.993 (SRISK) Coef. Chi-square test value 0.027 p-value 1 Table 11. Breusch Pagan test. Breusch and Pagan Lagrangian multiplier test for random effects. DCOVAR[ID,t] = Xb + u[ID] + e[ID,t] Estimated Results: Var SD = sqrt(Var) 13 7 DCOVAR 6.32 10 7.95 10 14 7 4.83 10 2.20 10 13 7 u 1.34 10 3.66 10 Test: Var(u) = 0 chibar2(01) =2.1 10 Prob > chibar2 =0.0000 Table 12. CoVaR ARCH(1,1) GARCH(1,1). Delta_CoVaR Coef. Std. Err. t-Value p-Value [95% Conf Interval] Delta_CoVaR DCovar_1 15,697.014 2877.322 5.46 0 21,336.462 10,057.566 Beta 88.335 12.162 7.26 0 64.498 112.171 Exch_Rate 0.069 0.002 33.07 0 0.065 0.073 FFR 59.711 4.458 13.39 0 68.448 50.974 TBILL_DELTA 0.648 0.156 4.14 0 0.341 0.954 JKSE_VIX 1.421 0.21 6.75 0 1.009 1.834 LIQUIDITY_SPREAD 19.918 6.29 3.17 0.002 7.589 32.247 TED_SPREAD 7.812 2.941 2.66 0.008 2.048 13.576 Constant 237.717 30.951 7.68 0 177.054 298.381 ARCH arch L1 0.047 0.012 3.96 0 0.023 0.07 garch L1 0.955 0.011 85.70 0 0.933 0.976 Constant 8.457 9.509 0.89 0.374 10.18 27.095 GARCH results for DCoVaR exhibit that all variables have a positive relationship and were statistically signiﬁcant to the systemic risk escalation except for the central bank funding rate. The connection hints to how the monetary policy, through increasing or lowering interest rates, could enable systemic crises to occur. Referring to the same table, the bank stock beta and liquidity spread are the two biggest contributors precipitating the contagion effect. The bank beta indicates how the institutions’ equity behave across time Risks 2022, 10, 174 18 of 27 with regards to the market volatility, whereas liquidity spread is an indicator of a market under distress when the spread widens during the ﬁnancial crises. To consider the effects of unobserved variables on the independent variables in DCoVaR, MES, and SRISK, we estimated the extension of analysis and incorporated the Finite Mixture Model (FFM). The summary of FMM class 1 and class 2 and the difference analysis for DCoVaR and MES is exhibited in Table 13. For SRISK, the FMM fails to achieve the convergence, and it was concluded that it is best to stick to the ﬁxed model. Table 13. Latent class marginal means. Coefﬁcient Std. Err. t-Value p-Value [95% Conf Interval] 7 9 7 7 DCOVAR 2.82 10 1.83 10 153.92 0 2.78 10 2.86 10 6 8 6 6 DCOVAR 1.08 10 1.18 10 91.64 0 1.05 10 1.10 10 MES 0.0129582 0.0000196 662.66 0 0.0129199 0.0129966 MES 0.0173357 0.0000992 174.72 0 0.0171413 0.0175302 Based on the regression, overall output and the outcome can be summarized as follow: a. Beta and market index volatility: Stock beta have a positive correlation and are statistically signiﬁcant to the systemic risk in all market model estimations. In this case, the bank systemic risk swings downward or upward in the same direction as the overall market. The results also conﬁrmed that we applied market index volatility. The investigation of using simple beta to sort the systemic bank was also suggested by Benoit et al. (2013). b. Exchange rate: The ﬂuctuation of exchange rate could trigger and amplify the sys- temic risk in the economy. Its effects statistically signiﬁcantly validated both the linear and ARCH models. Our ﬁndings were similar to Yesin (2013), Mayordomo et al. (2014), and de Mendonça and da Silva (2018). However, they were contrary to Tram et al. (2021). The shock of exchange rate volatility inﬂuence the banks’ assets and liabilities, especially when there is no hedging or insurance to cover the risk. The Asia Financial Crises in 1997, where Indonesia was one of severely hit economies, is a good example of the catastrophic impact of exchange rate to the banking system. c. Central bank funding and T bill rate: The outcome is statistically signiﬁcant, while the effect was mixed between estimation models. CoVaR and MES reported the negative effects of FFR on the systemic risk, while SRISK reported the opposite. We suspect that the SRISK methodology, which considered the leverage effect, the outcome where bank assets sensitivity to the monetary policy interest rate changes, which was also studied by Jobst (2014) and Brunnermeier and Pedersen (2009). From another perspective, when we assessed the delta of the 3-month T bill, the implication was the same across all models. The phenomena could indicate that the banks are more sensitive to the FFR than the 3-month T bill rate, as it resembles the overnight money market of short-term liquidity resorts. Ramos-Tallada (2015) also iterated the sensitivity of banks to short-term interest rates and potential losses during the tight monetary policy. d. Liquidity spread: In general, the liquidity spread is not signiﬁcantly related to the banks’ systemic risk exposure. Because we used the 3-month repo rate, the non- existence could be traced to the very limited repo transactions in the Indonesian banking market. However, the effect could be different for other countries as it very much depends on the banks’ portfolios. On the other hand, TED spread the results are quite mixed among models. CoVaR detected a negative trend for systemic risk and was in line with Ramos-Tallada (2015), while MES and SRISK perceived the change as adding more fuel to the risk (Laséen et al. 2017). Further research should explore the impact of the SRISK model on benchmark rates as it is also arguably in line with the central bank and funding rate. Risks 2022, 10, 174 19 of 27 4.4. Technical Integration The BCBS (2018) indicator-based approach values the institution size, interconnected- ness, substitutability, global cross-jurisdictional activity, and complexity. Basel guidelines give an equal proportion of 20% weight to the ﬁve categories. BCBS (2012) allows depar- ture from the guidelines with purpose to better capture speciﬁc Domestic Systemically Important Banks (D-SIBs) characters and country externalities. Indonesia Financial Services Authority (OJK) as the bank regulator adjusted the formula composition and re-arranged the indicators following POJK No. 2/POJK.03/2018. The SIBs assessment indicators after country adjustment are exhibited in Table 14. Table 14. Basel and OJK Indicators. Category and BCBS Indicator Category Adjusted Indicators Indicator Weighting G-SIBs Weighting (Weighting) D-SIBs Weighting Size (20%) Total exposures 20% Size (33.3%) Total exposures 100% Intra-ﬁnancial system Intra-ﬁnancial system 6.67% 33.3% assets assets Interconnectedness Interconnectedness Intra-ﬁnancial system Intra-ﬁnancial system 6.67% 33.3% (20%) (33.3%) liabilities liabilities Securities outstanding 6.67% Securities outstanding 33.3% Notional amount of over Notional amount of the counter (OTC) 6.67% over the counter (OTC) 25% derivatives derivatives Trading and Level 3 assets 6.67% available-for-sale 25% Complexity Complexity (20%) securities (33.3%) Domestic indicators 25% Trading and available for Substitutability 6.67% sale securities (payment system & 25% custodian) Assets under custody 6.67% Payment activity 6.67% Substitutability (20%) Underwritten transactions 3.33% in debt & equity markets Trading volume 3.33% Cross-jurisdictional claims 10% Cross-jurisdictional Cross-jurisdictional activity (20%) 10% liabilities Source: OJK (2018). We aimed to provide a practical assessment framework and possible technical in- dicators to integrate the effects of macroeconomic shocks into the SIBs calculation. The tailor-made designs for each country were not only permitted by Basel but were also im- portant for providing holistic supervision analysis to mitigate future systemic risk (BCBS 2012). We used Indonesian banks as the sample, but regulatory authority could be repli- cated to non-bank ﬁnancial institutions, albeit adjusted to industry or speciﬁc country characteristics, risk, etc. The framework starts using a base model derived from BCBS (2018) and is then developed using a combination of macroeconomics and micro-bank granular data. During the preliminary steps, supervisors discuss what variables or ratios to use that represent both aspects and allocate weight to each variable and what technical computation methods and data sources to use. Some reading related to the speciﬁc country SIBs like Brämer and Gischer (2013) when determining the suggestion for Australia D-SIBs, Risks 2022, 10, 174 20 of 27 Bengtsson et al. (2013) for Sweden banks, and Glasserman and Loudis (2015) in their report comparing the US and international G-SIBs. The third step involved the data collection process, where much varies depending on what statistics are gathered. The source could come from the internal data warehouse based on ﬁnancial report submissions or external database sources. After data collection, the process continues to the analysis and technical calculations. For thorough assessment processes, we propose the integration of the market model approach to complement and validate the systemic bank shortlist based on Basel. The choice of market model could also be developed further to suit policy-makers’ needs. Regarding the weight for each ratio or parameters, they could be attributed equally or based on some other methods, such as using structural equation model, professional judgment and survey, or a combination of both. The last step in the framework is to group the banks or ﬁnancial institutions based on the analysis. The methods for segregating the groups are also quite open, which can act as a validation tool for comparison. For the illustration of framework refer to Figure 2. In addition, to put the assessment framework into practice or technical calculation, we also suggest adding some ratios and parameters to reﬂect the integration of macro and micro data into the SIBs calculation (see Table 15). The change of methodology for the reviewing process is also encouraged by Basel at least once every three years (BCBS 2018). The proposed sample of ratios to represent macroeconomics shocks in line with our previous regression results, like currency exposures. Incorporating the entity creates exposure to an unfavourable swing of currency move- ment, i.e., unhedged liabilities to total liabilities. Indonesia experienced high currency volatility during the Asia Financial Crises in 1997 after the shift from the pegged currency system to the ﬂoating system. Nowadays, the central bank imposes mandatory hedging to portion foreign liabilities; however, some are still exposed to sudden shocks. Market volatility: Stock beta, marked to market securities per total securities in portfolio, T-bills, and T-bonds to total securities. This ratio is to acknowledge the effect of market volatility that could bring potential harm to ﬁnancial institutions. We also consider government bonds risky investments, where the Eurozone sovereign debt crisis in 2010– 2011 is a ﬁne example of a similar condition. Paltalidis et al. (2015) provided evidence where the sovereign credit channel was one of the systemic risk transmission channels. Policies exposure: The delta of future incomes or liabilities are seen as the consequences of change in the policy interest rate. The ratios aim to capture the entity fragility coming from government regulations or policy-makers’ decisions. The sample under this criterion shows the change in risk free anchor rate, people mobility restriction impact on business during the Covid pandemic, administered price, etc. Risks 2022, 10, 174 21 of 27 Risks 2022, 10, x FOR PEER REVIEW 21 of 27 Score Macro and micro‐In‐ SIB Score in De‐ Base Indicators Calculation & Vali‐ Data Collection dicators & Base Ad‐ scending Order dation justment Cluster Analysis Network central‐ -Micro-excel and ra- Source: Z‐score (Calinski‐ ity tios BCBS analysis Harabasz) - Indicators and -Macro market weight model (CoVaR, -Eikon Thomson Principal com‐ - Technical MES, SRISK), early Results as‐ Banks grouped Reuters/Bloom- ponent analysis, computation warning system, signed to to 5 clusters berg, etc Granger causal‐ - Data sources\ stress test, etc. normal dis‐ -F/S submitted to ity -Equal weight or dif- tribution regulator SIB if fer based on risk i.e., curve In the top 2 structural equation Weighted net‐ modelling, profes- work graph map SIB if sional judgement, z‐score  80% analytical hierarchy process. Threshold score and SIB list SIB bucketing & Capital sur‐ charge Figure 2. Assessment Framework Workﬂow. Source: Author adapted from OJK (2018). Figure 2. Assessment Framework Workflow. Source: Author adapted from OJK (2018). Risks 2022, 10, 174 22 of 27 Table 15. SIBs Technical Integration. Indicator POJK No. 2/POJK.03/2018 Indicator Macro-Micro Category and BCBS Category Category Indicator Weight- Indicators Weight- Indicators Weighting G-SIBs (Weighting) (Weighting) Weighting ing D-SIBs ing D-SIBs Size (20%) Total exposures 20% Size (33.3%) Total exposures 100% Size (25%) Total exposures 100% Intra-ﬁnancial system Intra-ﬁnancial system 6.67% Intra-ﬁnancial system assets 33.3% 33.3% assets assets Interconnectedness Interconnectedness Interconnectedness Intra-ﬁnancial system Intra-ﬁnancial system 6.67% Intra-ﬁnancial system liabilities 33.3% 33.3% (20%) (33.3%) (25%) liabilities liabilities Securities outstanding 6.67% Securities outstanding 33.3% Securities outstanding 33.3% Notional amount of Notional amount of over the Notional amount of over the counter (OTC) 6.67% counter (OTC) 25% over the counter (OTC) 25% derivatives derivatives derivatives Trading and Trading and available-for-sale Level 3 assets 6.67% 25% available-for-sale 25% Complexity Complexity (20%) securities Complexity (25%) securities (33.3%) Domestic indicators 25% Domestic indicators 25% Trading and available 6.67% for sale securities Substitutability Substitutability (payment system 25% (payment system & 25% & custodian) custodian) Assets under custody 6.67% Currency exposure 33.3% Payment activity 6.67% Market volatility 33.3% Macroeconomics Substitutability shocks Underwritten (20%) (25%) transactions in debt & 3.33% Policies exposure 33.3% equity markets Trading volume 3.33% Cross-jurisdictional 10% claims Cross-jurisdictional activity (20%) Cross-jurisdictional 10% liabilities Source: Author adapted from BCBS (2018) and OJK (2018). Risks 2022, 10, 174 23 of 27 5. Conclusions and Policies Implication This paper investigated how macroeconomic shocks could affect systemic risk through several transmission channels. To explore macroeconomic variables’ connection to systemic risk, we employed three market models: CoVaR (Adrian and Brunnermeier 2016), expected marginal shortfall (Acharya et al. 2012), and SRISK (Brownlees and Engle 2017) using the adjusted linear equation used by de Mendonça and da Silva (2018) and carrying it forward by employing ﬁxed effects, random effects, and GARCH models. To consider the unobserved groups of variables that could affect the independent variables, we ﬁt FMM. The ﬁndings show that stock beta, market index volatility, and exchange rate volatility amplify the transmission of systemic risk. In addition, the change in anchor interest rate by policy-makers is also signiﬁcantly proven by statistics, though the effect varies among the market models. The difference could come from the employed method variables and the impact being different for each interest rate time horizon. Furthermore, related to the liquidity spread, the outcomes are a mixture between models. This paper proposes some practical improvement steps in the systemic risk assessment framework to better capture the potential macroeconomic shocks. We also suggest technical integration calculation samples and ratios reﬂecting the added steps. The integrated macro and micro granular data could portray the overall risk endogenously and externally expose the systemically important ﬁnancial institutions. However, the study poses some shortcomings as it does not isolate certain window periods of crises, and it applied the cross-section systemic risk models only. The limitations open the opportunity for further research to explore the impact of other non-cross section models, such as the cross-entropy joint probability of defaults and isolating the outcome to certain crises, such as the GFC 2008 and COVID-19 pandemic. Author Contributions: Conceptualization, M.Z.S.; Data curation, M.R. and M.Z.S.; Formal analysis, M.R. and M.Z.S.; Funding acquisition, M.R. and S.M.; Methodology, M.Z.S.; Project administration, M.Z.S., S.M., and K.D.; Resources, S.M. and K.D.; Software, M.Z.S., S.M. and K.D.; Supervision, K.D., M.R. and S.M.; Validation, M.R. and S.M.; Visualization, S.M.; Writing—original draft, M.Z.S.; Writing—review & editing, M.R., M.Z.S. and K.D. All authors have read and agreed to the published version of the manuscript. Funding: This research gain funded from Australia Awards ID No. 17/1126 and the APC was funded by State University of Jakarta. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Market data is from Eikon Thomson Reuters and prudential balance sheet data is from Indonesia Financial Services Authority. Acknowledgments: This paper presents the authors’ views and will not be thought of as those of Indonesia’s Financial Services Authority. We would like to thank Qiongbing Wu (Western Syd- ney University), two anonymous reviewers, and Capstone editing for the valuable comments and discussion during the writing process of this paper. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A. Robustness Test 1. Pooled OLS Classical Tests a. To ensure the robustness of results, we run some tests of OLS classic assumptions. First, using Breusch Pagan to detect the heteroscedasticity in the DCoVaR series. Breusch–Pagan/Cook–Weisberg test for heteroskedasticity Assumption: Normal error terms Variable: Fitted values of Delta_CoVaR H0: Constant variance chi (1) = 19.08 Prob > chi = 0.0000 b. Autocorrelation of error terms Risks 2022, 10, x FOR PEER REVIEW 24 of 27 chi (1) = 19.08 Risks 2022, 10, 174 24 of 27 Prob > chi = 0.0000 b. Autocorrelation of error terms Table A1. Breusch-Godfrey LM test for autocorrelation DCoVaR. Table A1. Breusch‐Godfrey LM test for autocorrelation ΔCoVaR. 2 2 Lags(p) chi df Prob > chi 2 2 Lags(p) chi df Prob > chi 1 479.55 1 0.0000 1 479.55 1 0.0000 Ho: no serial correlation. Ho: no serial correlation. The Breusch-Godfrey test of residual error term test is signiﬁcant therefore it rejects The Breusch‐Godfrey test of residual error term test is significant therefore it rejects the null hypothesis of no autocorrelation. To determine the density whether the residuals the null hypothesis of no autocorrelation. To determine the density whether the residuals are normally distributed we plot it using kernel density estimation. The ﬁgure shows that are normally distributed we plot it using kernel density estimation. The figure shows that kurtosis is higher with narrow distribution compared to benchmark. kurtosis is higher with narrow distribution compared to benchmark. Figure A1. Residual distribution curve Figure A1. Residual distribution curve. 2. ARIMA (1,0,1) ΔCoVaR 2. ARIMA (1,0,1) DCoVaR Using ARIMA (1,0,1) both AR(1) and MA(1) statistically significant and hint there are linear Using ARIMA (1,0,1) both AR(1) and MA(1) statistically signiﬁcant and hint there are correlation or autocorrelation. linear correlation or autocorrelation. Table A2. ARIMA regression results Table A2. ARIMA regression results. DCovar_1 Coef. St. Err. t‐Value p‐Value [95% Conf Interval] Sig DCovar_1 Coef. St. Err. t-Value p-Value [95% Conf Interval] Sig DCovar_1 DCovar_1 _cons 0.008 0.008 1.09 0.277 −0.007 0.023 _cons 0.008 0.008 1.09 0.277 0.007 0.023 ARMA ARMA ar ar L1. 0.999 0.001 1773.13 0 0.998 1 *** L1. 0.999 0.001 1773.13 0 0.998 1 *** ma. ma. L1. 0.639 0.003 223.91 0 0.644 0.633 *** L1. −0.639 0.003 −223.91 0 −0.644 −0.633 *** Constant /sigma 0.001 0 500.40 0 0.001 0.001 *** *** p < 0.01. Risks 2022, 10, 174 25 of 27 To investigate further the ﬁtness model of AR and MA for DCoVaR we run another test using correlogram and partial auto correlogram. The results conﬁrm the ARIMA (1,0,1) result of autocorrelation existence and to address the problem we use ARCH and GARCH model for DCoVaR. Table A3. Autocorrelation test. 1 0 1 1 0 1 Q Prob > Q LAG AC PAC [Autocor] [Partial Autocor] 1 0.0374 0.0381 0.0417 2 0.0715 0.0802 0.0001 3 0.0416 0.0488 0.0000 4 0.0536 0.0769 0.0000 5 0.0938 0.1109 0.0000 6 0.0315 0.0463 0.0000 7 0.0161 0.0300 0.0000 8 0.0919 0.0907 0.0000 9 0.1099 0.1311 0.0000 10 0.0015 0.0369 0.0000 11 0.0247 0.0091 0.0000 12 0.0302 0.0573 0.0000 13 0.0602 0.0105 0.0000 14 0.0024 0.0197 0.0000 15 0.0122 0.0419 0.0000 16 0.0008 0.0170 0.0000 17 0.0279 0.0332 0.0000 18 0.0188 0.0187 0.0000 19 0.0260 0.0305 0.0000 20 0.0335 0.0405 0.0000 Notes We provide the datasets for those want to reproduce the reported results. The Matlab coding are also available in the GitHub website as provided and developed by Belluzo (2020). Adrian and Brunnermeier (2016) used commonly convention sign q > 50 to represent the median, other like Benoit et al. (2013) write it as median. References Acharya, Viral, Robert Engle, and Matthew Richardson. 2012. 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Macroeconomics of Systemic Risk: Transmission Channels and Technical Integration

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ISSN: 2227-9091
DOI: 10.3390/risks10090174
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Risks – Multidisciplinary Digital Publishing Institute

Published: Sep 1, 2022

Keywords: systemic risk; interconnectedness; financial crises; macroeconomics; market model

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Macroeconomics of Systemic Risk: Transmission Channels and Technical Integration

Macroeconomics of Systemic Risk: Transmission Channels and Technical Integration

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Macroeconomics of Systemic Risk: Transmission Channels and Technical Integration

Macroeconomics of Systemic Risk: Transmission Channels and Technical Integration

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