the first one shows the significant difference

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35 Conclusion

The results posted by the regression show three main key takeaways: the first one shows the significant difference between the effect of policy rates during good times and bad times. This should segregate the policy rates intended for monetary impact and systemic risk impact. This should be raised as an issue between systemic risk and monetary policy department since there is a conflicting interest that each target policy exhibits.

The second key takeaway of the regression is the near zero significant impact of the policy variable on the growth of loan portfolio. This shows that the policy variables have little effect on the loan portfolio. Conclusion can be brought up that there could be other policy measures that the Central Bank can utilize to impact the loan portfolios of banks to allow them to lend more, especially during this time of need to nonfinancial institutions.

This transitions to the various effects towards different bank classes and asset size. The impact on the universal and commercial banks revolves around the reserve requirement ratio which shows the significance of reserve requirement cuts on the loan portfolios of universal and commercial banks. This shows the significance of reserve requirement as a nudge to the banks to change their loan portfolio during systemic shocks. However, like other policy variables, the effect is near zero which imposes the need of other policies than reserve requirements to affect the loan portfolio. This is further explained by the insignificance of policy variables on the thrift and rural banks during good times and bad times.

Lastly, for the asset classes, it has been identified that the impact of the policy variables decreases as the total assets of the banks increases due to their bigger capability to hold their liquidity and not respond immediately to the changes given by the Central Bank. This

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36 shows that the smaller banks tend to be reactive to the policy variables, but again, the effect is near zero, which shows the weak association of policy rates and bank lending.

Through the findings, the realization comes from the paper of Laseen (2017) which shows the trade-off between systemic risk and monetary policy. The risk-taking behaviour of the banks is affected by “systematic monetary policy reaction”. It comes from the idea that the negative impact of contractionary monetary policy implies a negative impact on productivity even without altering the risk behaviour of the banks. However, implementing policies that can affect the behaviour due to the small surprise of the upcoming systemic shock and strong financial sector will mitigate the negative effects. Another key factor brought up by the findings is that the policy reaction should go beyond the monetary policy. It was pointed out that financial variables such as risk and others are less affected by monetary policy which can only return some welfare improvements. This property shows the limit of monetary policy to mitigate risk, especially making a huge significant impact on the bank lending of banks. There needs to be an optimal policy mix of monetary and systemic risk policy or a priority of one of the two policies depending on the presence of crisis, the type of market, and the characteristics of bank. With these steps, we can truly establish a strong significant link between the Central Bank and the banks all around the country.

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37 Outlook to the Central Bank’s next steps

The findings of the study show the struggles of the Central Bank with instruments to affect the economy, especially specifically targeted markets like the lending market. Therefore, if we consider systemic risk, we cannot just have a “systemic-risk enhanced monetary policy” to affect the loan behaviour of banks while considering systemic risk. We learned from past phenomena that we cannot use one instrument to address two issues. This further confirms the policy mix of monetary and systemic risk policy to further address the concerns of the economy, as well as impact the lending behaviour of banks towards nonfinancial institutions.

Another concern that is seen in the paper that should be addressed is the measurement of systemic risk. Unlike the systemic shocks present in the paper, the significance of central banks investigating systemic risk is its anticipation. It shows an opportunity for central banks to form models to anticipate systemic risk to evidently affect lending behaviour of banks. Seeing that the systemicness arises from the lending behaviour of banks to nonfinancial institutions, the Central Bank can device certain models such as network models on interbank and nonfinancial institutions lending, as well as non-bank financial institutions like pawnshops. This is to further specify specific banks and/or nonfinancial institutions that can be heavily affected by impending systemic shocks and current systemic risk.

It also raises an issue on how risk indices are developed. When we look at the current risk indices that we have globally like the volatility index (VIX), systemic risk does not show.

This poses problems because financial markets price private transactions but never price social costs. It is associated with the fact that systemic risk is a public good which makes it being hard to have a definite price. With these in hand, we must focus on having an

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38 estimate on the “price of systemic risk” to properly integrate it with the decisions being done by financial institutions and to anticipate incoming systemic shocks.

This all transition to the macroprudential framework done by the BSP to address the following findings. One key factor shown in their recent macroprudential policy strategy framework is the value of financial stability communication. Without intervention, financial markets will not have interests regarding the social cost of their activities and/or systemic risk. Therefore, it will be impactful if BSP can develop a strong communication channel towards the financial institutions during good and bad times to do appropriate targeting of policies done by the Central Bank.

Lastly, I would like to end the paper by concluding that alone, reserve requirement cuts will only affect a fraction of banks’ decisions towards lending. It can still be considered as a policy tool but only complementary to other instruments and cannot be used as a standalone policy. Considering the behaviours of the big banks in the Philippines, manipulating the reserve requirement alone does not suffice the big change in their own behaviour, especially during bad times. It can even have contradicting effects on the behaviour of the banks. Moreover, it also shows the insignificance of monetary policy during crisis, especially when the crisis is coming from the real economy. It can be that monetary policies can affect the economy short term, but as seen with the problems like the liquidity trap, monetary policies alone cannot make a huge dent in the systemicness problem that the country is experiencing now. Therefore, there must be a huge development and dynamic policy mix that the government and Central Bank will commit to impact not only the levels of the lending problem, but the behaviour of the banks in the long run.

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42 Appendix: Robust Testing Results

Table 12.1

Robust Testing of the DiffGMM Model (Coefficient of the Lagged Dependent Variable) (Contractionary and Expansionary Monetary Policy) (Overnight Reverse Repurchase

Rate)

Universal and Commercial Banks

Variables Without Systemic Shocks With Systemic Shocks

Pooled OLS -0.1435781 -0.1435798

Fixed Effects -0.1728634 -0.1729463

2Step DiffGMM -0.1552784 -0.1505018

Source: Author’s calculations.

Table 12.2

Robust Testing of the DiffGMM Model (Coefficient of the Lagged Dependent Variable) (Contractionary and Expansionary Monetary Policy) (Reserve Requirement Ratio)

Universal and Commercial Banks

Pooled OLS -0.1501883 -0.1436406

Fixed Effects -0.1805319 -0.172868

2Step DiffGMM -0.1531664 -0.1485

Source: Author’s calculations

Table 13.1

Robust Testing of the DiffGMM Model (Coefficient of the Lagged Dependent Variable)

(Contractionary and Expansionary Monetary Policy) (Overnight Reverse Repurchase Rate)

Thrift and Rural Banks

Pooled OLS 0.0074879 0.0088262

Fixed Effects -0.0482062 -0.046204

2Step DiffGMM -0.0233756 -0.016412

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43 Table 13.2

(Contractionary and Expansionary Monetary Policy) (Reserve Requirement Ratio) Thrift and Rural Banks

Pooled OLS 0.0077272 0.0088936

Fixed Effects -0.0460823 -0.0459259

2Step DiffGMM -0.0130934 -0.0157667

Table 14.1

(Contractionary and Expansionary Monetary Policy) (Overnight Reverse Repurchase Rate)

Small Assets Banks

Pooled OLS 0.0157295 0.0171176

Fixed Effects -0.04568 -0.0439482

2Step DiffGMM -0.159138 -0.0140091

Table 14.2

(Contractionary and Expansionary Monetary Policy) (Reserve Requirement Ratio) Small Assets Banks

Pooled OLS 0.0173989 0.0176732

Fixed Effects -0.0438248 -0.0435719

2Step DiffGMM -0.0156495 -0.0049885

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44 Table 15.1

Robust Testing of the DiffGMM Model (Coefficient of the Lagged Dependent Variable) (Contractionary and Expansionary Monetary Policy)(Overnight Reverse Repurchase

Rate)

Medium Assets Banks

Pooled OLS -0.2656634 -0.2649699

Fixed Effects -0.309299 -0.3091357

2Step DiffGMM -0.2913077 -0.2828559

Table 15.2

Medium Assets Banks

Pooled OLS -0.2664248 -0.2651909

Fixed Effects -0.3211109 -0.3092804

2Step DiffGMM -0.2910182 -0.2865321

Table 16.1

Robust Testing of the DiffGMM Model (Coefficient of the Lagged Dependent Variable) (Contractionary and Expansionary Monetary Policy) (Overnight Reverse Repurchase

Rate)

Large Assets Banks

Pooled OLS -0.2121995 -0.2135655

Fixed Effects -0.2353156 -0.2364137

2Step DiffGMM -0.2059541 -0.2039934

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45 Table 16.2

Large Assets Banks

Pooled OLS -0.2221689 -0.2139125

Fixed Effects -0.2447659 -0.2364699

2Step DiffGMM -0.2043656 -0.2063535