Revisiting the Transmission Mechanisms of Monetary Policy …...• Inflation targeting as the...

Revisiting the Transmission Mechanisms of Monetary Policy in the Philippines

(Preliminary Results)

Eloisa Glindro, Vic Delloro, Christopher Martin, and Joan Allon

BIS – CAFRAL Asian Research Network Workshop 22 March 2016

Outline of presentation

I. Objectives

II. Overview of Philippine monetary policy

framework

III. Empirical analysis : methodology and results

IV. Conclusion and policy implications

I. Objectives

• Examine the relative strength of the transmission channels of monetary policy.

• Provide policy perspective on the estimation results

• Inflation targeting as the monetary policy framework (since 2002)

• Distinct feature : the BSP charter prohibits issuance of own securities for monetary policy operations.

II. Overview: Philippine monetary policy framework

Source of basic data: BSP

Stylized fact: Mean and volatility of inflation are lowest during the IT period

13.10

7.01 4.00

10.43

1.80 1.87 0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

1980 - 1992 1993 - 2001 2002 - 2015Q4

Mean Standard Deviation

4.04

6.00

3.31

1.73 2.69

1.12

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

2002 - 2007 2008 - 2009 2010 - 2015Q4

Mean Standard Deviation

Stylized fact: Inflation has been on target since 2009

Inflation targeting period

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

12.0

Actual Inflation Target


Stylized fact: Inflation stabilization happened alongside greater

exchange rate flexibility

0

10

20

30

40

50

60

-10

0

10

20

30

40

50

60

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Inflation Rate (LHS) Reverse Repurchase Rate (LHS) Nominal Exchange Rate (RHS)

in % ₱/US$1


III. Empirical Analysis

Empirical methodology

• Bayesian vector autoregression (integrative analysis of transmission channels)

• Complemented by: – Autoregressive distributed lag model – Rolling regression

Why not VAR? A simple two-variable VAR with two lags has the following form:

and a symmetric residual variance-covariance matrix, Σ :

or

total number of parameters to be estimated equals n(np+1)

Basic source: Rummel, O. (2015). 2nd SEACEN intermediate course on econometric modeling and forecasting. Bank of England

𝑦1𝑡 = 𝑐1 + 𝛽11,1𝑦1,𝑡−1 + 𝛽12,1𝑦2,𝑡−1 + 𝛽11,2𝑦1,𝑡−2 + 𝛽12,2𝑦2,𝑡−2 + 𝜀1𝑡 𝑦2𝑡 = 𝑐2 + 𝛽21,1𝑦1,𝑡−1 + 𝛽22,1𝑦2,𝑡−1 + 𝛽21,2𝑦1,𝑡−2 + 𝛽22,2𝑦2,𝑡−2 + 𝜀2𝑡

𝑦1𝑡𝑦2𝑡 =

𝑐1𝑐2 +

𝛽11,1 𝛽12,1𝛽21,1 𝛽22,1

𝑦1𝑡−1𝑦2𝑡−1 +

𝛽11,2 𝛽12,2𝛽21,2 𝛽22,2

𝑦1𝑡−2𝑦2𝑡−2 +

𝜀1𝑡𝜀2𝑡

Why BVAR? • With limited macroeconomic datasets, Bayesian methods are

increasingly used to deal with the curse of dimensionality (Litterman, 1986; Doan, Litterman, and Sims, 1984; Sims &Zha, 1998).

• Classical econometrics treats the parameters of a model as fixed,unknown constants to be estimated.

• Bayesian econometrics recognizes parameter uncertainty.

– Parameters are treated as random variables to which a probability distribution is assigned

– Specifically, we want to learn about a vector of unknown parameter coefficients 𝜃 = (𝑏, Σ),conditional on the data, y.

Bayes’ Rule Conditional distribution of the parameters given the data,

• The prior density, p(𝜃), contains any non-data information about 𝜃and summarisesour inference about 𝜃before seeing the data.

𝑝 𝜃 𝑦 ∝ 𝑝 𝑦 𝜃 𝑥 𝑝(𝜃) posterior density

likelihood function

prior density

• The posterior density, p(𝜃|y), summarises all we know about 𝜃after seeing the data – the posterior is a result of an updating rule that combines both data and non-data information .

• The likelihood function, p(y|𝜃), is the density of the data conditional on the parameters of the model (referred to as the data-generating process).

Bases for Litterman/Minnesota priors

1. Stochastic trend (unit root) in many macroeconomic time series;

2. More recent values of a series typically has more information about the current value of the series; and

3. Past values of the variable itself contain more information about its current value of the series than past values of other variables.

• The simplicity of the Litterman/Minnesota priors comes from the fact that the Σ is assumed to be known (replaced by its estimate Σ�).

Litterman/Minnesota priors

• Drawback: it does not provide a full Bayesian treatment of Σ as an unknown parameter.

𝑝(𝑏|Σ�)~𝑁(𝑏,𝑉)

• When Σ is replaced by Σ�, only the prior and prior distribution for 𝑏 need to be identified.

Minnesota prior mean

• Many macroeconomic time series follow a random walk: – the prior mean of the coefficient on the own first lag

of each variable is one (𝛽𝑖𝑖,1 = 1; i = 1, … , p); and – the prior means of coefficients on higher lags

(including cross lags) are likely to be close to zero

• However, the prior mean assumption would depend on the stationary properties of the underlying series.

Minnesota prior variance

• The prior variance for coefficient 𝛽𝑖𝑖 is specified as follows:

where: − p is the lag length; − 𝜆1, 𝜆2, 𝜆3 and 𝜆4 are hyperparametersset by the researcher; − σiand σjare the variances of the error term in equations iand j, where irefers to the

dependent variable in the i-th equation and j to the independent variables in that equation.

𝜆1𝑝𝜆3

2

𝜎𝑖𝜆1𝜆2𝜎𝑖𝑝𝜆3

2

𝜎𝑖𝜆4 2

𝑖𝑖 𝑖 ≠ 𝑗

𝑖𝑖 𝑖 = 𝑗

for the constant

𝑦1𝑡 = 𝑐1 + 𝛽11,1𝑦1,𝑡−1 + 𝛽12,1𝑦2,𝑡−1 + 𝛽11,2𝑦1,𝑡−2 + 𝛽12,2𝑦2,𝑡−2 + 𝜀1𝑡

𝜎1𝜆4 2

𝜆1/1𝜆32

= 𝜆1 2

𝜎1𝜆1𝜆2/𝜎2 2

𝜎1𝜆1𝜆2/𝜎22𝜆32

𝜆1/2𝜆32

Minnesota prior variance Deterministic Own lags Lags of other variables

Prior hyperparameters: • Lambda 1 (λ1) = 0.10 • Lambda 2 (λ2) = 0.99 • Lambda 3 (λ3) = 1.00

• The BVAR system consists of the following: – rate of change in nominal exchange rate (q/q) – 91-day secondary Treasury bill rate – weighted average lending rate of universal/commercial banks – growth rate of nominal credit to the private sector (q/q, sa) – growth rate of real household consumption (q/q, sa) – growth rate of gross fixed capital formation (q/q, sa) – inflation rate (q/q) – weighted average of BSP’s overnight RRP rate and SDA rate.

• Trend and Federal Funds rate are included as exogenous variables in the BVAR system.

• Period of estimation : 2000Q2 – 2014Q4 • Lag structure : 4 lags

1/ SDA and RRP are weighted by their respective volumes of transactions.

BVAR system

Empirical Results

Response of Selected Macroeconomic Indicators to Generalized Shock to Weighted Policy Rate (in ppt)

-0.50

0.00

0.50

1.00

1.50

1 2 3 4 5 6 7 8 9 10

Nominal exchange rate

-0.20

0.00

0.20

0.40

0.60

0.80

1 2 3 4 5 6 7 8 9 10

91-day Tbill

-0.050.000.050.100.150.200.250.30

1 2 3 4 5 6 7 8 9 10

Average lending rate

-0.10

-0.05

0.00

0.05

0.10

1 2 3 4 5 6 7 8 9 10

Household consumption

-1.00-0.80-0.60-0.40-0.200.000.200.40

1 2 3 4 5 6 7 8 9 10

Gross fixed capital formation

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

1 2 3 4 5 6 7 8 9 10

Credit to the private sector

-0.15

-0.10

-0.05

0.00

0.05

0.10

1 2 3 4 5 6 7 8 9 10

Inflation

-0.200.000.200.400.600.801.001.20

1 2 3 4 5 6 7 8 9 10

Weighted policy rate

• Investment falls by a much larger scale than consumption.

• The initial one ppt increase in policy rate is fully transmitted into the 91-day T-bill rate and about 2/3 in the case of lending rate after four quarters

• Modest impact on credit

• Modest impact on inflation

Healthy domestic financial system

Notes: • End-period data: NPL ratio (inclusive of interbank loans) • Starting January 2013, figures are computed as prescribed under BSP Circular No. 772. Gross NPL represents the actual level of NPL without

any adjustment for loans treated as “loss” and fully provisioned • CAR 2001-2011 data is on the Philippine Banking System; data for 2012 onwards is on U/KBs.

PH banks remain adequately capitalized Capital Adequacy Ratio (%)

Quality of banks’ assets and loans continue to improve

Gross Non-Performing Loans and Non-Performing Assets to Gross Assets Ratios (%)

6

8

10

12

14

16

18

20

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

end-

Sep

'15

CAR Solo CAR ConsolidatedBIS Standard BSP Standard

0

1

2

3

4

5

6

7

8

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

NPL Ratio NPA Ratio

2015 GNPL: 2.09% GNPA: 2.03%

End-Sep 2015 CAR Solo: 15.55%

CAR Consolidated: 16.40%

21

Response of Selected Macroeconomic Indicators to Generalized Shock to Inflation (in ppt)

-0.20

0.00

0.20

0.40

0.60

0.80

1 2 3 4 5 6 7 8 9 10


-0.10

0.00

0.10

0.20

0.30

0.40

1 2 3 4 5 6 7 8 9 10

91-day Tbill

-0.05

0.00

0.05

0.10

0.15

1 2 3 4 5 6 7 8 9 10


-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

1 2 3 4 5 6 7 8 9 10


-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0.00

0.01

1 2 3 4 5 6 7 8 9 10


-0.20

-0.15

-0.10

-0.05

0.00

0.05

1 2 3 4 5 6 7 8 9 10


-0.50

0.00

0.50

1.00

1.50

1 2 3 4 5 6 7 8 9 10

Inflation

-0.02-0.010.000.010.020.030.040.05

1 2 3 4 5 6 7 8 9 10


• Moderate effect on credit • Inflation shock exerts a more prolonged impact on real household consumption and investment but relatively moderate effect on investment.

• Policy rate response exhibits inertia in the face of inflation shock

• Interest rates respond less relative to a monetary policy shock

Response of Selected Macroeconomic Indicators to Generalized Shock to Nominal Exchange Rate (in ppt)

-0.50

0.00

0.50

1.00

1.50

1 2 3 4 5 6 7 8 9 10


-0.02

0.00

0.02

0.04

0.06

0.08

0.10

1 2 3 4 5 6 7 8 9 10

91-day Tbill

-0.01

0.00

0.01

0.02

0.03

0.04

1 2 3 4 5 6 7 8 9 10


-0.05

0.00

0.05

0.10

1 2 3 4 5 6 7 8 9 10


-0.02

-0.01

-0.01

0.00

0.01

0.01

0.02

0.02

1 2 3 4 5 6 7 8 9 10


-0.15

-0.10

-0.05

0.00

0.05

1 2 3 4 5 6 7 8 9 10


-0.04

-0.02

0.00

0.02

0.04

0.06

1 2 3 4 5 6 7 8 9

Inflation

-0.02

0.00

0.02

0.04

0.06

0.08

1 2 3 4 5 6 7 8 9 10


• Relative to inflation and policy rate shocks, the impact of exchange rate shock on both nominal and real credit, and hence, real investment is positive yet minimal.

• Exchange rate shock has the least effect on market rates.

• The effect on inflation is equally small.

Estimated Inflation Persistence and Exchange Rate Pass-Through: ARDL Model

Inflation Persistence Output Gap World rice

inflation World oil inflation

Exchange rate Constant

1982 Q1 2001 Q4

0.45 0.25 0.22 0.03 0.36 1.99

- (0.009) (0.003) (0.129) (0.045) (0.000)

2002 Q1 2015 Q2

0.36 0.29 0.04 0.06 0.15 0.87

- (0.014) (0.010) (0.000) (0.029) (0.000)

Variables (all are stationary) are in first-difference of the logarithms of the series. Reported coefficients correspond to long-run coefficients with p-values in parenthesis. Results of residual diagnostics based on correlogram of Q-statistics do not reject the null hypothesis that the errors from residuals of the models are not serially independent (no evidence of autocorrelation). ARDL bounds test rejects the null of no long-run relationship. Output gap is estimated using Hodrick-Prescott filter.

-0.4

0.0

0.4

0.8

1.2

1.6

94 96 98 00 02 04 06 08 10 12 14

Constant

0.0

0.2

0.4

0.6

0.8

1.0

94 96 98 00 02 04 06 08 10 12 14

Inflation (q/q)

-.2

-.1

.0

.1

.2

.3

94 96 98 00 02 04 06 08 10 12 14

Output gap

.00

.04

.08

.12

.16

.20

94 96 98 00 02 04 06 08 10 12 14

Rice inflation (q/q)

.00

.02

.04

.06

.08

.10

94 96 98 00 02 04 06 08 10 12 14

Oil inflation (q/q)

.05

.10

.15

.20

.25

.30

.35

94 96 98 00 02 04 06 08 10 12 14

Exchange rate (percentage chage, q/q)

Rolling regression: Augmented Phillips Curve

Estimated sterilization coefficients

Sterilization Coefficient

1990 – 2001 -0.70

2002 – 2015 -0.47

BSP staff estimates using Depository Corporations Survey (Standardized Revised Forms-based) The coefficient is a measure of the extent of sterilization where β= -1 represents full monetary sterilization of reserve changes and β = 0 implies no sterilization. A value of the sterilization coefficient between these levels, -1 < β < 0, indicates partial sterilization.

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

96 98 00 02 04 06 08 10 12 14

NFARATIO

Rolling Coefficients, 1990.03 - 2015.12Window = 60; Step = 5

Extension: transmission of global financial shocks to exchange rate and stock market

-.1

.0

.1

.2

.3

2 4 6 8 10

Response of FFR to FFR

-.1

.0

.1

.2

.3

2 4 6 8 10

Response of FFR to VIX

-2

0

2

4

6

2 4 6 8 10

Response of VIX to FFR

-2

0

2

4

6

2 4 6 8 10

Response of VIX to VIX

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10

Response of EMBIPH to FFR

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10

Response of EMBIPH to VIX

-8

-4

0

4

8

12

2 4 6 8 10

Response of portfolio inflows (q/q growth trate) to FFR

-8

-4

0

4

8

12

2 4 6 8 10

Response of portfolio inflows (q/q g.r.) to VIX

-.4

.0

.4

.8

2 4 6 8 10

Response of nominal exchange rate (q/q g.r.) to FFR

-.4

.0

.4

.8

2 4 6 8 10

Response of nominal exchange rate (q/q, g.r.) to VIX

-6

-4

-2

0

2

4

6

2 4 6 8 10

Response of stock prices (q/q, g.r.) to FFR

-6

-4

-2

0

2

4

6

2 4 6 8 10

Response of stock prices (q/q, g.r.) to VIX

Res pons e to Genera l i zed One S.D. Innova ti ons

Upward Credit Rating Momentum Philippine credit ratings hover above the investment grade status

Current ratings: Fitch: BBB- (investment grade), Sep-2015 S&P: BBB (1 notch above investment grade, Apr-2015 Moody’s: Baa2 (investment grade), Dec-2014

Robust external sector dynamics

0

5

10

15

20

25

30

-1

1

3

5

7

9

11

13

15

17

19

21

23

25

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Cash Remittances thru banks (in USD Bn; rhs)Growth Rate (in %; lhs)

Source: BSP Source: BSP

-10.0

0.0

10.0

20.0

-20.0-10.0

0.010.020.030.0

2006

2007

2008

2009

2010

2011

2012

2013

2014

Q1-

Q3

2014

Q1-

Q3

2015

Balance of Payments (BPM6) 2005 - Q3 2015 (in US$ Million)

Capital & Fin'l AccountCurrent AccountBalance of Payments

0

10

20

30

40

50

60

70

0.0

20.0

40.0

60.0

80.0

100.0

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

End-

Sep

'15

Total Outstanding External Debt 2005 – End-September 2015

External Debt (USD Bn; lhs)External Debt/GDP ratio (%; rhs)

End-Sep 2015 US$ 75.6 BnP

(26.0%)

Q1-Q3 2015 US$ 1.8 BnP

2015: US$ 25.8 Bnr

(4.6%) Overseas Filipinos’ Remittances 2006 - 2015

29

End-Feb. ‘16 US$ 81.3 BnP

Selected macroprudential measures 1. Reserve accumulation and associated liquidity management operations during periods of high capital inflows. 2. Prepayment of external obligations 3. Further liberalization of foreign exchange regulations 4. Concentration limits of loans for real estate activities 5. Caps on loan-to-value ratio (60% of the appraised value of RE collateral) 6. Expanded reporting requirement for bank exposures to RE sector 7. Introduction of Real Estate Stress Test (REST) 8. General loan-loss provisioning 9. Single borrower’s limit 10. Limits on banks’ non-deliverable forward (NDF) positions (in March 2013); 11. Limits on open FX positions 12. Asset cover for banks’ FCDU (100% asset cover at all times) 13. Basel 3 minimum capital requirements 14. Basel 3 leverage ratio 15. Segregation of customer funds and securities received by banks in the performance of securities brokering

functions 16. Inclusion of salary loans in the monitoring of HH loans 17. Expanded reporting of cross-border financial positions 18. Improved regulations for stronger credit risk management (e.g., cash flow analysis and ability to pay as critical

factors in assessing credit worthiness of borrowers) 19. Prohibition on (since end-2013) external funds and direct placements by non-banks in the SDA and gradual

lowering the interest rate 20. Lowering of policy rate by 100 bps in 2012, partly to reduce the incentive for foreigners to hold peso-

denominated fixed income securities. 21. Guidelines for determining domestic systemically important banks (DSIBs)

All measures are supported by clear communication strategy with stakeholders


• The effect of policy rate shock is most pronounced on investment but less so on consumption.

• The modest response of inflation to policy rate shock could have also been partly moderated by lower sensitivity to imported inflation and exchange rate, as borne by the ARDL estimates.

• Inflation shock, on the other hand, has a more prolonged impact on real household consumption. – However, policy rate response exhibits inertia in the

face of inflation shock, possibly due to uncertainty in the nature of supply shock.

• Exchange rate shock has the smallest impact on market

rates and components of demand. As a result, the pass-through of exchange rate shock to inflation is equally small.


• The response of credit growth to policy rate adjustment remains relatively modest. – May be partly attributed to strong balance sheet

position of the Philippine banking system. – Such strength, however, may also imply greater

conservatism and higher risk aversion among banks, with natural preference for safer investments.


• The interest rate channel of monetary policy need to gain further traction.

• As the economy moves in the direction of higher growth, the BSP’s planned shift to an interest rate corridor (IRC) system is envisioned to strengthen the BSP’s capacity to guide the market interest rate towards the BSP’s target policy rate.


• Strong fundamentals will not only serve as buffer and anchor but also support a positive differentiation of the Philippine economy in comparison to other countries, which would matter in the severity and volume of potential reversals in capital flows (Fernandez and Kalaw, 2015)


Policy implications

• The current BSP programs on financial inclusion and microfinance are essential complements to existing macroprudential regulations and on-going efforts to finetune monetary policy operations.

Revisiting the Transmission Mechanisms of Monetary Policy in the Philippines

(Preliminary Results)

Eloisa Glindro, Vic Delloro, Christopher Martin, and Joan Allon

BIS – CAFRAL Asian Research Network Workshop 22 March 2016

N.B. The study is part of the 2015 SEACEN collaborative research on Transmission Mechanisms of Monetary Policy. The views do not represent those of the BSP.

Revisiting the Transmission Mechanisms of Monetary Policy …...• Inflation targeting as the...

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