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Chapters on Monetary Policy

Dissertation Defense

Mauricio Villamizar

Advisor: Professor Guido Kuersteiner

Committee: Professor Behzad Diba and Professor Matthew Canzoneri

(2)

1 “Identifying the Effects of Simultaneous Monetary Policy Shocks”

Job Market Paper (2014)

2 “Great Expectations? Evidence from Colombia’s Exchange Rate Survey”

Working Paper No. 735 at the Central Bank of Colombia (2012)

3 “The Impact of FX Intervention in Colombia: An Event Study Approach”

Forthcoming in the Journal of Desarrollo y Sociedad (2014)

Other Papers

“The Effects of FX Intervention: Evidence from a Rule-Based Policy Discontinuity in Colombia”

“The Impact of Pre-Announced Day-to-Day Interventions on the Colombian Exchange Rate”

(3)

Chapter 1

“Identifying the Effects of Simultaneous Monetary Policy Shocks”

(4)

Main Findings

Interest rate interventions have a significant impact on the economy but FX interventions have almost no effect

Empirical anomalies can be eliminated when accounting for the systematic responses of policy

(5)

Motivation

Several economies who claim to have a floating exchange rate (under an inflation targeting regime) do not really float

(6)

Motivation

The impossible trinity (trilemma) indicates that a country cannot Allow for free capital flows

Have autonomous monetary policy Adopt a fixed or managed exchange rate

In the empirical literature, there is a lack of consensus regarding the effectiveness of Central Bank intervention

US: Angrist et al. (2004, 2009, 2011, 2013), Bernanke et al.(1998), Christiano et al. (1996, 1998), Romer and Romer (1989, 2004), Rudebusch et al. (1998)

Latin America: Echavarria et al. (2009a, 2009b, 2013), Dominguez et al. (2012), Adler and Tovar (2011), Toro et al. (2010, 2005), Kamil (2008)

(7)

Colombia

The Central Bank of Colombia has adopted a policy framework consisting of two policy instruments

Interest rate Interventions (IRI) Foreign Exchange Interventions (FXI)

(8)

Research Objective

1 Model monetary policy behavior (parametrically) in order to extract

policy shocks, while leaving the response of the economy unspecified

2 Estimate the effect of these shocks (non-parametrically) on

economic variables

I employ:

Proprietary data(Direct interventions and internal forecasts)

(9)

Challenges

Empirical estimation faces 3 main challanges

1 Measurement Error: Not being able to observe policymakers’ exact

decisions

2 Simultaneity bias: Central banks react to economic conditions, and

economic conditions react to central bank interventions

3 Omitted Variable bias: Not capturing the relevant variables to model

policy behavior

I address (1) and (2) with the information and high frequency of my data set

(10)

Figure: Different Mechanisms of FX Intervention: 1999-2012

(11)

Figure: Intervention Interest Rate: 1999-2012

(12)

Internal Forecasts of Central Bank

Exchange Rate Misalignment Forecasts(et−Forecast(et))

7 “in house” structural models based on PPP, SVEC methodologies, current account equilibrium and HP filters

Inflation Forecasts(Forecast(πt)−πTargett )

Monetary Transmission Mechanism model with 9 equations based on prices, aggregate demand, wages, UIP condition, risk premium, etc.

Long Term GDP Forecasts (yt−Forecast(yt))

(13)

Other Variables Considered

Net position of the CBoC

Total net credit/debit with respect to the financial system

Board Meetings

Analogous to meeting dates of the US Open Market Committee

Capital Controls

During 2007-2008 investors had to deposit 40% of the inflow at the Central Bank during 6 months without interest payments

Other Variables

Exchange rate Volatility International Reserves US Fed Funds Rate Industrial Production

(14)

Methodology

Overview

Model the undertakings of monetary authorities

Extract the unexpected component of policy (policy surprises)

(15)

Independent Policies

FXI Policy Function:

FXIt =max[0, x

0

1tβ1+vt]

vt∼N(0, σ12)

IRI Policy Function:

(16)

Independent Policies

FXI Shock:

1t = FXIt−E[FXIt |x1t]

= FXIt−

Z

FXIt>0

(FXIt)dF(FXIt|x1t)

= FXIt−Φ x10tβ1

σ1

! "

x10tβ1+σ1φ

x10tβ1

σ1

!

/Φ x

0

1tβ1

σ1

!#

IRI Shock:

2t =IRIt−x

0

(17)

Dependent Policies

There is no reasona priori to believe that instruments are independent

A bivariate process forFXIt andIRIt can be described as:

FXIt∗ = x10tβ1+vt FXIt = max[0, FXIt∗]

IRIt = x20tβ2+2t

vt 2t ∼N 0, σ2 1 σ12

σ12 σ22

(18)

Dependent Policies

Constructing the Maximum Likelihood Function (in 2 stages):

DefiningA≡σ21−

σ212 σ2 2

andb≡x10tβ1+σσ122 2

(IRIt−x

0 2tβ2)

Stage 1: When FXIt >0 (FXIt =FXIt∗)

f(FXIt,IRIt) = f (FXI

t |IRIt,x1t,x2t)f (IRIt |x1t,x2t)

= 1

A1/2φ

FXIt∗−b A1/2

1

σ2φ

IRIt−x

0 2tβ2

σ2

(19)

Dependent Policies

Stage 2: When FXIt = 0 (FXIt∗≤0)

f (FXIt,IRIt) = Pr(FXIt∗≤0|IRIt,x1t,x2t)f(IRIt |x1t,x2t)

=

1−Φ

b A1/2

1

σ2φ

IRIt−x

0 2tβ2

σ2

!

Stage 1 + Stage 2

Ln(θ) =

 Y

FXIt∗≤0

1−Φ

b

A1/2

Y

FXIt∗>0

1

A1/2φ

FXI

t −b

A1/2

  " Y 1 σ2

φ IRIt−x

0

2tβ2

σ2

(20)

IRFs

IRFs were estimated using equations (12) and (13):

Romer and Romer (2004):

Yit=γ0+

h

X

j=0

γj1t−j+ h

X

k=0

γk2t−k +ςit (12)

Jorda (2005):

Yit+s =ηs0+η

s

(21)

Table: Tobit Estimation: FXIt =max[0,x10tβ1+vt] +1t

Var / Specification (x10tβ1) (1) (2) (3)

FXIt−1 0.51*** 0.36*** 0.35***

(0.058) (0.056) (0.058)

et−1−Forecast(¯et−1) -2.36** -4.63*** -6.69***

(1.017) (1.078) (1.410)

Forecast(πt−1)−Target(πt−1) -7.41 -4.45 -5.81

(7.965) (8.121) (8.045)

yt−1−Forecast(¯yt−1) -40.8*** -66.0*** -47.4***

(11.901) (12.322) (13.390)

DCapitalControls -164.8*** -164.6***

(19.140) (20.192)

∆Rest−1 -0.3

(22)

Table: OLS Estimation: ∆IRIt=x20tβ2+2t

Var / Specification (x20tβ2) (1) (2) (3) (4)

∆IRIt−1 0.36*** 0.19*

(0.093) (0.110)

IRIt−1 -0.06*** -0.07***

(0.015) (0.012)

et−1−Forecast(¯et−1) 0.00 0.01***

(0.006) (0.005)

Forecast(πt−1)−Target(πt−1) 0.07***

(0.023)

yt−1−Forecast(¯yt−1) 0.08*** 0.08***

(0.025) (0.015)

∆it1−year1 0.63*** 0.76***

(23)

Table: Covariances of Bivariate Process

Specification x1t(1) x1t(2) x1t(3)

x2t(1) -0.04 -0.04 -0.03

(0.058) (0.061) (0.128)

x2t(2) -0.02 -0.04 -0.09

(0.054) (0.058) (0.215)

x2t(3) -0.04 -0.03 -0.04

(0.058) (0.061) (0.125)

x2t(4) -0.03 -0.05 -0.11

(0.053) (0.055) (0.177)

FXIt = max[0, x

0

(24)

Figure: Observed Policy Instruments vs Policy Shocks: 1999-2012

(25)

Figure: Implied IRFs of Inflation

(

π

t

target(

π

t

))

−3 −1.5 0 1.5 3 %

0 5 10 15 20 25

periods after shock

delta IRI CI upper CI lower

(a) Response to a 1% change in ∆IRI

−3 −1.5 0 1.5 3 %

0 5 10 15 20 25

periods after shock

OLS residual CI upper CI lower

(26)

Summary FXI Shocks (1t) IRI Shocks (2t)

Inflation 0 − (≥1 year)

Industrial Production 0 − (months: 10-12)

Aggregate Demand 0 − (months: 11-15)

Exchange Rate 0 − (≤15 days)

Exchange Rate Volatility − (≤16 days) + (≤11 days)

(27)

Conclusions

Empirical anomalies, such as the price puzzle, are eliminated when accounting for the systematic responses of policy

FXI are not effective for depreciating domestic currency but they do have a small effect on reducing exchange rate volatility

A 1% increase in the intervention interest rate raises the 1-year Treasury bond’s yield by up to 0.25%

Policy positively impacts different maturity rates

(28)

Conclusions

Chapter 2

Great Expectations? Evidence from Colombia’s Exchange rate Survey

(29)

Conclusions

Main Findings

Short term expectations outperform a random walk process and do not exhibit a risk premium

(30)

Conclusions

Motivation

Internal discussion within the board of directors

How accurate were exchange rate expectations? Should they be included in internal forecasts/models?

Most theoretical models agree that expectations play a central role in the determination of the exchange rate.

(31)

Conclusions

Motivation

Most models in the international finance literature assume:

Rational Expectations

No time-varying risk premium

Homogeneous expectations

We find that these assumptions do not hold for the Colombian case

(32)

Conclusions

Data

We use the Central Bank Expectations Survey (Oct 2003-Aug 2012)

Conducted monthly to banks, stockbrokers and pension funds

(Traders and Analysts)

Survey asks for1-month,end-of-year, and1-year ahead exchange rate

Unbalanced panel with 4,389 observations for each forecast

15 establishments answered survey in 85% of cases 41 establishments answered survey in 50% of cases

(33)

Conclusions

(34)

Conclusions

Table: Accuracy of 1-month and 1-year forecasts

Entity Median Direction ∆St Direction ∆St +/−50 pesos +/−50 pesos

1-month 1-year 1-month 1-year

Banks 15 66% 35% 64% 9%

Stockbrokers 19 65% 43% 61% 15%

(35)

Conclusions Stabilizing Expectations

Deriving the Risk Premium

Ftt+k−St = it−ρembit−i ∗

t (CIP)

Et[St+k]−St = it−ρembit−i ∗

t −rpt (UIP)

Ftt+k−St = Et[St+k]−St+rpt (CIP+UIP)

= (Et[St+k]−St+k) + (St+k−St) +rpt

(36)

Conclusions Stabilizing Expectations

Estimations

For Panel regressions, we considered:

Fixed and random effects

To control for characteristics that could be correlated with the probability of an agent’s participation in the survey

Seemingly Unrelated Regressions

(37)

Conclusions Stabilizing Expectations

Efficient Market Hypothesis (EMH)

EMH can fail due to 1) a risk premium or 2) failure of rational expectations

Et[Si,t+k]−St=β0+β1(Ftt+k−St) +it

Table: Existence of Risk Premium

Variable 1-month 1-year

β0 -.006*** .012*** (0.000) (0.003)

β1 1.05*** 0.63***

(0.038) (0.030)

t:β1= 1 2.34 139.6*** (0.126) (0.000)

Et[Si,t+k]−St=α0+α1(St+k−St) +νit

Table: Rational Expectations

Variable 1-month 1-year

α0 -.005*** .04***

(0.000) (0.003)

α1 0.26*** 0.07***

(0.009) (0.008)

(38)

Conclusions Stabilizing Expectations

Orthogonality Condition

Are Agents capturing the impact of news and fundamentals?

Table: Et[Si,t+k]−St+k=xt0β+ηit

Fundamental 1-month 1-year

Board Meetingst -.012 0.31*** (0.014) (0.049)

Intervention Interest Ratet 0.00 0.003 (0.002) (0.007)

Ftt+k−St -0.52*** 0.24*** (0.062) (0.066)

St−St−k -0.08*** 0.044*** (0.015) (0.018)

(39)

Conclusions Stabilizing Expectations

Stabilizing Expectations

“Anticipatory purchases of foreign exchange tend to hasten the anticipated fall in the exchange value... and the actual fall may set up expectations of a further fall”

-Nurske 1944

Expectations can be seen as a linear combination of the spot rateSt and some

variablext

Et[St+k] =βxt+ (1−β)St

Candidates forxt used in the literature:

Past rate St−k → extrapolative

Expected past rateEt−k[St] → adaptive

(40)

Conclusions Stabilizing Expectations

Stabilizing Expectations

Table: Stabilizing Expectations

Type of Expectation 1-month 1-year

Extrapolative

Et[Si,t+k]−St=β0+β1(St−St−k) +t β1= -0.03** β1= -0.14*** (0.013) (0.015) Adaptive

Et[Si,t+k]−St=α0+α1(St−Et−k[St]) +νt α1= -0.07*** α1= -0.17*** (0.015) (0.017) Regressive

(41)

Conclusions

Out-of-Sample Forecasts

We set forth 5 competing strategies against a random walk process

In-Sample: Oct 2003 - May 2005

Used Rolling regressions to re-estimate parameters every forecast period 1-period out-of-sample forecasts were computed for each strategy

Constructed Mean Squared Prediction Errors (MSPEs)

MSPE =

N−1

P

i=0

[(E[St+k+ˆi]−St+i)−(E[St+k+i]−St+i)]2

N

Constructed 2 MSPE statistics: MSPEmodels

MSPERW

and(MSPERW−MSPEmodel)

(42)

Conclusions

Out-of-Sample Forecasts

Table: (MSPERW−MSPEmodel)

Strategy 1-month 1-year

Extrapolative -0.0006 0.18*** (0.001) (0.042)

Adaptive -0.0004 0.20***

(0.001) (0.045)

Regressive 0.003*** 0.09***

(0.001) (0.030)

Ftt+k−St 0.003** 0.03** (0.002) (0.016)

(43)

Conclusions

Conclusions

Revaluations were generally followed by expectations of further revaluation in the short run, but by expectations of devaluations in the long run

Financial establishments behaved poorly in terms of forecasting the direction and the level of the exchange rate

The Efficient Market Hypothesis does not hold for the Colombian case

Failure of rational expectations (short and long run) and existence of a risk premium (long run)

(44)

Chapter 3

The Impact of Foreign Exchange Intervention in Colombia: An Event Study Approach

(45)

Main Findings

Rule-based FX interventions was the only successful mechanism

(46)

Motivation

To date, there is still great controversy as to which exchange rate model should be used when measuring the effects of policy

It is important to understand the effects of policy without imposing parametric assumptions

(47)
(48)

Limitations

Subjectivity when choosing the window size of events

Large windows over-smooth density of the underlying data structure Small bandwidths reduce bias but increase variance

(49)

Event Window

Pre (-) and Post (+) Events

2, 5, 10 and 15 day windows

Event (Cluster of USD Purchases/Sales) Begins when central bank intervenes

Ends when central bank stops interventions for 2, 5, 10 or 15 consecutive days

For Example

Day 1 2 3 4 5 6 7 8 9 10

Intervention X X X X

(50)

Success Criteria

4 criteria for a successful Intervention-Frankel (1994), Fatum et al. (2001), and Humpage (1996)

Criteria Pre-Event Event Post-Event

Direction USD Purchases

+

∆St> 0

Reversal

∆St< 0 USD Purchases

+

∆St> 0

Smoothing

∆St< 0 USD Purchases

+

∆St> −

∆St

Matching USD Purchases

+

∆St> −

(51)

Table: Direction Criteria (5-day event)

FX Intervention Favorable cases H0:p≤0.5

(p-value)

Discretionary (Spot market)

USD Purchases 6/11 (0.27)

Discretionary (Options)

USD Purchases 11/19 (0.18)

Rules-Based (Options)

USD Purchases 7/11 (0.11)

USD Sales 7/9 (0.02)**

(52)

Table: Reversal Criteria (5-day event)

FX Intervention Favorable cases H0:p≤0.5

(p-value)

Discretionary (Spot market)

USD Purchases 5/11 (0.50)

Discretionary (Options)

USD Purchases 6/19 (0.92)

Rules-Based (Options)

USD Purchases 7/11 (0.11)

USD Sales 7/9 (0.02)**

(53)

Table: Smoothing Criteria (5-day event)

FX Intervention Favorable cases H0:p≤0.5 H0:p≤0.8

(p-value) (p-value)

Discretionary (Spot market)

USD Purchases 8/11 (0.03)** (0.62)

Discretionary (Options)

USD Purchases 12/19 (0.08)* 1 (0.93)

Rules-Based (Options)

USD Purchases 10/11 (0.00)***1 (0.09)*

USD Sales 9/9 (0.00)***1 (0.00)***

(54)

Table: Matching Criteria (5-day event)

FX Intervention Average Difference H0: +

∆St>

− ∆St

(p-value)

Discretionary (Spot market)

USD Purchases 0.06 (0.42)

Discretionary (Options)

USD Purchases 0.05 (0.39)

Rules-Based (Options)

USD Purchases 1.08 (0.11)

USD Sales -0.72 (0.02)**

(55)

Conclusions

Rule-based options were successful according to all criteria

All intervention mechanism were successful according to the smoothing criterion

However, Brazil’s counterfactual exercise casts doubts on Discretionary options

(56)

Referencias

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