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Discussion“The Changing Relationship Between Commodity Prices and Prices
of Other Assets with Global Market Integration”by Barbara Rossi
Domenico GiannoneUniversite libre de Bruxelles, ECARES and CEPR
IMF-TMB ConferencePolicy Responses to Commodity Price Movements
Istanbul, April 2012
1 / 32
Once upon a time...
American Economic Association meetings, 2009
2 / 32
Once upon a time...
American Economic Association meetings, 2009
2 / 32
Once upon a time...
Discussion of Can
exchange rates forecast
commodity prices? Hélène Rey
London Business School, CEPR,
NBER
3 / 32
Once upon a time...
Intriguing result
! Exchange rates of small commodity exporters
have forecasting power for global commodity
index. True in and out of sample.
! Reverse is not true, i.e. commodity prices do not
seem to forecast exchange rates very well (and
not out of sample).
4 / 32
Once upon a time...
Conclusions
! Very interesting set of results
! Calls for extensions
! Stock market index has also some forecast ability
in and out of sample.
! Seems a slightly noisier predictor than the
exchange rate.
5 / 32
The Data
95−Q1 00−Q1 05−Q1 10−Q14
4.5
5
5.5(log) Global Commodity Price
95−Q1 00−Q1 05−Q1 10−Q1100
150
200
250
300
350(log) NZ Equity Price
6 / 32
The Sample
95−Q1 00−Q1 05−Q1 10−Q14
4.5
5
5.5(log) Global Commodity Price
95−Q1 00−Q1 05−Q1 10−Q1100
150
200
250
300
350(log) NZ Equity Price
7 / 32
Forecasting Global Commodity Prices (CP)
Forecasting Global Commodity Prices (CP) using asset prices ofsmall commodity producers (e.g. New Zealand (NZ))- Exchange Rate (EXR)- Equity Prices (EP)
This paper (EP)
Et∆CPt+1 = α + β∆EPNZt + ρ∆CPt
Barbara’s previous work ( EXR):
Et∆CPt+1 = α + β∆EXRNZt + ρ∆CPt
The Naive Benchmark: Random Walk
Et∆CPt+1 = 0
8 / 32
Forecasting Global Commodity Prices (CP)
Forecasting Global Commodity Prices (CP) using asset prices ofsmall commodity producers (e.g. New Zealand (NZ))- Exchange Rate (EXR)- Equity Prices (EP)
This paper (EP)
Et∆CPt+1 = α + β∆EPNZt + ρ∆CPt
Barbara’s previous work ( EXR):
Et∆CPt+1 = α + β∆EXRNZt + ρ∆CPt
The Naive Benchmark: Random Walk
Et∆CPt+1 = 0
8 / 32
Forecasting Global Commodity Prices (CP)
Forecasting Global Commodity Prices (CP) using asset prices ofsmall commodity producers (e.g. New Zealand (NZ))- Exchange Rate (EXR)- Equity Prices (EP)
This paper (EP)
Et∆CPt+1 = α + β∆EPNZt + ρ∆CPt
Barbara’s previous work ( EXR):
Et∆CPt+1 = α + β∆EXRNZt + ρ∆CPt
The Naive Benchmark: Random Walk
Et∆CPt+1 = 0
8 / 32
Forecasting Global Commodity Prices (CP)
Forecasting Global Commodity Prices (CP) using asset prices ofsmall commodity producers (e.g. New Zealand (NZ))- Exchange Rate (EXR)- Equity Prices (EP)
This paper (EP)
Et∆CPt+1 = α + β∆EPNZt + ρ∆CPt
Barbara’s previous work ( EXR):
Et∆CPt+1 = α + β∆EXRNZt + ρ∆CPt
The Naive Benchmark: Random Walk
Et∆CPt+1 = 0
8 / 32
The Input
95−Q1 00−Q1 05−Q1 10−Q1
−10
−5
0
5
10
Commodity Price
Equity Price
9 / 32
The Output
04−Q1 06−Q1 08−Q1 10−Q1−5
0
5
10
15
Model Forecast
Commodity Price
Random Walk
10 / 32
The Output
04−Q1 06−Q1 08−Q1 10−Q1−5
0
5
10
15
Model Forecast
Commodity Price
Random Walk
11 / 32
The Evaluation: Quadratic Loss
04−Q1 06−Q1 08−Q1 10−Q1−5
0
5
10
15
Model Forecast
Commodity Price
Random Walk
04−Q1 06−Q1 08−Q1 10−Q10
20
40
60
80
100
120Squared Errors
Model
Random Walk
12 / 32
The Evaluation: Fluctuation Test
04−Q1 06−Q1 08−Q1 10−Q1−5
0
5
10
15
Model Forecast
Commodity Price
Random Walk
04−Q1 06−Q1 08−Q1 10−Q10
20
40
60
80
100
120Squared Errors
Model
Random Walk
Smooth Mod.
Smooth RW
13 / 32
The empirical finding
[...] the appearance of the out-of-sample predictive ability of theequity market predictor [...] dated around mid-2000.
Since the mid-2000s marked a large increase in investment incommodity markets, ...
... our empirical evidence suggests a decrease in marketsegmentation at approximately the same time, ...
... and therefore the possibility that shocks in equity markets mighthave started to spill-over onto commodity markets in that period.
14 / 32
The empirical finding
[...] the appearance of the out-of-sample predictive ability of theequity market predictor [...] dated around mid-2000.
Since the mid-2000s marked a large increase in investment incommodity markets, ...
... our empirical evidence suggests a decrease in marketsegmentation at approximately the same time, ...
... and therefore the possibility that shocks in equity markets mighthave started to spill-over onto commodity markets in that period.
14 / 32
The empirical finding
[...] the appearance of the out-of-sample predictive ability of theequity market predictor [...] dated around mid-2000.
Since the mid-2000s marked a large increase in investment incommodity markets, ...
... our empirical evidence suggests a decrease in marketsegmentation at approximately the same time, ...
... and therefore the possibility that shocks in equity markets mighthave started to spill-over onto commodity markets in that period.
14 / 32
The empirical finding
[...] the appearance of the out-of-sample predictive ability of theequity market predictor [...] dated around mid-2000.
Since the mid-2000s marked a large increase in investment incommodity markets, ...
... our empirical evidence suggests a decrease in marketsegmentation at approximately the same time, ...
... and therefore the possibility that shocks in equity markets mighthave started to spill-over onto commodity markets in that period.
14 / 32
Other Predictors
00−Q1 10−Q14
4.5
5
5.5(log) Global Commodity Price
00−Q1 10−Q1100
150
200
250
300
350(log) NZ Equity Price
00−Q1 10−Q1−1
−0.8
−0.6
−0.4
−0.2
0(log) Exch. Rate
00−Q1 10−Q14.3
4.4
4.5
4.6
4.7
4.8
4.9
5(log) Global IP
15 / 32
Other Predictors
04−Q1 06−Q1 08−Q1 10−Q10
0.5
1
1.5
2
2.5
3
Equity Price
16 / 32
Other Predictors
04−Q1 06−Q1 08−Q1 10−Q10
0.5
1
1.5
2
2.5
3
Equity Price
Exch. Rate
17 / 32
Other Predictors
04−Q1 06−Q1 08−Q1 10−Q10
0.5
1
1.5
2
2.5
3
Equity Price
Exch. Rate
Global IP
18 / 32
Other Predictors
04−Q1 06−Q1 08−Q1 10−Q10
0.5
1
1.5
2
2.5
3
Equity Price
Exch. Rate
Global IP
Pool
19 / 32
Summing up
The appearance of predictive ability is not specific to the theequity market predictor!!
Factors other that the decrease in market segmentation andspillovers from equity markets might have been at work!
20 / 32
Forecasting Commodity Prices at the Time of the GreatRecession
00−Q1 10−Q14
4.5
5
5.5(log) Global Commodity Price
00−Q1 10−Q1100
150
200
250
300
350(log) NZ Equity Price
00−Q1 10−Q1−1
−0.8
−0.6
−0.4
−0.2
0(log) Exch. Rate
00−Q1 10−Q14.3
4.4
4.5
4.6
4.7
4.8
4.9
5(log) Global IP
21 / 32
Forecasting Commodity Prices at the Time of the GreatRecession
00−Q1 10−Q14
4.5
5
5.5(log) Global Commodity Price
00−Q1 10−Q1100
150
200
250
300
350(log) NZ Equity Price
00−Q1 10−Q1−1
−0.8
−0.6
−0.4
−0.2
0(log) Exch. Rate
00−Q1 10−Q14.3
4.4
4.5
4.6
4.7
4.8
4.9
5(log) Global IP
22 / 32
Commodity and Equity Prices at the Time of the GreatRecession
95−Q1 00−Q1 05−Q1 10−Q1
−30
−25
−20
−15
−10
−5
0
5
10
15
Commodity Price
Equity Price
23 / 32
Forecasting Commodity Prices at the Time of the GreatRecession
04−Q1 06−Q1 08−Q1 10−Q10
0.5
1
1.5
2
2.5
3
Equity Price
Exch. Rate
Global IP
Pool
24 / 32
Forecasting Commodity Prices at the Time of the GreatRecession
04−Q1 06−Q1 08−Q1 10−Q10
0.5
1
1.5
2
2.5
3
Equity Price
Exch. Rate
Global IP
Pool
25 / 32
Forecasting Commodity Prices at the Time of the GreatRecession
04−Q1 06−Q1 08−Q1 10−Q10
0.5
1
1.5
2
2.5
3
Equity Price
Exch. Rate
Global IP
Pool
26 / 32
Can we really forecast commodity prices??
27 / 32
An Alternative Look at the Data:Modeling Structural Change
Model: Time-Varying Vector Autoregressions (TV-VAR)Cogley and Sargent, 2001, 2005, Primiceri, 2006
yt = A0,t + A1,tyt−1 + εt , εt ∼ N(0,Σt) (1)
Parameters evolve according to
Coefficients : θt = θt−1 + ωt , ωt ∼ N(0,Ω) (2)
Variances : log σt = log σt−1 + ξt , ξt ∼ N(0,Ξ) (3)
Autocovariance : φi ,t = φi ,t−1 + ψi ,t , ψi ,t ∼ N(0,Ψi ) (4)
ψi ,t , ξt , ωt , εt all mutually uncorrelated at all leads and lags.
Reliable Forecasting Tool: D’Agostino, Gambetti and Giannone, 2009.
⇒ Flexible but parsimonious model of structural changes
28 / 32
An Alternative Look at the Data:Modeling Structural Change
Model: Time-Varying Vector Autoregressions (TV-VAR)Cogley and Sargent, 2001, 2005, Primiceri, 2006
yt = A0,t + A1,tyt−1 + εt , εt ∼ N(0,Σt) (1)
Parameters evolve according to
Coefficients : θt = θt−1 + ωt , ωt ∼ N(0,Ω) (2)
Variances : log σt = log σt−1 + ξt , ξt ∼ N(0,Ξ) (3)
Autocovariance : φi ,t = φi ,t−1 + ψi ,t , ψi ,t ∼ N(0,Ψi ) (4)
ψi ,t , ξt , ωt , εt all mutually uncorrelated at all leads and lags.
Reliable Forecasting Tool: D’Agostino, Gambetti and Giannone, 2009.
⇒ Flexible but parsimonious model of structural changes
28 / 32
An Alternative Look at the Data:Modeling Structural Change
Setting the Prior
yt = A0,t + A1,tyt−1 + εt , εt ∼ N(0,Σt) (5)
Parameters evolve according to
θt = θt−1 + ωt , ωt ∼ N(0,Ω) (6)
Estimate the model by ML over the training sample: 1992-2001 (θ)
θ0 = θ V (θ0) = V (θ)
Ω = V (θt − θt−1) = V (θ)× λ2
Quite loose prior to favor time variation:λ2 = 1
10
Estimation: Gibbs Sampling on entire sample
29 / 32
An Alternative Look at the Data:Time Varying Coefficients
Q1−00 Q1−05 Q1−10 Q1−150
0.1
0.2
0.3
0.4
0.5Commodity to Commodity
Q1−00 Q1−05 Q1−10 Q1−15−0.1
−0.05
0
0.05
0.1
0.15Equity to Commodity
Q1−00 Q1−05 Q1−10 Q1−15−0.5
0
0.5
1Commodity to Equity
Q1−00 Q1−05 Q1−10 Q1−15−0.2
−0.1
0
0.1
0.2
0.3Equity to Equity
If anything, Commodity Prices are becoming Less Predictable30 / 32
An Alternative Look at the Data:Stochastic Volatility
Q1−02 Q1−04 Q1−06 Q1−08 Q1−10 Q1−120
50
100
150
200Variance of Commodity Price Innovations
Q1−02 Q1−04 Q1−06 Q1−08 Q1−10 Q1−120
100
200
300
400
500
600Variance of Equity Price Innovations
Strong comovement in volatility: might be exploited in riskassessment 31 / 32
Conclusions
Fascinating, relevant and innovative research agenda
Vey interesting and well executed paper.
I strongly advice you to read it!!
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