Common shocks in stocks and bonds - Wild Apricot · 2020. 10. 5. · Common shocks in stocks and...
Transcript of Common shocks in stocks and bonds - Wild Apricot · 2020. 10. 5. · Common shocks in stocks and...
Anna Cieslak and Hao Pang (Duke Fuqua) 1
Q Group Fall Seminar on Zoom
Common shocks in stocks and bonds
Anna Cieslak
Duke University, Fuqua School of Business, NBER, and CEPR
Hao Pang
Duke University, Fuqua School of Business
October 19, 2020
Motivation
Anna Cieslak and Hao Pang (Duke Fuqua) 2
� Interest in how the Fed affects financial markets (and the economy)
Motivation
Anna Cieslak and Hao Pang (Duke Fuqua) 2
� Interest in how the Fed affects financial markets (and the economy)
� Estimated effects of the Fed on financial markets are large
� But some open questions remain
– Mechanism: Short-term rate, risk premia, expectations about the economy?– Consistency across asset classes: Stocks vs. bonds?
Motivation
Anna Cieslak and Hao Pang (Duke Fuqua) 2
� Interest in how the Fed affects financial markets (and the economy)
� Estimated effects of the Fed on financial markets are large
� But some open questions remain
– Mechanism: Short-term rate, risk premia, expectations about the economy?– Consistency across asset classes: Stocks vs. bonds?
→ Need a methodology to jointly identify the different economic shocks induced by the Fed
Motivation
Anna Cieslak and Hao Pang (Duke Fuqua) 2
� Interest in how the Fed affects financial markets (and the economy)
� Estimated effects of the Fed on financial markets are large
� But some open questions remain
– Mechanism: Short-term rate, risk premia, expectations about the economy?– Consistency across asset classes: Stocks vs. bonds?
→ Need a methodology to jointly identify the different economic shocks induced by the Fed
→ Yield curve to help
This paper
Anna Cieslak and Hao Pang (Duke Fuqua) 3
� Approach to identify economic shocks from asset prices alone by combining
– Finance view: discount-rate and cash-flow news (Campbell, Shiller, 1988)– Macro view: structural disturbances (Sims, 1980)
This paper
Anna Cieslak and Hao Pang (Duke Fuqua) 3
� Approach to identify economic shocks from asset prices alone by combining
– Finance view: discount-rate and cash-flow news (Campbell, Shiller, 1988)– Macro view: structural disturbances (Sims, 1980)
� Decompose daily innovations in stock returns and yield changes into orthogonal sourcesof news
ω =
ωg growth news (cash-flow risk)
ωm monetary news (pure discount-rate risk via short rate)
ωp+ hedging premium news (compensation for cash-flow risk)
ωp− common premium news (compensation for discount-rate risk)
This paper
Anna Cieslak and Hao Pang (Duke Fuqua) 3
� Approach to identify economic shocks from asset prices alone by combining
– Finance view: discount-rate and cash-flow news (Campbell, Shiller, 1988)– Macro view: structural disturbances (Sims, 1980)
� Decompose daily innovations in stock returns and yield changes into orthogonal sourcesof news
ω =
ωg growth news (cash-flow risk)
ωm monetary news (pure discount-rate risk via short rate)
ωp+ hedging premium news (compensation for cash-flow risk)
ωp− common premium news (compensation for discount-rate risk)
� Importance of two sources of risk-premium shocks
Applications
Anna Cieslak and Hao Pang (Duke Fuqua) 4
� Assess overall impact of different economic shocks on stocks and yields (on all days)6= event studies; e.g., monetary news comes out on a continuous basis, not just in event windows ...
Applications
Anna Cieslak and Hao Pang (Duke Fuqua) 4
� Assess overall impact of different economic shocks on stocks and yields (on all days)6= event studies; e.g., monetary news comes out on a continuous basis, not just in event windows ...
� Dissect news content of Fed and macro announcements... and even on “event” days news is not one-dimensional
Applications
Anna Cieslak and Hao Pang (Duke Fuqua) 4
� Assess overall impact of different economic shocks on stocks and yields (on all days)6= event studies; e.g., monetary news comes out on a continuous basis, not just in event windows ...
� Dissect news content of Fed and macro announcements... and even on “event” days news is not one-dimensional
� Interpret shocks to asset prices day-by-day during the Covid-19 crisis
Structural VAR interpretation of asset pricing models
Anna Cieslak and Hao Pang (Duke Fuqua) 5
� Models commonly express transformed asset prices Yt (bond yields, log pd ratio) as≈affinefunctions of state variables Ft
Yt = a+ AFt rank(A) = dim(Ft)
Ft = µF + ΦFt−1 + ΣFωt Var(ωt) = I and ΣF diagonal
Ft: beliefs about growth, monetary policy, uncertainty, time-varying risk aversion, ...
Structural VAR interpretation of asset pricing models
Anna Cieslak and Hao Pang (Duke Fuqua) 5
� Models commonly express transformed asset prices Yt (bond yields, log pd ratio) as≈affinefunctions of state variables Ft
Yt = a+ AFt
Ft = µF + ΦFt−1 + ΣFωt ←structural form
Ft: beliefs about growth, monetary policy, uncertainty, time-varying risk aversion, ...
� We usually do not observe Ft directly, but we observe Yt
Yt = µY +ΨYt−1 + ut ←reduced form
Structural VAR interpretation of asset pricing models
Anna Cieslak and Hao Pang (Duke Fuqua) 5
� Models commonly express transformed asset prices Yt (bond yields, log pd ratio) as≈affinefunctions of state variables Ft
Yt = a+ A Ft
Ft = µF + ΦFt−1 + ΣFωt ←structural form
Ft: beliefs about growth, monetary policy, uncertainty, time-varying risk aversion, ...
� We usually do not observe Ft directly, but we observe Yt
Yt = µY +ΨYt−1 + ut ←reduced form
... and we have theory-based predictions about structural loadings A
Structural VAR interpretation of asset pricing models
Anna Cieslak and Hao Pang (Duke Fuqua) 5
� Models commonly express transformed asset prices Yt (bond yields, log pd ratio) as≈affinefunctions of state variables Ft
Yt = a+ A Ft
Ft = µF + ΦFt−1 + ΣFωt ←structural form
Ft: beliefs about growth, monetary policy, uncertainty, time-varying risk aversion, ...
� We usually do not observe Ft directly, but we observe Yt
Yt = µY +ΨYt−1 + ut ←reduced form
... and we have theory-based predictions about structural loadings A
→ Back out structural shocks ωt from ut and restrictions on A
ωt︸︷︷︸
structural
= A−1
ut︸︷︷︸
reduced
with A = AΣF . (1)
Structural VAR interpretation of asset pricing models
Anna Cieslak and Hao Pang (Duke Fuqua) 5
� Models commonly express transformed asset prices Yt (bond yields, log pd ratio) as≈affinefunctions of state variables Ft
Yt = a+ A Ft
Ft = µF + ΦFt−1 + ΣFωt ←structural form
Ft: beliefs about growth, monetary policy, uncertainty, time-varying risk aversion, ...
� We usually do not observe Ft directly, but we observe Yt
Yt = µY +ΨYt−1 + ut ←reduced form
... and we have theory-based predictions about structural loadings A
→ Back out structural shocks ωt from ut and restrictions on A
ωt︸︷︷︸
structural
= A−1
ut︸︷︷︸
reduced
with A = AΣF . (1)
Caveats: orthogonality, micro-foundations, stochastic volatility
Literature
Anna Cieslak and Hao Pang (Duke Fuqua) 6
Term-structure models. Cochrane, Piazzesi (2005); Bansal, Shaliastovich (2013); Green-wood, Vayanos (2013); Cieslak, Povala (2015, 2016); Duffee (2018)
Stock-bond comovement. Connolly, Stivers, Sun (2005); Andersen, Bollerslev, Diebold,Vega (2007); Bekaert, Engstrom, Xing (2008); Baele, Bekaert, Inghelbrech (2010); Lettau,Wachter (2011); David, Veronesi (2013); Campbell, Sunderam, Viceira (2017); Campbell,Pflueger, Viceira (2019); Koijen, Lustig, Van Nieuwerburgh (2017); Song (2017); Pfluegerand Rinaldi (2020)
Identification of monetary surprises. Rigobon, Sack (2004); Gurkaynak, Sack, Swanson(2005); Bernanke, Kuttner (2005); Campbell, Evans, Fisher, Justiniano (2012); Hanson, Stein(2015); Nakamura, Steinsson (2018); Swanson (2017); Cieslak, Schrimpf (2019); Jarocinski,Karadi (2019); Miranda-Agrippino, Ricco (2019)
Structural VARs. Faust (1998); Faust, Swanson, Wright (2004); Uhlig (2005); Rubio-Ramirez, Waggoner, Zha (2010); Arias, Caldara, Rubio-Ramirez (2019); Ludvigson, Ma, Ng(2019)
Identification idea
Anna Cieslak and Hao Pang (Duke Fuqua) 7
� Use the entire yield curve and stocks jointly to identify the loadings matrix A
Identification idea
Anna Cieslak and Hao Pang (Duke Fuqua) 7
� Use the entire yield curve and stocks jointly to identify the loadings matrix A
� Impose two sets of restrictions [SDF modely]
Identification idea
Anna Cieslak and Hao Pang (Duke Fuqua) 7
� Use the entire yield curve and stocks jointly to identify the loadings matrix A
� Impose two sets of restrictions [SDF modely]
i. Monotonicity restrictions across yield maturities
Identification idea
Anna Cieslak and Hao Pang (Duke Fuqua) 7
� Use the entire yield curve and stocks jointly to identify the loadings matrix A
� Impose two sets of restrictions [SDF modely]
i. Monotonicity restrictions across yield maturities
ii. Sign restrictions between stocks and yields
Identification idea
Anna Cieslak and Hao Pang (Duke Fuqua) 7
� Use the entire yield curve and stocks jointly to identify the loadings matrix A
� Impose two sets of restrictions [SDF modely]
i. Monotonicity restrictions across yield maturities
ii. Sign restrictions between stocks and yields
� Note: We focus on the comovement between stock returns and yield changes(+) stock-yield comovement ←→ (−) stock-bond return comovement
Identification: Monotonicity restrictions across yield maturities
Anna Cieslak and Hao Pang (Duke Fuqua) 8
[SDF model: Yieldsy]
� Impact of short-rate expectations shocks decays with maturityBlanchard-Quah-type long-run restriction in the cross-section of yields (here: not a zero restriction)
Identification: Monotonicity restrictions across yield maturities
Anna Cieslak and Hao Pang (Duke Fuqua) 8
[SDF model: Yieldsy]
� Impact of short-rate expectations shocks decays with maturityBlanchard-Quah-type long-run restriction in the cross-section of yields (here: not a zero restriction)
� Impact of risk-premium shocks increases with maturityBansal and Shaliastovich (2013) (two factors: real and nominal uncertainty); Greenwood and Vayanos (2013)
(bond supply); Hanson and Stein (2015) (demand of reaching-for-yield investors)
� Empirical magnitudes: Impact of 1σ risk-premium shock ∼2x larger on 10y than 2y yieldCieslak and Povala (2015, 2016) y
Identification: Monotonicity restrictions across yield maturities
Anna Cieslak and Hao Pang (Duke Fuqua) 8
[SDF model: Yieldsy]
� Impact of short-rate expectations shocks decays with maturityBlanchard-Quah-type long-run restriction in the cross-section of yields (here: not a zero restriction)
� Impact of risk-premium shocks increases with maturityBansal and Shaliastovich (2013) (two factors: real and nominal uncertainty); Greenwood and Vayanos (2013)
(bond supply); Hanson and Stein (2015) (demand of reaching-for-yield investors)
� Empirical magnitudes: Impact of 1σ risk-premium shock ∼2x larger on 10y than 2y yieldCieslak and Povala (2015, 2016) y
Identification: Sign restriction between stocks and yields
Anna Cieslak and Hao Pang (Duke Fuqua) 9
ShocksShort-rate expectations Risk premium
growth monetary hedging commonωg ↑ ωm ↑ ωp+ ↑ ωp− ↑
Yield changes (+) (+) (−) (+)Stock returns (+) (−) (−) (−)Stock-yield comovement (+) (−) (+) (−)
[SDF model: Comovementy]
Identification: Sign restriction between stocks and yields
Anna Cieslak and Hao Pang (Duke Fuqua) 9
ShocksShort-rate expectations Risk premium
growth monetary hedging commonωg ↑ ωm ↑ ωp+ ↑ ωp− ↑
Yield changes (+) (+) (−) (+)Stock returns (+) (−) (−) (−)Stock-yield comovement (+) (−) (+) (−)
[SDF model: Comovementy]
� Growth news ωg ↑ raises yields and stock pricesLRR-type model: IES > 1; Taylor rule: Fed tightens less than 1-for-1 with growth news y
Identification: Sign restriction between stocks and yields
Anna Cieslak and Hao Pang (Duke Fuqua) 9
ShocksShort-rate expectations Risk premium
growth monetary hedging commonωg ↑ ωm ↑ ωp+ ↑ ωp− ↑
Yield changes (+) (+) (−) (+)Stock returns (+) (−) (−) (−)Stock-yield comovement (+) (−) (+) (−)
[SDF model: Comovementy]
� Growth news ωg ↑ raises yields and stock pricesLRR-type model: IES > 1; Taylor rule: Fed tightens less than 1-for-1 with growth news y
� Monetary news ωm ↑ lowers stock prices, raises yieldsNK channel: real rate gap ↑; discount-rate effect via risk-free rate
Identification: Sign restriction between stocks and yields
Anna Cieslak and Hao Pang (Duke Fuqua) 9
ShocksShort-rate expectations Risk premium
growth monetary hedging commonωg ↑ ωm ↑ ωp+ ↑ ωp− ↑
Yield changes (+) (+) (−) (+)Stock returns (+) (−) (−) (−)Stock-yield comovement (+) (−) (+) (−)
[SDF model: Comovementy]
� Growth news ωg ↑ raises yields and stock pricesLRR-type model: IES > 1; Taylor rule: Fed tightens less than 1-for-1 with growth news y
� Monetary news ωm ↑ lowers stock prices, raises yieldsNK channel: real rate gap ↑; discount-rate effect via risk-free rate
� Suppose ωg and ωm shocks are priced and earn time-varying risk premia
– Hedging premium ωp+: bonds hedge growth/cash-flow risk in stocksFlight-to-safety; real/cash-flow uncertainty
Identification: Sign restriction between stocks and yields
Anna Cieslak and Hao Pang (Duke Fuqua) 9
ShocksShort-rate expectations Risk premium
growth monetary hedging commonωg ↑ ωm ↑ ωp+ ↑ ωp− ↑
Yield changes (+) (+) (−) (+)Stock returns (+) (−) (−) (−)Stock-yield comovement (+) (−) (+) (−)
[SDF model: Comovementy]
� Growth news ωg ↑ raises yields and stock pricesLRR-type model: IES > 1; Taylor rule: Fed tightens less than 1-for-1 with growth news y
� Monetary news ωm ↑ lowers stock prices, raises yieldsNK channel: real rate gap ↑; discount-rate effect via risk-free rate
� Suppose ωg and ωm shocks are priced and earn time-varying risk premia
– Hedging premium ωp+: bonds hedge growth/cash-flow risk in stocksFlight-to-safety; real/cash-flow uncertainty
– Common premium ωp−: both stocks and bonds exposed to discount-rate riskMonetary/discount-rate uncertainty
Implementation
Anna Cieslak and Hao Pang (Duke Fuqua) 10
� Estimate VAR(1) in yield changes ∆y(n)t and log stock returns ∆st to obtain reduced-form
shocks ut
zt = ∆Yt = (∆y(2)t ,∆y
(5)t ,∆y
(10)t ,∆st)
′ (2)
– Daily sample 1983–2017 (extended through Covid-19 crisis)
� Recover structural shocks ω =(ωg, ωm, ωp+, ωp−
)′by sign and monotonicity restrictions
on the structural matrix A = AΣF
– Set identification as opposed to point identification
Implementation
Anna Cieslak and Hao Pang (Duke Fuqua) 11
� Start from Cholesky decomposition of the reduced-form covariance matrix
Σu = PP′ and ut = Pω
∗t
– P lower triangular, ω∗t uncorrelated shocks, V ar(w∗
t ) = I
� Simulate rotation matrices Q such that QQ′ = Q′Q = I
ut = PQ′
︸︷︷︸
A(Q)
Qw∗t
︸︷︷︸
ωt(Q)
– Qw∗t is another set of uncorrelated shocks
� Store 1000 Q’s for which A(Q) = PQ′ satisfies restrictions
� Select the median-target (MT) solution with θi = vec(A(Qi))
θMT = min
i
[θi − median(θi)
std(θi)
]′[θi − median(θi)
std(θi)
]
– i.e., pick solution for which on-impact responses to ω are closest to the median of on-impact
responses across solutions
Paths of cumulative shocks
Anna Cieslak and Hao Pang (Duke Fuqua) 12
Validity of identified shocks
Anna Cieslak and Hao Pang (Duke Fuqua) 13
� Growth shocks ωg comove strongly with real GDP growth forecast updates (Blue Chipsurvey) y
Validity of identified shocks
Anna Cieslak and Hao Pang (Duke Fuqua) 13
� Growth shocks ωg comove strongly with real GDP growth forecast updates (Blue Chipsurvey) y
−4
−2
0
2
12
−m
on
th c
um
ula
tiv
e, z
−sc
ore
s
1985 1990 1995 2000 2005 2010 2015
Growth shocks
Survey real GDP forecast updates (1−qtr ahead)
Validity of identified shocks
Anna Cieslak and Hao Pang (Duke Fuqua) 13
� Growth shocks ωg comove strongly with real GDP growth forecast updates (Blue Chipsurvey) y
� Monetary shocks ωm have significantly higher volatility on scheduled FOMC announce-ment days y
– Note: We do not use any information about the timing of FOMC meetings– Significant relation to monetary surprises in event studies (GSS (2005); Swanson (2017)) y
Validity of identified shocks
Anna Cieslak and Hao Pang (Duke Fuqua) 13
� Growth shocks ωg comove strongly with real GDP growth forecast updates (Blue Chipsurvey) y
� Monetary shocks ωm have significantly higher volatility on scheduled FOMC announce-ment days y
– Note: We do not use any information about the timing of FOMC meetings– Significant relation to monetary surprises in event studies (GSS (2005); Swanson (2017)) y
� Risk-premium shocks ωp+ and ωp− relate with innovations to standard measures of bondand equity risk premium with expected signs
Equity risk premium Bond risk premium
Lettau Kelly Martin Cochane CieslakLudvigson Pruitt Piazzesi Povala
hedging premium, ωp+ ↑ (+) (+) (+) (−) (−)common premium, ωp− ↑ (+) (+) (+) (+) (+)
Validity of identified shocks
Anna Cieslak and Hao Pang (Duke Fuqua) 13
� Growth shocks ωg comove strongly with real GDP growth forecast updates (Blue Chipsurvey) y
� Monetary shocks ωm have significantly higher volatility on scheduled FOMC announce-ment days y
– Note: We do not use any information about the timing of FOMC meetings– Significant relation to monetary surprises in event studies (GSS (2005); Swanson (2017)) y
� Risk-premium shocks ωp+ and ωp− relate with innovations to standard measures of bondand equity risk premium with expected signs
Equity risk premium Bond risk premium
Lettau Kelly Martin Cochane CieslakLudvigson Pruitt Piazzesi Povala
hedging premium, ωp+ ↑ (+) (+) (+) (−) (−)common premium, ωp− ↑ (+) (+) (+) (+) (+)
� Results for expected inflation y and breakeven inflation rates y
Impulse responses
Anna Cieslak and Hao Pang (Duke Fuqua) 14
0 200 400 600-10
-5
0
5
10
15
0 200 400 600-10
-5
0
5
10
15
0 200 400 600-10
-5
0
5
10
15
0 200 400 600-10
-5
0
5
10
15
0 200 400 600-10
-5
0
5
10
15
0 200 400 600-10
-5
0
5
10
15
0 200 400 600-10
-5
0
5
10
15
0 200 400 600-10
-5
0
5
10
15
0 200 400 600-200
-100
0
100
0 200 400 600-200
-100
0
100
0 200 400 600-200
-100
0
100
0 200 400 600-200
-100
0
100
IRFs obtained with Jorda’s (2005) local projections; response to 1σ shock
Variance decompositions: How important are different shocks?
Anna Cieslak and Hao Pang (Duke Fuqua) 15
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
� growth news, ωg� hedging premium news, ωp+
� monetary news, ωm� common premium news, ωp−
� Over 1983–2017 sample, monetary and growth news accounts for 80% of daily variation in 2yyield changes, 40% in stock returns
� Risk-premium news generates 80% of variation in 10y yield changes, 60% in stock returns
Variance decompositions: Pre/post-1998
Anna Cieslak and Hao Pang (Duke Fuqua) 15
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
� growth news, ωg� monetary news, ωm
� Post-1998, monetary and common premium news ↓, growth and hedging premium news ↑
→ Switch in stock-yield comovement from (−) to (+)→ Consistent with Campbell, Pflueger, Viceira (2020)
Variance decompositions: Pre/post-1998
Anna Cieslak and Hao Pang (Duke Fuqua) 15
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
2y yld 5y yld 10y yld stocks0
0.1
0.2
0.3
0.4
0.5
0.6
� hedging premium news, ωp+� common premium news, ωp−
� Post-1998, monetary and common premium news ↓, growth and hedging premium news ↑
→ Switch in stock-yield comovement from (−) to (+)→ Consistent with Campbell, Pflueger, Viceira (2020)
Dissecting news content on Fed announcement days
Anna Cieslak and Hao Pang (Duke Fuqua) 16
� Fed events are a multidimensional information bundle→ Interpretation has been challenging
Dissecting news content on Fed announcement days
Anna Cieslak and Hao Pang (Duke Fuqua) 16
� Fed events are a multidimensional information bundle→ Interpretation has been challenging
� Stocks but not bonds earn high returns on days of scheduled FOMC meetings and atregular intervals over the FOMC cycle
Lucca and Moench (2015); Cieslak, Morse, Vissing-Jorgensen (2019)
Dissecting news content on Fed announcement days
Anna Cieslak and Hao Pang (Duke Fuqua) 16
� Fed events are a multidimensional information bundle→ Interpretation has been challenging
� Stocks but not bonds earn high returns on days of scheduled FOMC meetings and atregular intervals over the FOMC cycle
Lucca and Moench (2015); Cieslak, Morse, Vissing-Jorgensen (2019)
� Channels of Fed transmission:
– Conventional monetary channel : Fed exogenously changes (expected) short rate
– Information channel : Fed reveals information about growth that investors did not haveCampbell, Evans, Fisher, Justiniano (2012); Nakamura and Steinsson (2018)
– Risk-premium channel : Fed influences amount or price of risk perceived by investorsHanson and Stein (2015)
Dissecting news content on Fed announcement days
Anna Cieslak and Hao Pang (Duke Fuqua) 16
� Fed events are a multidimensional information bundle→ Interpretation has been challenging
� Stocks but not bonds earn high returns on days of scheduled FOMC meetings and atregular intervals over the FOMC cycle
Lucca and Moench (2015); Cieslak, Morse, Vissing-Jorgensen (2019)
� Channels of Fed transmission:
– Conventional monetary channel : → ωm
– Information channel : → ωg
Campbell, Evans, Fisher, Justiniano (2012); Nakamura and Steinsson (2018)
– Risk-premium channel : → ωp+, ωp−
Hanson and Stein (2015)
FOMC announcement-day returns
Anna Cieslak and Hao Pang (Duke Fuqua) 17
returnt or return component(ωit) = γ0 + γ11t,FOMC + εt (1994–2017)
−10
0
10
20
30
40
−1.5
−1
−.5
0
.5
−1.5
−1
−.5
0
.5
1
∆y(10) (bps)∆y(2) (bps)∆s (bps)
overalloveralloverall ωgωgωg ωmωm
ωm ωp+ωp+ωp+ ωp−ωp−
ωp− rprprp
ωg : growth news; ωm: monetary news; ωp+: hedging premium news; ωp−: common premium news
FOMC announcement-day returns
Anna Cieslak and Hao Pang (Duke Fuqua) 17
returnt or return component(ωit) = γ0 + γ11t,FOMC + εt (1994–2017)
10
0
10
20
30
40
1.5
1
.5
0
.5
1.5
1
.5
0
.5
1
∆y(10) (bps)∆y(2) (bps)∆s (bps)
overalloveralloverall ωgωgωg ωmωm
ωm ωp+ωp+ωp+ ωp−ωp−
ωp− rprprp
ωg : growth news; ωm: monetary news; ωp+: hedging premium news; ωp−: common premium news
� FOMC-day effect on stock returns is 28bps
FOMC announcement-day returns
Anna Cieslak and Hao Pang (Duke Fuqua) 17
returnt or return component(ωit) = γ0 + γ11t,FOMC + εt (1994–2017)
10
0
10
20
30
40
1.5
1
.5
0
.5
1.5
1
.5
0
.5
1
∆y(10) (bps)∆y(2) (bps)∆s (bps)
overalloveralloverall ωgωgωg ωmωm
ωm ωp+ωp+ωp+ ωp−ωp−
ωp− rprprp
ωg : growth news; ωm: monetary news; ωp+: hedging premium news; ωp−: common premium news
� FOMC-day effect on stock returns is 28bps; insignificant for yield changes at −0.5bps
FOMC announcement-day returns
Anna Cieslak and Hao Pang (Duke Fuqua) 17
returnt or return component(ωit) = γ0 + γ11t,FOMC + εt (1994–2017)
10
0
10
20
30
40
1.5
1
.5
0
.5
1.5
1
.5
0
.5
1
∆y(10) (bps)∆y(2) (bps)∆s (bps)
overalloveralloverall ωgωgωg ωmωm
ωm ωp+ωp+ωp+ ωp−ωp−
ωp− rprprp
ωg : growth news; ωm: monetary news; ωp+: hedging premium news; ωp−: common premium news
� FOMC-day effect on stock returns is 28bps; insignificant for yield changes at −0.5bps
� High stock returns driven by risk-premium news and monetary news
FOMC announcement-day returns
Anna Cieslak and Hao Pang (Duke Fuqua) 17
returnt or return component(ωit) = γ0 + γ11t,FOMC + εt (1994–2017)
10
0
10
20
30
40
1.5
1
.5
0
.5
1.5
1
.5
0
.5
1
∆y(10) (bps)∆y(2) (bps)∆s (bps)
overalloveralloverall ωgωgωg ωmωm
ωm ωp+ωp+ωp+ ωp−ωp−
ωp− rprprp
ωg : growth news; ωm: monetary news; ωp+: hedging premium news; ωp−: common premium news
� FOMC-day effect on stock returns is 28bps; insignificant for yield changes at −0.5bps
� High stock returns driven by risk-premium news and monetary news
� Nearly 70% of high stock returns on FOMC days due to news that reduces risk premium
yAll solutions yCMVJ cycle
FOMC announcement-day returns
Anna Cieslak and Hao Pang (Duke Fuqua) 17
returnt or return component(ωit) = γ0 + γ11t,FOMC + εt (1994–2017)
10
0
10
20
30
40
1.5
1
.5
0
.5
1.5
1
.5
0
.5
1
∆y(10) (bps)∆y(2) (bps)∆s (bps)
overalloveralloverall ωgωgωg ωmωm
ωm ωp+ωp+ωp+ ωp−ωp−
ωp− rprprp
ωg : growth news; ωm: monetary news; ωp+: hedging premium news; ωp−: common premium news
� Monetary and common premium ωp− news reduces yields...
� Hedging premium news ωp+ raises yields as bond insurance premium becomes less valuable
FOMC announcement-day returns
Anna Cieslak and Hao Pang (Duke Fuqua) 17
returnt or return component(ωit) = γ0 + γ11t,FOMC + εt (1994–2017)
10
0
10
20
30
40
1.5
1
.5
0
.5
1.5
1
.5
0
.5
1
∆y(10) (bps)∆y(2) (bps)∆s (bps)
overalloveralloverall ωgωgωg ωmωm
ωm ωp+ωp+ωp+ ωp−ωp−
ωp− rprprp
ωg : growth news; ωm: monetary news; ωp+: hedging premium news; ωp−: common premium news
� Monetary and common premium ωp− news reduces yields...
� Hedging premium news ωp+ raises yields as bond insurance premium becomes less valuable
� Combination of these effects leads to insignificant yield changes overall
FOMC announcement-day returns
Anna Cieslak and Hao Pang (Duke Fuqua) 17
returnt or return component(ωit) = γ0 + γ11t,FOMC + εt (1994–2017)
10
0
10
20
30
40
1.5
1
.5
0
.5
1.5
1
.5
0
.5
1
∆y(10) (bps)∆y(2) (bps)∆s (bps)
overalloveralloverall ωgωgωg ωmωm
ωm ωp+ωp+ωp+ ωp−ωp−
ωp− rprprp
ωg : growth news; ωm: monetary news; ωp+: hedging premium news; ωp−: common premium news
� Weaker FOMC-day effect before mid-1990s: only common premium ωp− significant yPre-1994
� Consistent with Fed facing growth-inflation tradeoff, thus not being able to reduce hedgingpremium ωp+ (growth uncertainty)
Interpretation of monetary policy surprises
Anna Cieslak and Hao Pang (Duke Fuqua) 18
� High-frequency changes in interest rates around FOMC announcements are a standardway of measuring monetary policy surprises
� Target, path, LSAP shocks ...
Interpretation of monetary policy surprises
Anna Cieslak and Hao Pang (Duke Fuqua) 18
Gurkaynak-Sack-Swanson shocks (narrow window)
(1) (2) (3) (4) (5)
all meetings scheduled meetings1991:7-2015:10 1994–2015:10 2009–2015:10
Target Path Target Path LSAP
growth ωg 0.052 0.170* 0.100 0.256*** 0.108(0.50) (1.86) (1.01) (2.84) (0.88)
monetary ωm 0.563*** 0.375*** 0.396*** 0.517*** 0.027(4.67) (3.83) (3.83) (6.53) (0.17)
hedging pr ωp+ 0.011 0.010 0.008 -0.064 -0.338**(0.16) (0.16) (0.11) (-0.97) (-2.22)
common pr ωp− -0.044 0.361*** -0.011 0.352*** 0.715***(-0.63) (5.32) (-0.12) (4.53) (3.13)
R2 0.34 0.30 0.18 0.46 0.68N 213 213 175 175 55
Coefficients are standardized
� Target shock significantly associated only with monetary news
� Negative path shock can arise from negative growth news (stocks↓, yields↓) • news about monetaryeasing (stocks↑, yields↓) • news that reduces the risk premium (stocks↑, yields↓)
� LSAP shock is all about risk premium; little evidence of the QE signalling effect
x
Stock returns on non-farm payroll announcements
Anna Cieslak and Hao Pang (Duke Fuqua) 19
� Similar to FOMC announcement days, on macro announcements investors updateexpectations not just about the state of the economy
� News of rising unemployment (bad news) is good for stocks during economic expansions
Boyd, Hu, Jagannathan (2005); Law, Song, Yaron (2018)
Stock returns on non-farm payroll announcements
Anna Cieslak and Hao Pang (Duke Fuqua) 19
� 50
� 20
10
40
70
� 5 �
� 20
10
40
70
� 5 �
� 20
10
40
70
� 5 �
� 20
10
40
70
BG: Bad NFP news, good times (69) BB: Bad NFP news, bad times (72)
GG: Good NFP news, good times (59) GB: Good NFP news, bad times (58)bp
s
ωgωg ωmωm ωp+ωp+ ωp−ωp− overalloverall
Good/bad times = top/bottom terciles of output gap. MMS/Bloomberg: Good/bad NFP surprises
BG: Bad NFP news in good times is good news for stocks
Stock returns on non-farm payroll announcements
Anna Cieslak and Hao Pang (Duke Fuqua) 19
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50� 20
10
40
70
BG: Bad NFP news, good times (69) BB: Bad NFP news, bad times (72)
GG: Good NFP news, good times (59) GB: Good NFP news, bad times (58)bp
s
ωgωg ωmωm ωp+ωp+ ωp−ωp− overalloverall
Good/bad times = top/bottom terciles of output gap. MMS/Bloomberg: Good/bad NFP surprises
BG: Bad NFP news in good times is good news for stocks in expectation of monetary easing
Stock returns on non-farm payroll announcements
Anna Cieslak and Hao Pang (Duke Fuqua) 19
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50� 20
10
40
70
BG: Bad NFP news, good times (69) BB: Bad NFP news, bad times (72)
GG: Good NFP news, good times (59) GB: Good NFP news, bad times (58)bp
s
ωgωg ωmωm ωp+ωp+ ωp−ωp− overalloverall
Good/bad times = top/bottom terciles of output gap. MMS/Bloomberg: Good/bad NFP surprises
BB: Bad NFP news in bad times is unambiguous: direct effect on growth expectations
Stock returns on non-farm payroll announcements
Anna Cieslak and Hao Pang (Duke Fuqua) 19
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50� 20
10
40
70
BG: Bad NFP news, good times (69) BB: Bad NFP news, bad times (72)
GG: Good NFP news, good times (59) GB: Good NFP news, bad times (58)bp
s
ωgωg ωmωm ωp+ωp+ ωp−ωp− overalloverall
Good/bad times = top/bottom terciles of output gap. MMS/Bloomberg: Good/bad NFP surprises
GG: Good NFP news in good times offset by expectation of monetary tightening
Stock returns on non-farm payroll announcements
Anna Cieslak and Hao Pang (Duke Fuqua) 19
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50� 20
10
40
70
BG: Bad NFP news, good times (69) BB: Bad NFP news, bad times (72)
GG: Good NFP news, good times (59) GB: Good NFP news, bad times (58)bp
s
ωgωg ωmωm ωp+ωp+ ωp−ωp− overalloverall
Good/bad times = top/bottom terciles of output gap. MMS/Bloomberg: Good/bad NFP surprises
GG: Good NFP news in good times offset by expectation of monetary tightening
Stock returns on non-farm payroll announcements
Anna Cieslak and Hao Pang (Duke Fuqua) 19
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50
� 20
10
40
70
� 50� 20
10
40
70
BG: Bad NFP news, good times (69) BB: Bad NFP news, bad times (72)
GG: Good NFP news, good times (59) GB: Good NFP news, bad times (58)bp
s
ωgωg ωmωm ωp+ωp+ ωp−ωp− overalloverall
Good/bad times = top/bottom terciles of output gap. MMS/Bloomberg: Good/bad NFP surprises
GB: Good NFP news in bad times induces significant risk-premium reactionωp+ ↓, ωp− ↑: less real uncertainty, more monetary uncertainty
Covid-19 crisis
Anna Cieslak and Hao Pang (Duke Fuqua) 20
Cumulative S&P 500 returns and shock contributions since Jan 2, 2020
Wu
han
lo
ckd
ow
n
US
fir
st P
2P
cas
e
Ital
y q
uar
anti
ne
1st
US
co
mm
un
ity
cas
e
Po
wel
l’s
stat
emen
t
Fed
cu
t 5
0b
ps
EU
tra
vel
ban
Nat
ion
al e
mer
gen
cy
Fed
cu
t to
0
Fed
new
mea
sure
s
Ch
ina
trav
el b
an
Fis
cal
stim
ulu
s
−23.4%
−30
−20
−10
0
10
%
02Ja
n
23Ja
n
30Ja
n31
Jan
22Fe
b
26Fe
b28
Feb
03M
ar
11M
ar13
Mar
16M
ar
23M
ar24
Mar
cumulative overall returngrowth news ωg
hedging premium news ωp+ (flight-to-safety)monetary news ωm
common premium news ωp−
The plot reports simple cumulative returns; overall return Rt =∏
j(1 + Rt(ωj)) − 1
Covid-19 crisis
Anna Cieslak and Hao Pang (Duke Fuqua) 20
Cumulative S&P 500 returns and shock contributions since Jan 2, 2020
Wu
han
lo
ckd
ow
n
US
fir
st P
2P
cas
e
Ital
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uar
anti
ne
1st
US
co
mm
un
ity
cas
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Po
wel
l’s
stat
emen
t
Fed
cu
t 5
0b
ps
EU
tra
vel
ban
Nat
ion
al e
mer
gen
cy
Fed
cu
t to
0
Fed
new
mea
sure
s
Ch
ina
trav
el b
an
Fis
cal
stim
ulu
s
−23.4%
−8%
−30
−20
−10
0
10
%
02Ja
n
23Ja
n
30Ja
n31
Jan
22Fe
b
26Fe
b28
Feb
03M
ar
11M
ar13
Mar
16M
ar
23M
ar24
Mar
cumulative overall returngrowth news ωg
hedging premium news ωp+ (flight-to-safety)monetary news ωm
common premium news ωp−
The plot reports simple cumulative returns; overall return Rt =∏
j(1 + Rt(ωj)) − 1
Covid-19 crisis
Anna Cieslak and Hao Pang (Duke Fuqua) 20
Cumulative S&P 500 returns and shock contributions since Jan 2, 2020
Wu
han
lo
ckd
ow
n
US
fir
st P
2P
cas
e
Ital
y q
uar
anti
ne
1st
US
co
mm
un
ity
cas
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Po
wel
l’s
stat
emen
t
Fed
cu
t 5
0b
ps
EU
tra
vel
ban
Nat
ion
al e
mer
gen
cy
Fed
cu
t to
0
Fed
new
mea
sure
s
Ch
ina
trav
el b
an
Fis
cal
stim
ulu
s
−23.4%
−15.3%
−8%
−30
−20
−10
0
10
%
02Ja
n
23Ja
n
30Ja
n31
Jan
22Fe
b
26Fe
b28
Feb
03M
ar
11M
ar13
Mar
16M
ar
23M
ar24
Mar
cumulative overall returngrowth news ωg
hedging premium news ωp+ (flight-to-safety)monetary news ωm
common premium news ωp−
The plot reports simple cumulative returns; overall return Rt =∏
j(1 + Rt(ωj)) − 1
Covid-19 crisis
Anna Cieslak and Hao Pang (Duke Fuqua) 20
Cumulative S&P 500 returns and shock contributions since Jan 2, 2020
Wu
han
lo
ckd
ow
n
US
fir
st P
2P
cas
e
Ital
y q
uar
anti
ne
1st
US
co
mm
un
ity
cas
e
Po
wel
l’s
stat
emen
t
Fed
cu
t 5
0b
ps
EU
tra
vel
ban
Nat
ion
al e
mer
gen
cy
Fed
cu
t to
0
Fed
new
mea
sure
s
Ch
ina
trav
el b
an
Fis
cal
stim
ulu
s
−23.4%
−15.3%
−8%
+4%
−30
−20
−10
0
10
%
02Ja
n
23Ja
n
30Ja
n31
Jan
22Fe
b
26Fe
b28
Feb
03M
ar
11M
ar13
Mar
16M
ar
23M
ar24
Mar
cumulative overall returngrowth news ωg
hedging premium news ωp+ (flight-to-safety)monetary news ωm
common premium news ωp−
The plot reports simple cumulative returns; overall return Rt =∏
j(1 + Rt(ωj)) − 1
Covid-19 crisis
Anna Cieslak and Hao Pang (Duke Fuqua) 20
Cumulative S&P 500 returns and shock contributions since Jan 2, 2020
Wu
han
lo
ckd
ow
n
US
fir
st P
2P
cas
e
Ital
y q
uar
anti
ne
1st
US
co
mm
un
ity
cas
e
Po
wel
l’s
stat
emen
t
Fed
cu
t 5
0b
ps
EU
tra
vel
ban
Nat
ion
al e
mer
gen
cy
Fed
cu
t to
0
Fed
new
mea
sure
s
Ch
ina
trav
el b
an
Fis
cal
stim
ulu
s
−23.4%
−15.3%
−8%
+4%
−5.5%
−30
−20
−10
0
10
%
02Ja
n
23Ja
n
30Ja
n31
Jan
22Fe
b
26Fe
b28
Feb
03M
ar
11M
ar13
Mar
16M
ar
23M
ar24
Mar
cumulative overall returngrowth news ωg
hedging premium news ωp+ (flight-to-safety)monetary news ωm
common premium news ωp−
The plot reports simple cumulative returns; overall return Rt =∏
j(1 + Rt(ωj)) − 1
Conclusions
Anna Cieslak and Hao Pang (Duke Fuqua) 21
� Recover economically interesting shocks from VAR + intuitive restrictions on stocks andthe yield curve
– Motivated by theory but not married to a specific parametric model
– Trace out shocks on any day and over long sample ( 6=event studies)
– Analyze financial market reaction to macro events and Fed announcements
� Highlight the effect of the Fed on risk premia
� Importance of two risk-premium shocks
– Hedging and common premium
– Ambiguous impact on stocks and bonds