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Long Forward Probabilities, Recovery and theTerm Structure of Bond Risk Premiums
Vadim Linetsky
Northwestern University
Supported in part by NSF grants CMMI 1536503 and DMS 1514698.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Based on:
Likuan Qin and V.L., Positive Eigenfunctions of MarkovianPricing Operators: Hansen-Scheinkman Factorization, RossRecovery and Long Term Pricing, Operations Research (PE).
Likuan Qin and V.L., Long Term Risk: a MartingaleApproach, http://ssrn.com/abstract=2523110 (LT).
V.L., Yutian Nie and Likuan Qin, Long Forward Probabilities,Recovery and the Term Structure of Bond Risk Premiums,http://ssrn.com/abstract=2721366.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Long Term Factorization
Stochastic discount factor (pricing kernel) S assigns prices torisky future payoffs:
Pt,T (Y ) = EP[STY
St
âŁâŁâŁâŁFt
].
Long-term factorization of PK:
St =1
BtMt ,
where Bt is the long bond so that 1/Bt discounts at the rateof return on the long bond and Mt is a martingale.Alvarez and Jermann (Econometrica, 2005) first introducedthis factorization in a discrete-time ergodic setting.Hansen and Scheinkman, Long Term Risk: An OperatorApproach, Econometrica 2009, gave a study incontinuous-time ergodic Markovian environments andidentified the factors in terms of the principal eigenfunctionand eigenvalue of the Markovian pricing operator.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Rossâ Recovery Theorem
Rossâ question: can we uniquely recover physical probabilitiesP from currently observed asset prices?
Rossâ Recovery Theorem (J of Finance, 2015):
I All uncertainty is generated by a finite-state, discrete timeirreducible Markov chain
I Transition-independent pricing kernelI Then there is a unique recovery of transition probabilities from
Arrow-Debreu prices (via Perron-Frobenius Theorem)
Carr and Yu (2012)
I 1D diffusions on bounded intervals with regular boundaries
Walden (2013)
I 1D diffusions on RQ & L (2014, PE)
I Detailed analysis for general Markov processes (Borel rightprocesses)
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Connection between Long Term Factorization and RossâRecovery
Hansen and Scheinkman (2014), Borovicka, Hansen andScheinkman (Misspecified Recovery, 2014, to appear in J ofFinance), Q & L (2014, LT and PE) for generalcontinuous-time Markov models (also closely related resultsfor discrete-time, finite state Markov chains in Martin andRoss (2013)):
I Connect Rossâ recovery to the long-term factorization of thepricing kernel
I Identify Rossâ transition independence assumption with settingthe martingale component in the long-term factorization tounity:
Mt = 1, St =1
Bt.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Q & L (2014) Long Term Risk: A Martingale Approach
General (non-Markovian) semimartingale environment
Give a sufficient condition for convergence in semimartingaletopology of trading strategies investing in zero-coupon bondsand rolling over to the long bond
Show convergence in total variation of T -forward measuresQT to the long forward measure L = Qâ
Obtain long-term factorization of HJM models
Restricting to ergodic Markovian environments, recoverHansen & Scheinkman factorization in terms of the principaleigenvalue and eigenfunction of the Markovian pricingoperator
Show that Rossâ recovery identifies P = L and implies growthoptimality of the long bond
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Semimartingale Pricing Kernels
Start with (Ω,F , (Ft)tâ„0,P) with the usual hypothesis.
A semimartingale pricing kernel (St)tâ„0 is assumed to satisfy:
Strict positivity: S and Sâ are strictly positive,Normalization: S0 = 1,Integrability: EP[ST
St] <â for all T â„ t â„ 0.
Price at time t of a payoff Y â FT at time T > t:
Pt,T (Y ) = EP[STY
St
âŁâŁâŁâŁFt
].
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Bonds and Forward Measures QT
Zero-coupon bonds:
PTt := EP
[STSt
âŁâŁâŁâŁFt
].
For each T > 0, a roll-over strategy BT invests $1 at timezero in PT
0 , at time T rolls over into P2TT for [T , 2T ], etc.:
BTt =
P(k+1)Ttâk
i=0 P(i+1)TiT
, t â [kT , (k + 1)T ).
P-Martingales and T -forward factorizations:
MTt := StB
Tt , St =
1
BTt
MTt , t â„ 0.
For each T > 0, the T -forward measure QT (Jarrow 1987,Geman 1989, Jamshidian 1989):
QT |Ft = MTt P|Ft , t â„ 0.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
The Short-term Limit: Risk-Neutral Measure Q = Q0
If S is a special semimartingale, due to our strict positivityassumption there is a multiplicative decomposition:
St = eâDtMt ,
M is a local martingale and D is predictable.
If M is a martingale, then eDt can be interpreted as therisk-free asset (âimplied savings accountâ Doberlein andSchweizer (2001)), and M defines the RN measure:
Q|Ft = MtP|Ft , t â„ 0.
Under technical conditions, Doberlein and Schweizer (2001)prove that eDt coincides with the limit B0
t of roll-overstrategies BT
t as T â 0 (âclassical savings accountâ).
Further, when S is a supermartingale, D is non-decreasing.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
The Long-Term Limit: Long Forward Measure L = Qâ
Theorem (Q& L (2014, LT))
Assume that for each t > 0 there exists a positive random variableMât > 0 such that
limTââ
EP[|MTt âMât |] = 0.
(i) Positive P-martingales (MTt )tâ„0 converge to a positive
P-martingale (Mât )tâ„0 in Emeryâs semimartingale topology.(ii) Positive semimartingales (BT
t )tâ„0 converge to a positivesemimartingale (Bât )tâ„0 in semimartingale topology.(iii) Forward measures QT converge in total variation to anequivalent measure Qâ on each Ft , and
Qâ|Ft = Mât P|Ft , t â„ 0.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
The Long-Term Limit: Long Forward Measure L = Qâ
We call Qâ the long forward measure and denote it L.
We call (Bât )tâ„0 the long bond.
Long-term factorization of the semimartingale pricing kernel
St =1
BâtMât
into discounting at the rate of return on the long bond and amartingale component.
Extends Alvarez and Jermann (2005) and Hansen andScheinkman (2009) to general semimartingale environmentswithout Markovian assumption.
Under L, the long bond Bât is the numeraire asset.
The long term factorization is a general semimartingalephenomenon, not an artifact of Markovian models.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Risk-Return Trade-off under the Long Forward Measure
Theorem (Q & L (2014, LT))
(i) The long bond is growth optimal under L, that is, it has thehighest expected log return under L among all assets priced by thepricing kernel S .(ii) The Sharpe ratio of any asset priced by the PK S takes theform under L:
ELt
[RVt,t+Ï
]â R f
t,t+Ï
ÏLt
(RVt,t+Ï
) = âcorrLt(RVt,t+Ï ,
1
Rât,t+Ï
)R ft,t+ÏÏ
Lt
(1
Rât,t+Ï
)where RV
t,t+Ï , R ft,t+Ï and Rât,t+Ï is the return from holding asset
V , risk-free zero-coupon-bond and long bond from t to t + Ï .
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Risk-Return Trade-off under the Long Forward Measure -Cont.
Proposition (Q & L (2014, LT))
(i) Under diffusion setting,
limÏâ0
corrLt(Rât,t+Ï , 1/R
ât,t+Ï
)= â1.
(ii) If furthermore the risk free asset exists,
limÏâ0
corrLt(R0t,t+Ï , 1/R
ât,t+Ï
)= 0,
where R0t,t+Ï = B0
t+Ï/B0t is the return on the risk free asset from t
to t + Ï .
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
A Quartet of Measures
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Markovian Pricing Kernels
X is a conservative Borel right process (Borel topology on thestate space, strong Markov, right continuous paths). (Ft)tâ„0is generated by X .
Pricing kernel S is a positive semimartingale multiplicativefunctional of X :
St+s(Ï) = St(Ï)Ss(Ξt(Ï)),
where Ξs is the shift operator, Ξs : Ωâ Ω,
Xs(Ξt(Ï)) = Xt+s(Ï).
Pricing operators (Pt)tâ„0:
(Pt f )(x) = EPx [St f (Xt)]
for any Borel payoff f (x) for which expectation is well defined.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Positive Eigenfunctions and Eigen-Measures
Suppose Pt has an eigenfunction 0 < Ï(x) <â:
PtÏ(x) = eâλtÏ(x)
for each t > 0, x â E , and some λ â R.
The process
MÏt = Ste
λt Ï(Xt)
Ï(X0)
is a positive P-martingale, and PK admits aneigen-factorization (Hansen-Scheinkman (2009)):
St = eâλtÏ(X0)
Ï(Xt)MÏ
t .
We can define an eigen-measure QÏ,
QÏ|Ft := MÏt P|Ft , (Pt f )(x) = eâλtÏ(x)EQÏ
x
[f (Xt)
Ï(Xt)
].
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Long Forward Measure as an Eigen-Measure
Theorem (Q & L (2014, LT))
Suppose the Markovian pricing kernel satisfies the sufficientcondition for long term factorization under Px for each initial statex â E . Then, under some regularity condition, the long bond isidentified with a positive multiplicative functional of X in thetransition independent form:
Bât = eλLtÏL(Xt)
ÏL(x),
where ÏL(x) is a positive eigenfunction of the pricing operators(Pt)tâ„0 with the eigenvalues eâλLt for some λL â R. The longforward measure L is identified with the correspondingeigen-measure QÏL .
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Uniqueness of Recurrent Eigenfunction ÏR
Theorem (Q & L (2014, PE))
There is at most one positive eigenfunction ÏR such that X isrecurrent under the corresponding eigen-measure QÏR .
Proof is essentially based on the fact that for a recurrentMarkov process excessive functions are constant.
Q & L (2014, PE) give several sets of sufficient conditions forexistence
and explicitly verify existence in many financial models (incl.affine, quadratic).
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Ergodicity Identifies QÏR = L
Theorem (Q & L (2014, LT))
Assume X has a stationary distribution ” under QÏR and thereexist c > 0, α > 0 and T0 > 0 s.t. for each T â„ T0
|EQÏR
x [f (XT )]â E”[f (X )]| †c
ÏR(x)eâαT
for each f s.t. |f (x)| †1ÏR(x)
. Then (BTt )tâ„0 converge in
semimartingale topology to the positive semimartingale
Bât = eλtÏR(Xt)
ÏR(X0),
MÏRt = Mât , QÏR = L, and, assuming
â«E (1/ÏR)d” <â,
PTt = Ceâλ(Tât)ÏR(Xt) + O(eâ(λ+α)(Tât)), C = E”[1/ÏR(X )].
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Rossâ Recovery under Recurrence: P = QÏR
Rossâ assumption of transition independence of PK:
St = eâλth(Xt)
h(X0)
for some positive h.1h is a positive eigenfunction, and PK has the factorization
St = eâλtÏ(X0)
Ï(Xt)MÏ
t ,
where Ï = 1h and MÏ
t = 1.
P = QÏ is an eigen-measure.
If we further assume X is recurrent under P, then
P = QÏR .
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Rossâ Recovery under Ergodicity: P = L
Combining Rossâ transition independence assumption withrecurrence yields a unique recovery
P = QÏR .
Strengthening to ergodicity we arrive at the furtheridentification
P = QÏR = L.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Rossâ Recovery in Semimartingale Models without theMarkov Property
Rossâ assumption can be extended to non-Markoviansemimartingale environments by directly assuming that
Mât = 1.
This leads to
St =1
Bât
and idenfiticationP = L.
Under this assumption, the long bond is the numeraire assetunder P and is growth optimal by Jensenâs inequality.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Empirical Exploration
Data: 1993-2015 US Treasury yield curves from FRED (St. LouisFed web site; same data available from US Treasury).
Figure: Bootstrapped zero-coupon yield curves
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Modeling Term Structure at the Zero Lower Bound (ZLB)
The zero interest rate regime Dec 2008-2015 is a challenge toconventional interest rate models.
Affine models cannot deal with ZLB.
Gaussian models admit negative rates, while CIR-type havevanishing volatilities for bonds of all maturities at the ZLB.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Modeling Term Structure at the ZLB Cont.
Shadow rate idea due to F. Black (J. of Finance, 1995)âInterest Rates as Optionsâ: nominal short rate is a positivepart (due to the option to convert to currency) of a shadowrate that can get negative.
Gorovoi and Linetsky (Risk 2003, Mathematical Finance2004): solved with Vasicek shadow rate and calibrated toJapanese government bonds.
Adopted by the Bank of Japan in 2005 (Baba et al. 2005).
Kim and Singleton (J of Econometrics, 2012): extended andestimated 2-factor shadow rate models on JGB data.
Shadow models in use by central banks post-crisis.
We are working on a more general class based on SDEs withsticky boundaries that nest shadow rate models.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
B-QG2 Shadow Rate Model
Estimate the same specification for 2-factor shadow ratequadratic Gaussian model as Kim and Singleton.
The state variable X is a 2D Gaussian diffusion under P.
The market price of Brownian risk is affine in X , so that Xremains 2D Gaussian diffusion under Q.
The short rate is a positive part of the shifted quadraticfunction in X .
Estimation: extended Kalman filter, with the bond pricingPDE solved via ADI finite difference scheme, and KNITROnon-linear optimizer.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Estimation Results: P and Q Dynamics
Short rater(Xt) = (â0.0046 + 0.27X 2
1,t + 0.18X1,tX2,t + 0.05X 22,t)
+.
State Vector SDEs:
dXt =
[0.65 00.22 0.04
]([â0.050.77
]â Xt
)dt +
[0.1 00 0.1
]dBP
t .
dXt =
[0.32 0.040.64 0.08
]([0.93â5.92
]â Xt
)dt +
[0.1 00 0.1
]dBQ
t .
Market price of risk
λP(Xt) =
[â0.89â0.96
]+
[â3.33 0.424.21 0.40
]Xt .
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Estimation Results: The Filtered Path of Shadow Rate
Year1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
Yie
ld (
%)
-1
0
1
2
3
4
5
6
7
3 Month RateShadow Rate
Year1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
x1x2
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Principal Eigenfunction and Eigenvalue
As time to maturity T increases, the zero-coupon bond pricebehaves asymptotically as (the long bond asymptotics):
P(T , x) ⌠CeâλTÏ(x).
Using estimated Q measure parameters, the principaleigenvalue and eigenfunction are determined numerically byfinite differences: λ â 2.82%
Figure: Shape of principal eigenfunctionVadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Recovering L Measure Dynamics
In diffusion models, the market price of risk under L (RossâRecovery) is recovered in terms of the principal eigenfunction(Ï is the volatility matrix in the SDE):
λLi (x) =âj
Ïji (x)âj log Ï(x).
Numerical result shows that λL is well approximated by alinear function in the range [â0.3 0.2]Ă [â0.1 1.2], whichcontains the range of filtered state variables. In particular,
λL(Xt) â[
0.16â0.10
]+
[â0.38 0.170.17 â0.12
]Xt .
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Comparing P, L and Q
By inspection of market prices of risk, we observe that the Lmeasure dynamics is generally closer to Q (from which it isrecovered), than to the estimated P.
Recall that the transition independence assumption results inthe identification P = L. In contrast, in our results we seesignificant differences between estimated P and recovered L.
This is not surprising. Recall that P = L implies that the longbond is growth optimal.
In contrast, Frazzini and Pedersen (2014, J of FinancialEconomics) document that their âBetting Against theBetaâ (BAB) factor levering up shorter maturity bonds torisk parity with longer maturity bonds and shorting longermaturity bonds yields Sharpe ratio of 0.81 in the US Treasurybond market during 1952-2012.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
How far apart are P vs. L forecasts? Test of P = L
Using estimated L and P dynamics, we can write down theinstantaneous volatility of the martingale component:
v(x) â[â1.055â0.863
]+
[â2.946 0.2464.045 0.525
] [x1x2
].
When P = L, the martingale component is trivial, i.e.v(x) = 0. Thus we can test the hypothesis P = L by testingeach of the component of v(x) being equal to 0.
v1 v2 v11 v21 v220.00% 0.00% 0.08% 0.04% 0.00%
Table: p-values for vi = 0 and vij = 0.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
How far apart are P vs. L forecasts? The Timing of FedLift-off
We estimate the implied distribution of the first passage timeof the short rate above 25 bps as the proxy for the timing ofthe Fed zero interest rate policy lift-off as of August 19, 2015.
Median Mean
P 0.33 1.07Q 0.17 0.34L 0.16 0.32
Year0 0.5 1 1.5 2 2.5 3 3.5
Pro
babi
lity
0
0.1
0.2
0.3
0.4
0.5
0.6
P forecastQ forecastL forecast
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
How far apart are P vs. L forecasts? The Timing of FedLift-off
We estimate the implied distribution of the first passage timeof the short rate above 25 bps as the proxy for the timing ofthe Fed zero interest rate policy lift-off as of Dec. 30, 2011.
Median Mean
P 2.13 2.83Q 1.34 1.47L 1.32 1.46
Year0 1 2 3 4 5 6 7
Pro
babi
lity
0
0.02
0.04
0.06
0.08
0.1
0.12
P forecastQ forecastL forecast
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Bond risk premiums
Maturity0 5 10 15 20 25 30
Shar
pe ra
tio
-0.1
0
0.1
0.2
0.3
0.4
0.5
RealizedP forecastL forecast
Figure: Realized, P forecast and L forecast Sharpe ratio over 3 monthholding period.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums
Further work
Refining interest rate modeling at the zero lower bound:general sticky boundary models, stochastic volatility, linkswith macroeconomic variables.
Explorations of market-implied forecasts.
Looking at other markets.
Vadim Linetsky Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums