Double Mean-Reversion in FX
Philippe Balland
Quan Minh Tran
Gabriela Hodinic
P. Balland 1 June 2010
Contents 1. Market Observations 2. Single Mean-Reversion 3. Double Mean-Reversion 4. ATM Minimum 5. Smile Calibration 6. Two-Factor SLV Model 7. Simplification 8. Calibration
P. Balland 2 June 2010
Market Observations Three key observations:
(1) Short-dated ATM expiring within a month are:
• highly volatile • weakly correlated to ATM expiring
beyond one year (when considering monthly changes).
P. Balland 3 June 2010
(2) The ATM curve has often a local minimum within the first three months.
(3) Strangle margins are persistent at the short-end of the curve and the implied volatility-of-volatility is therefore large at the short-end.
USDJPY - May 2010
-100.00%
-50.00%
0.00%
50.00%
100.00%
150.00%
200.00%
250.00%
300.00%
350.00%
400.00%
0 1 2 3 4 5 6
mat
EqvolvolMktEqrhoMkt
P. Balland 4 June 2010
Eqvolvol, Eqrho for expiry T are respectively the SABR volatility-of-volatility and the SABR correlation :
Teqγ
Teqρ
dtdWdW
dWtWXdX
XtFS
Teq
fxtt
fxt
Teqt
Teq
Ttt
tt
ρ
γγσσ
σ
>=<
−=
=
,
))(exp(/
)(2
21
0
where S is the spot process, F is the forward, and T
0σ is the initial volatility for expiry T. We calculate and using the local-time approximation as this is more accurate than the expansion formula in the vicinity of the forward:
Teqγ T
eqρ
221
2
)()(2
02
20
)]([
)]([)(])[(
kItkt
Att
Ttt
kT
tekXE
dtkXEkXkXE
−
++
≈−
−+−=− ∫κδσ
δσ
See E. Benhamou, O. Croissant (2007).
P. Balland 5 June 2010
Single Mean-Reversion We consider for each expiry T the following one-factor mean-reverting dynamic:
dtdWdW
dWeZ
ZZ
dWXdX
fxtt
tu
tut
ttt
fxtt
Ttt
ρ
γ
υ
υσ
σ
σλσ
σσ
>=<
=
−=
=
∫ −
,
)var22exp(
/
0)(
0
The volatility-of-volatility γ , correlation ρ and mean-reversion λ are calibrated to the entire smile-surface by moment matching:
Teq
duE
duEF
TeqFF
Tu
TuTK
TKTK
ρρ
λγ
γλγ
σ
σ
=
=
=
∫
∫
022
022
)][(
])[(1
11
)](,[
)](0,[)](,[
P. Balland 6 June 2010
As illustrated in the example below, the calibration loses accuracy at the short-end. This could be prevented by using a time-dependent volatility-of-volatility, but at the cost of losing time-homogeneity.
USDJPY - May 2010λ=1
-100.00%
-50.00%
0.00%
50.00%
100.00%
150.00%
200.00%
250.00%
300.00%
350.00%
400.00%
0 1 2 3 4 5 6
mat
EqvolvolMkt
EqrhoMkt
EqvolvolAnalytic 1F
EqrhoAnalytic 1F
Eqvolvol MC
Eqrho MC
P. Balland 7 June 2010
Double Mean-Reversion We could improve the calibration’s accuracy at the short-end and maintain time-homogeneity by using a two-factor stochastic volatility model:
sdtsd
sdtsd
sdt
ldtldt
sdtldt
ttt
fxtt
Ttt
dWdtZZdZ
dWdtZZdZ
ZZ
dWXdX
γλ
γλ
υ
υσ
σσ
σσ
+−=
+−=
−=
=
∞ )(
)(
)var22exp(
/ 0
By direct integration, we obtain:
sdt
ldtt
t sdu
tusd
t ldu
tutuld
ttttt
ZZZE
dWe
dWee
eeZZeZeZZ
sd
ldsdld
ldsd
sd
sdld
sd
ldsdsdsd
++=
+
−+
−−+−+=
∫
∫−
−−−−
−−−∞
−∞
−
][
)1(
)()()1(
0)(
0))(()(
00
σ
λ
λλλλλ
λ
λλλ
λλσλλσσ
γ
γ
Typically, the dynamic is based on two non-overlapping time-scales:
yearweek lddlsdsd 1/1,1/1 ∝=∝= λτλτ .
P. Balland 8 June 2010
Hence, the dynamic is similar to the two-factor volatility model considered in Balland (2006) and Bergomi (2008, 2005):
∫ −= −−−−
t ldu
tutuld
ldt dWeeZ ldsdld
ldsd
sd0
))(()( )1( λλλλλ
λ γ
∫≈ −t ldu
tuld dWe ld
0)( λγ
∫= −t sdu
tusd
sdt dWeZ sd
0)(λγ
P. Balland 9 June 2010
ATM Minimum We can approximate short-dated ATM levels as follows:
∫=∫
∫≈T
uTT
uT
TuTT
duZEduE
duEATM
01
021
021
])[2exp(][
][σσ
σ
The ATM curves generated by the double mean-reversion model admit typically local minima.
USDJPY ATM curve
0.12
0.125
0.13
0.135
0.14
0.145
0.15
0.155
0.16
0 0.5 1 1.5 2 2.5
double-mrev lnsv
double-mrev heston
USDJPY Nov09
P. Balland 10 June 2010
Smile Calibration We calibrate the parameters ldsdldsd λλγγ ,,, to the smile surface by moment matching:
Teq
duE
duEldsdldsdF
TeqFldsdldsdF
Tldeq
Tsdeqldsd
Tldeq
Tsdeq
Tldeqfxld
Tsdeqfxsd
Tu
TuTK
TKTK
ρ
λλγγ
γλλγγ
γγργγ
γργρ
σ
σ
=
=
=
++
+
∫
∫
,,,2
,2
,
,,,,
022
022
2)()(
)][(
])[(2
12
)](,,,[
)](0,[)](,,,[
where and are obtained by solving the following equations:
Tsdeq,γ T
ldeq,γ
)](,[)](0,[
)](,[)](0,[
1,1
1,1
TKTK
TKTK
ldldFT
ldeqF
sdsdFT
sdeqF
λγγ
λγγ
=
=
P. Balland 11 June 2010
USDJPY - May 2010
-100.00%
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0.00%
50.00%
100.00%
150.00%
200.00%
250.00%
300.00%
350.00%
400.00%
0 1 2 3 4 5 6
mat
EqvolvolMktEqrhoMktEqvolvolAnalytic 2FEqrhoAnalytic 2FEqvolvolMCEqrhoMC
The moment-matching technique used to compute effective volatility-of-volatilities and correlations is accurate when compared to the Monte-Carlo method. We observe that the fit to market data is substantially improved by using double mean-reversion.
P. Balland 12 June 2010
As illustrated in the example below, the model parameters sdλ , ldλ , sdγ , ldγ , ldsd ,ρ and fxld ,ρ appear relatively stable over time.
USDJPYλsd=40, λ ld=0.4
-1
0
1
2
3
4
5
Dec-09 Jan-10 Jan-10 Feb-10 Mar-10 Mar-10 Apr-10 May-10 May-10
volvol[sd]
volvol[ld]rho[sd,ld]
rho[sd,fx]
rho[ld,fx]
The correlation parameter fxsd ,ρ appears less stable. This is expected since the model does not include any local volatility.
P. Balland 13 June 2010
Two-Factor SLV Model We control the joint evolution between risk-reversal and spot by including a local volatility component )ln,( tXtσ in the dynamic:
)var22exp(
)ln,(/0
σσυ
υσ
ttt
fxttttt
ttt
ZZ
dWXtXdX
FXS
−=
=
=
∫
∫⊥−⊥
−
−+
=
+=
t fxu
tuldfxld
ldu
t fxu
tuldfxld
ldu
ldt
sdt
ldtt
dWem
dWemZ
ZZZ
ld
ld
0)(2/12
,
0)(
,
)1( λ
λ
σ
γρ
γρ
2/12,
,,,
)1(
0)(2/122
,
0)(
0)(
,
)1(
fxld
fxldfxsdldsd
sd
sd
sd
sd
t fxu
tusdsdfxsd
sdu
t fxu
tusdsd
sdu
t fxu
tusdfxsd
sdu
sdt
dWem
dWem
dWemZ
ρ
ρρρ
λ
λ
λ
α
γαρ
γα
γρ
−
−
⊥⊥−⊥
⊥−⊥
−
=
−−+
+
=
∫
∫
∫
where ⊥⊥ ldldsdsd mmmm ,,, are the mixing-weight parameters.
P. Balland 14 June 2010
As these parameters vary between zero and one while maintaining fixed the target smile, the dynamic varies from local to stochastic volatility dynamic. The parameters control the amount of volatility-of-volatility parallel to the spot motion. As they increase from zero to one while the target smile is fixed, the slope of the local volatility decreases to compensate for the increase in volatility-of-volatility parallel to spot. Despite these parameters affecting the backbone of the dynamic, they have in fact little effect on the valuation of exotics.
ldsd mm ,
An asymptotic calculation shows that we have for all mixing weights:
volatility-implied :)/ln,(ln
0/lnln )0,)(ln(2
=Σ
∆∆ Σ∂=
FKF
FKFATM
T
F
P. Balland 15 June 2010
The ATM-speed coefficient FATMln∆
∆ is to be understood in the sense of Malliavin derivative:
⊥∆∆ += fx
tfx
tFATM
ATMdATM dWdW
t
t )(ln L The parameters ⊥⊥ ldsd mm , control the amount of volatility-of-volatility orthogonal to the spot motion. As they increase from zero to one while the target smile is fixed, the convexity of the local volatility decreases to compensate for the increase in volatility-of-volatility. Hence, the mixing-weights ⊥⊥ sdld mm , control the convexity of the local volatility and thus control the joint evolution of risk-reversal (slope of smile) and spot. They are therefore critical to the valuation of Barrier and DNT products as these parameters affect directly the expected slope of the smile prevailing when spot hits the barrier level. An asymptotic calculation shows that the RR25 speed F
RRln
25∆∆ depend on the level of mixing
weights.
P. Balland 16 June 2010
As illustrated in the example below, the spot and risk-reversal are strongly correlated.
dRR25/dlnF
∆RR25 = 0.0767* ∆lnF - 2E-05
-0.006
-0.004
-0.002
0
0.002
0.004
0.006
-0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04
∆F/F
∆R
R25
The mixing weight parameters ⊥⊥ sdld mm , can be set to match historical RR25 speeds or DNT prices.
P. Balland 17 June 2010
Simplification Typical short-dated products do not depend strongly on the mixing-weights and as these parameters control whether the skew implied by the dynamic originates from local or stochastic volatility.
sdm ldm
Consequently, we can simplify the dynamic by assuming and to be zero: sdm ldm
)var22exp(
)ln,(/0
σσυ
υσ
ttt
fxttttt
ttt
ZZ
dWXtXdX
FXS
−=
=
=
2/12,
,,,
)1(
0)(2/122
,
0)())((2/12
,
)1(
])1([
fxld
fxldfxsdldsd
sd
ldldsd
sd
t fxu
tusdsdfxsd
sdu
t fxu
tutusdsd
sduldfxld
ldut
dWem
dWeemmZ
ρ
ρρρ
λ
λλλσ
α
γαρ
γαγρ
−
−
⊥⊥−⊥
⊥−−−⊥⊥
=
−−+
+−=
∫
∫
Using our moment matching technique, we can approximate the dynamic of the volatility driver using either a one-factor or a two-factor dynamic:
P. Balland 18 June 2010
(i) ∫∫ ⊥⊥−⊥⊥⊥−⊥ += t fxu
tuu
t fxu
tuut dWedWeZ sdld
0)(
0)( λλσ γγ
(ii) ∫ ⊥−⊥= t fx
utu
ut dWeZ ld0
)(λσ γ The orthogonalisation allows fast backward and forward inductions. In particular, we can approximate the volatility drivers using Markov chains:
(i) ⊥⊥⊥⊥⊥⊥ Σ+Σ= tttttZ ξξσ
(ii) ⊥⊥Σ= tttZ ξσ where ⊥⊥⊥
tt ξξ , are independent N(0,1)-processes characterized by their auto-correlation functions. The version (ii) is sufficient for first generation exotic products. We can calibrate the local-volatility to the smile assuming (ii) in particular.
P. Balland 19 June 2010
Calibration Parametric Local Volatility We parameterize the local volatility:
)(ln )(ln
)(ln )()(ln 0
tkilling
tconvex
tskew
t
XX
XtX
σσσ
σσ
×××
=
We choose the local volatility skew using a ratio of CEV. This ensures that the skew component has a CEV-like shape near the forward while being bounded:
)ln)1(tanh(
)(ln
11
21
21
)()(
1
1
tqqqq
bXaX
tskew
X
Xt
t
−+=
=
−+−+
++
−
−
β
σ β
β
1)(ln << tskew Xq σ
P. Balland 20 June 2010
The local volatility skew component has a functional form similar to that suggested by Brown and Randall (2003):
)1(
)/lntanh()(ln
)/lncosh(1
smiletsmile XXasmile
skewtskewskewatmt XXaX
−×+
×+=
σ
σσσ
Note however that the BR functional form is additive while our parameterisation is in fact multiplicative. The convex local volatility shares the same short-dated asymptotic as SABR in order to minimize changes in the smile when the mixing-weight parameters vary:
22 )(lnln21)(ln tttconvex XbXaX ++=σ The killing component ensures finite moments by exponentially decreasing the spot volatility outside the boundaries and : )(tX low )(tXup
))(ln)(lnexp()(ln )(
)(++ −−=
tlow
upt
XtX
tXX
tkilling kkXσ
P. Balland 21 June 2010
Finally, we calibrate the function )(0 tσ to the prevailing ATM curve by forward induction. Parametric Smile We can parameterize the smile surface using asymptotic expansions of the previous diffusion ( 0,0 == ldsd λγ ) which is a direct extension of SABR for FX:
))(exp(
)(ln)(ln /2
21
0 tγWγ
dWXXXdXT
tTT
t
ttconvextskewttt
−=
××=σσσ
σσσ
In this case, the local volatility is obtained by forward induction using the equation:
2/1]ln|[/)ln,()ln,( var22t
ZZtDt XeEXtXt tt
σσσσ −=
where )ln,( tD Xtσ is the Dupire local volatility obtained from the parameterized smile.
P. Balland 22 June 2010