Consistent pricing and hedging of an FX options book

L. Bisesti, A. Castagna and F. Mercurio∗

1 Introduction
In the foreign exchange (FX) options market away-from-the-money options are quite ac-
tively traded, and quotes for the same type of instruments are available everyday with
very narrow spreads (at least for the main currencies). This makes it possible to devise
a procedure for extrapolating the implied volatilities of non-quoted options, providing us
with reliable data to which one can calibrate one’s favorite alternative to the Black and
Scholes (1973) (BS) model.
Brigo, Mercurio and Rapisarda (2004) have proposed an extension to the BS model
where both the volatility and interest rates are stochastic in a very simple way. In this
model, with uncertain volatility and uncertain interest rates (UVUR), the underlying asset
evolves as a geometric Brownian motion with time-dependent coefficients, which are not
known initially, and whose value is randomly drawn at an infinitesimal future time.
As stressed by the authors themselves, the UVUR model can accommodate very general
volatility surfaces and, in case of the FX options market, one can achieve a perfect fitting
to the main volatility quotes.
In this article, we test the goodness of this model as far as some fundamental practical
implications are concerned. First of all, we ourselves show the fitting capability of the
model with an example from real market data. We then support the goodness of our
calibration by providing a diagnostic on the forward volatilities implied by the model. We
also compare the model prices of some exotic options with the corresponding ones given
by a market practice. Finally, we show how to derive bucketed sensitivities to volatility
and how to hedge accordingly a typical options book.
The article is organized as follows. Section 2 provides a brief description of the FX op-
tions market and its volatility quotes. Section 3 introduces the UVUR model and describes
its analytical tractability. Section 4 deals with an example of calibration to real market
data. Section 5 illustrates a forward volatility surface and some forward volatility curves
implied by the previously calibrated parameters. Section 6 deals with the issue of pricing
∗
Product and Business Development and FX Options Trading, Banca IMI, Corso Matteotti, 6, 20121,
Milan, Italy. We are grateful to Aleardo Adotti, head of the Product and Business Development at Banca
IMI, for his constant support and encouragement and to Francesco Rapisarda and Micol Ghisoni for helpful
discussions.

1
exotic options. Section 7 considers an explicit example of volatility hedging applied to a
given options book. Section 8 concludes the article.

2 A brief description of the FX options market

A stylized fact in the FX market is that options are quoted depending on their Delta, and
not their strike as in other options market. This basically reflects the sticky Delta rule,
according to which implied volatilities do not vary, from a day to the next, if the related
moneyness remains the same. To state it differently, when the underlying exchange rate
moves, and the Delta of an option changes accordingly, a different implied volatility has
then to be plugged into the corresponding Black and Scholes (1973) formula.
The FX options market is characterized by three volatility quotes up to relatively long
expiries (at least for the EUR/USD exchange rate): i) the at-the-money (ATM), ii) the
risk reversal (RR) for the 25∆ call and put, iii) the (vega-weighted) butterfly (VWB) with
25∆ wings.1 From these market quotes, one can easily infer the implied volatilities for the
25∆ call and put, and then build upon them an entire smile for the range going from a 5∆
put to a 5∆ call.2

2.1 The market quotes

We denote by S(t) the value of a given exchange rate, say the EUR/USD, at time t. We
set S0 := S(0) > 0 and denote, respectively, by P d (0, t) and P f (0, t) the domestic and
foreign discount factors for maturity t. We then consider a market maturity T . The Delta,
at time 0, of a European call with strike K, maturity T and volatility σ is given by
à S0 P f (0,T ) 1 2 !
ln KP d (0,T ) + 2 σ T
P f (0, T )Φ √ ,
σ T
where Φ denotes the standard normal distribution function.3 The market quotes for ma-
turity T are defined as follows.
The ATM volatility is that of a 0∆ straddle, whose strike, for each given expiry, is
chosen so that the related put and call have the same ∆ but with different signs. Denoting
by σAT M the ATM volatility for the expiry T , the ATM strike KAT M can be immediately
derived:
P f (0, T ) 1 σAT
2
KAT M = S0 d e2 MT (1)
P (0, T )
The RR is a structure where one buys a call and sells a put with a symmetric Delta.
The RR is quoted as the difference between the two implied volatilities, σ25∆c and σ25∆p
1
In accordance with the market jargon, we drop the “%” sign after the level of the Delta, so that a 25∆
call is one whose Delta is 0.25. Analogously, a 25∆ put is one whose Delta is -0.25.
2
Notice that a x∆ call is equivalent to a (P f (0, T ) − x)∆ put, with P f defined below.
3
Notice that this Delta can be interpreted as the discounted probability of ending in the money under
the measure associated with the numeraire S(t)/P f (0, t).

2
to plug into the Black and Scholes formula for the call and the put respectively. Denoting
such a price, in volatility terms, by σRR , we have:

σRR = σ25∆c − σ25∆p (2)

The VWB is built up by selling a quantity of ATM straddle and buying a quantity of
25∆ strangle, in such a way the resulting structure has a zero Vega. The butterfly’s price
in volatility terms, σV W B , is then defined by:
σ25∆c + σ25∆p
σV W B = − σAT M (3)
2
For the given expiry T , the two implied volatilities σ25∆c and σ25∆p can be immediately
identified by solving a linear system. We obtain:

σ25∆c = σAT M + σV W B + 12 σRR (4)

σ25∆p = σAT M + σV W B − 12 σRR (5)

The two strikes corresponding to the 25∆ put and 25∆ call can be derived, after straight-
forward algebra, from their definitions:
P f (0, T ) −ασ25∆p √T + 1 σ25∆p
2 T
K25∆p = S0 d
e 2
P (0, T )
(6)
P f (0, T ) ασ25∆c √T + 1 σ25∆c
2 T
K25∆c = S0 d e 2
P (0, T )

where α := −Φ−1 ( 14 /P f (0, T )) and Φ−1 is the inverse normal distribution function. We
stress that, for typical market parameters and for maturities up to two years, α > 0 and

K25∆p < KAT M < K25∆c

Starting from the implied volatilities σ25∆p , σ25∆c and σAT M and the related strikes, one
can finally build the whole implied volatility smile for expiry T . A consistent construction
procedure is given, for instance, in Castagna and Mercurio (2004).
An example of market volatility quotes is given in Table 1 and the associated implied
volatility surface is shown in Figure 1.

3 The UVUR model

We assume that the exchange rate dynamics evolves according to the uncertain volatility
model with uncertain interest rates proposed by Brigo, Mercurio and Rapisarda (2004). In
this model, the exchange rate under the domestic risk neutral measure follows
(
S(t)[(rd (t) − rf (t)) dt + σ0 dW (t)] t ∈ [0, ε]
dS(t) = d f
(7)
S(t)[(ρ (t) − ρ (t)) dt + σ(t) dW (t)] t > ε

3
where rd (t) and rf (t) are, respectively, the domestic and foreign instantaneous forward
rates for maturity t, σ0 and ε are positive constants, W is a standard Brownian motion,
and (ρd , ρf , σ) is a random triplet that is independent of W and takes values in the set of
N (given) triplets of deterministic functions:


(r1d (t), r1f (t), σ1 (t)) with probability λ1


(rd (t), rf (t), σ2 (t)) with probability λ2
2 2
(ρd (t), ρf (t), σ(t)) = . ..

 .. .


(rd (t), rf (t), σ (t)) with probability λ
N N N N

where the λi are strictly positive and add up to one. The random value of (ρd , ρf , σ) is
drawn at time t = ε.
The intuition behind the UVUR model is as follows. The exchange rate process is
nothing but a BS geometric Brownian motion where the asset volatility and the (domestic
and foreign) risk free rates are unknown, and one assumes different (joint) scenarios for
them.
The volatility uncertainty applies to an infinitesimal initial time interval with length
ε, at the end of which the future values of volatility and rates are drawn. Therefore, S
evolves, for an infinitesimal time, as a geometric Brownian motion with constant volatility
σ0 , and then as a geometric Brownian motion with the deterministic drift rate rid (t) − rif (t)
and deterministic volatility σi (t) drawn at time ε.
In this model, both interest rates and volatility are stochastic in the simplest possible
manner. As already noted by Brigo, Mercurio and Rapisarda (2004), uncertainty in the
volatility is sufficient by itself to accommodate implied volatility smiles (σRR close to zero),
whereas uncertainty in interest rates must be introduced to capture skew effects (σRR far
from zero).
Setting µi (t) := rid (t) − rif (t) for t > ε, µi (t) := rd (t) − rf (t) and σi (t) = σ0 for t ∈ [0, ε]
and each i, and sZ
Z t t
Mi (t) := µi (s) ds, Vi (t) := σi2 (s) ds
0 0

we have that the density of S at time t > ε is the following mixture of lognormal densities:
N
( · ¸2 )
X 1 1 y
pt (y) = λi √ exp − 2 ln − Mi (t) + 21 Vi2 (t) . (8)
i=1
yV i (t) 2π 2V i (t) S 0

Accordingly, European option prices are mixtures of BS prices. For instance the arbitrage-
free price of a European call with strike K and maturity T is
N
" Ã ! Ã !#
X ln SK0 + Mi (T ) + 12 Vi2 (T ) ln SK0 + Mi (T ) − 12 Vi2 (T )
d Mi (T )
P (0, T ) λi S0 e Φ − KΦ .
i=1
Vi (T ) Vi (T )
(9)

4
Further details can be found in Brigo, Mercurio and Rapisarda (2004).
The analytical tractability at the initial time is extended to all those derivatives which
can be explicitly priced under the BS paradigm. In fact, the expectations of functionals
of the process (7) can be calculated by conditioning on the possible values of (ρd , ρf , σ),
thus taking expectations of functionals of a geometric Brownian motion. Denoting by E
the expectation under the risk-neutral measure, any smooth payoff VT at time T has a
no-arbitrage price at time t = 0 given by
N
X n ¯ d o XN
d
V0 = P (0, T ) ¯ d f f
λi E VT (ρ = ri , ρ = ri , σ = σi ) = λi V0BS (rid , rif , σi ) (10)
i=1 i=1

where V0BS (rid , rif , σi ) denotes the derivative’s price under the BS model when the risk free
rates are rid and rif and the asset (time-dependent) volatility is σi .
The advantages of model (7) can be summarized as follows: i) explicit dynamics; ii)
explicit marginal density at every time (mixture of lognormals with different means and
standard deviations); iii) explicit option prices (mixtures of BS prices) and, more generally,
explicit formulas for European-style derivatives at the initial time; iv) explicit transitions
densities, and hence future option prices; v) explicit (approximated) prices for barrier
options and other exotics;4 vi) potentially perfect fitting to any (smile-shaped or skew-
shaped) implied volatility curves or surfaces.

4 An example of calibration
We consider an example of calibration to EUR/USD market data as of 12 February 2004,
when the spot exchange rate was 1.2832.
In Table 1 we report the market quotes of EUR/USD σAT M , σRR and σV W B for the
relevant maturities from one week (1W) to two years (2Y), while in Table 2 we report the
corresponding domestic and foreign discount factors.
The implied volatility surface that is constructed from the basic volatility quotes is
shown in Table 3, for the major Deltas, and in Figure 1, where for clearness’ sake we plot
the implied volatility in terms of put Deltas ranging from 5% to 95% and for the same
maturities as in Table 1.
In order to exactly fit both the domestic and foreign zero coupon curves at the initial
4
As an example, the closed-form formula for the price of an up and out call under the UVUR model is
reported in Appendix A.

5
 σAT M σRR σV W B
1W 11.75% 0.50% 0.190%
2W 11.60% 0.50% 0.190%
1M 11.50% 0.60% 0.190%
2M 11.25% 0.60% 0.210%
3M 11.00% 0.60% 0.220%
6M 10.87% 0.65% 0.235%
9M 10.83% 0.69% 0.235%
1Y 10.80% 0.70% 0.240%
2Y 10.70% 0.65% 0.255%

Table 1: EUR/USD volatility quotes as of 12 February 2004.

T (in years) P d (0, T ) P f (0, T )

1W 0.0192 0.999804 0.999606
2W 0.0384 0.999595 0.999208
1M 0.0877 0.999044 0.998179
2M 0.1726 0.998083 0.996404
3M 0.2493 0.997187 0.994803
6M 0.5014 0.993959 0.989548
9M 0.7589 0.990101 0.984040
1Y 1.0110 0.985469 0.978479
2Y 2.0110 0.960102 0.951092

Table 2: Domestic and foreign discount factors for the relevant maturities.

time, the following no-arbitrage constraints must be imposed for each t:5
N
X Rt
rid (u) du
λi e− 0 = P d (0, t)
i=1
N
(11)
X Rt f
λi e− 0 ri (u) du = P f (0, t)
i=1

Our calibration is then performed by minimizing the sum of squared percentage differences
between model and market volatilities of the 25∆ puts, ATM puts and 25∆ calls, while
respecting the constraint (11). Given that ε is arbitrarily small, we considered the limit
case ε = 0 in the calculation of option prices (9).6
5
We can safely use the same λ’s both for the domestic and foreign risk-neutral measures, since such
probabilities do not change when changing measure due to the independence between W and (ρd , ρf , σ).
6
We notice that, setting ε = 0, σ0 is no longer an optimization parameter.

6
 10∆p 25∆p 35∆p ATM 35∆c 25∆c 10∆c
1W 11.96% 11.69% 11.67% 11.75% 11.94% 12.19% 12.93%
2W 11.81% 11.54% 11.52% 11.60% 11.79% 12.04% 12.78%
1M 11.60% 11.39% 11.39% 11.50% 11.72% 11.99% 12.77%
2M 11.43% 11.16% 11.15% 11.25% 11.48% 11.76% 12.60%
3M 11.22% 10.92% 10.90% 11.00% 11.23% 11.52% 12.39%
6M 11.12% 10.78% 10.76% 10.87% 11.12% 11.43% 12.39%
9M 11.04% 10.72% 10.71% 10.83% 11.09% 11.41% 12.39%
1Y 11.00% 10.69% 10.68% 10.80% 11.06% 11.39% 12.38%
2Y 11.02% 10.63% 10.60% 10.70% 10.94% 11.28% 12.34%

Table 3: EUR/USD volatility quotes as of 12 February 2004.

Given the high degrees of freedom at hand, we set N = 2 and assumed that the domestic
rate ρd is deterministic and equal to rd , so that the first constraint in (11) is automatically
satisfied. In fact, sticking to only two scenarios and assuming uncertainty only in the asset
volatility σ and foreign rate ρf is sufficient, in the considered case and many others as
well, to achieve a perfect calibration to the three main volatility quotes for all maturities
simultaneously.
To speed up the calibration procedure, we resorted to a non parametric estimate of
functions ρf and σ, assuming rif and σi , i = 1, 2, to be constant over each interval defined
by consecutive market maturities. In such a way, we could apply an iterative procedure
and calibrate one implied volatility curve at a time, starting from the first maturity and up
to the last. Precisely, we set t0 := 0, t1 := 1W , t2 := 2W , t3 := 1M , t4 := 2M , t5 := 3M ,
f
t6 := 6M , t7 := 9M , t8 := 1Y , t9 := 2Y , and denoted by ri,j and σi,j the constant values
f
assumed, respectively, by ri and σi , i = 1, 2, on the intervals [tj−1 , tj ) j = 1, . . . , 9. At each
f
maturity tj , we then optimized over r1,j , σ1,j and σ2,j , which are the only free parameters at
f f
the j-th step appearing in formula (9), given that we expressed r2,j as a function of r1,j by
f f
the second constraint in (11), and also given the previously obtained values r1,1 , . . . , r1,j−1 ,
f f
σ1,1 , . . . , r1,j−1 and σ2,1 , . . . , r1,j−1 .
The perfect fit to three main volatilities for each maturity holds true for many different
specifications of the probability parameter λ1 . We then chose an optimal λ1 by calibrating
the whole implied volatility matrix in Table 3, under the constraint that the three main
quotes are reproduced exactly. We obtained λ1 = 0.625. The values of the other model
parameters are shown in Table 4.
In Table 5 we show our calibration’s errors in absolute terms: the model perfectly fits
the three main volatilities for each maturity and performs quite well for almost every level
of Delta. The performance slightly degenerates for extreme wings. However, the largest
error is quite acceptable, given also that market bid-ask spreads are typically higher.
The perfect calibration to the basic volatility quotes is essential for a Vega breakdown
along the strike and maturity dimensions. This is extremely helpful to traders, since it

7
 15

14

13

12

11

10

90
80
70
60
1 50
2
3 40 Delta
4 30
5
6 20
Maturity 7 10
8
9

Figure 1: EUR/USD implied volatilities (in percentage points) as of 12 February 2004.

allows them to understand where their volatility risk is concentrated. The possibility of
such a Vega break down is a clear advantage of the UVUR model. In general, the calculation
of bucketed sensitivities is neither straightforward nor even possible when we depart from
the BS world. In fact, classical and widely used stochastic-volatility models, like those of
Hull and White (1987) or Heston (1993), can not produce bucketed sensitivities. A trader
is then typically compelled to resort to a dangerous and unnatural parameter hedging or
to an overall Vega hedge based on a parallel shift of the implied volatility surface.
In Section 7 we will show how to calculate a Vega break down and, accordingly, how
to hedge a book of exotic options in terms of plain-vanilla instruments.

5 The forward volatility surfaces

The quality of calibration to implied volatility data is usually an insufficient criterion for
judging the goodness of an alternative to the BS model. In fact, a trader is also interested
in the evolution of future volatility surfaces, which are likely to have a strong impact both
in the pricing and especially in the hedging of exotic options.
Once the deterministic (time-dependent) volatility σ and interest rates ρd and ρf are
drawn at time ε, we know that the model (7) behaves as a BS geometric Brownian motion,
thus leading to flat implied volatility curves for each given maturity. This is certainly a
drawback of the model. However, the situation improves sensibly if we consider forward
implied volatility curves.
A forward implied volatility is defined as the volatility parameter to plug into the BS
formula for forward starting option to match the model price.

8
 f
r1,j σ1,j σ2,j
1W 9.82% 9.23% 15.72%
2W 5.14% 8.96% 15.36%
1M 5.47% 8.90% 15.21%
2W 3.44% 8.26% 15.21%
3W 2.84% 7.79% 14.72%
6M 3.09% 7.92% 15.05%
9M 3.11% 7.96% 14.90%
1Y 2.79% 7.81% 15.13%
2Y 3.02% 7.51% 15.44%

Table 4: Calibrated parameters for each maturity.

10∆p 25∆p 35∆p ATM 35∆c 25∆c 10∆c
1W 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 0.01%
2W 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 0.01%
1M 0.01% 0.00% 0.00% 0.00% 0.01% 0.00% 0.01%
2M 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01%
3M 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 0.01%
6M -0.02% 0.00% 0.01% 0.00% 0.00% 0.00% -0.01%
9M -0.02% 0.00% 0.00% 0.00% 0.00% 0.00% -0.01%
1Y 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
2Y 0.02% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01%

Table 5: Absolute differences (in percentage points) between model and market implied
volatilities.

A forward starting option with forward start date T1 and maturity T2 is an option
where the strike price is set as a proportion α of the spot price at time T1 . In case of a
call, the payoff at time T2 is
[S(T2 ) − αS(T1 )]+
whose BS price at time 0 is
"  
P d (0,T1 )P f (0,T2 ) 1 2
ln αP d (0,T2 )P f (0,T1 ) + 2 σ(T1 , T2 , α) (T2 − T1 )
S0 P f (0, T2 )Φ √ 
σ(T1 , T2 , α) T2 − T1
 # (12)
P d (0,T1 )P f (0,T2 ) 1 2
d
P (0, T2 ) f ln αP d (0,T2 )P f (0,T1 )
− 2
σ(T1 , T 2 , α) (T 2 − T 1 )
−α d P (0, T1 )Φ √  ,
P (0, T1 ) σ(T1 , T2 , α) T2 − T1

where σ(T1 , T2 , α) denotes the forward volatility for the interval [T1 , T2 ] and “moneyness”
α.

9
 12th F ebruary2004 three − monthf wd
1W 11.75% 10.63%
2W 11.60% 10.63%
1M 11.50% 10.63%
2M 11.25% 10.64%
3M 11.00% 10.65%
6M 10.87% 10.66%
9M 10.83% 10.65%
1Y 10.80% 10.63%
2Y 10.70% 10.62%

Table 6: Comparison between ATM implied volatilities as of 12th February 2004 and three
month forward ATM implied volatilities.

In Figure 2 we show the three-month forward volatility surface that is implied by

the previous calibration. Such a surface is the graph of function σ(T1 , T2 , α) for different
values of T2 and α, with T1 set to 0.25 (three months). For a more consistent plot and a
better homogeneity of values, we replaced α with ∆, thus using different α’s for different
maturities. The α for given maturity T2 and ∆ was calculated as the moneyness of the
plain vanilla option with the same ∆ and same time to maturity T2 − T1 . In Table 6 we
compare the ATM volatilities as of 12th February 2004 and the three-month forward ATM
implied volatility. The level of the surface, as is clear from the ATM volatilities, keeps a
regular term structure. The shape of the surface also looks consistent with the initial one.

Similar plots can be obtained by considering different forward start dates T1 . This
provides a strong empirical support to model (7), since its forward volatility surfaces are
regular and realistic in that they do not differ too much from the initial one.
As a further example, in Figure 3 we show the forward evolution of the three-month
implied volatility smile. To this end, we set T2 = T1 + 0.25 and considered the forward
implied volatility curves for T1 ∈ {1W, 2W, 1M, 2M, 3M, 6M, 9M, 1Y, 2Y }. The evolution
is sensible and realistic also in this case: the shape of the smile keeps the features usually
observed in the market.

6 Pricing exotic options

In this section, we will briefly describe the empirical procedure used by many practitioners
in the market to account for implied volatility smiles in the pricing of non-quoted instru-
ments. We will also compare the prices of some exotic options obtained with the market
practice with those coming from the UVUR model with N = 2.
Market practitioners tend to stick to a BS constant-volatility model to price exotic
options, but they also adopt some rules of thumbs, based on hedging arguments, to include

10
 15

14

13

12

11

10

90
80
70
60
1 50
2
3 40 Delta
4 30
5
6 20
Maturity 7 10
8
9

Figure 2: The three-month forward volatility surface.

the volatility surface into the pricing. To cope with a smile-shaped volatility surface,
traders hedge their positions by keeping low exposures not only in classical Greeks like
Delta, Gamma and Vega, but also in some higher order Greeks like the DVegaDvol (a.k.a.
Volga) and the DVegaDSpot (a.k.a. Vanna). The Volga measures the sensitivity of the
Vega of the option with respect to a change in the implied volatility, whereas the Vanna
measures the sensitivity of the Vega with respect to the a change in the underlying spot
price. The Volga can be thought of as a sensitivity with respect to the volatility of the
implied volatility, whereas the Vanna as a sensitivity with respect to the correlation between
the underlying asset and the implied volatility. By setting the Vega, the Vanna and the
Volga of the hedged portfolio equal to zero, traders try to minimize the model risk deriving
from using BS, which is manifestly inconsistent with the reality.
The trader’s procedure for pricing an exotic option can be summarized as follows.
First, he/she prices the option with the BS formula by plugging into it the ATM volatility.
He/she then calculates the option’s Vega, Vanna and Volga. The related exposures can be
hedged by buying and selling appropriate numbers of out-of-the-money and at-the-money
options. Since the most liquid options for each expiry are the ATM calls (or puts) and the
25∆ calls and puts, the three exposures are eventually hedged by means of combinations
of such options. Once the hedging portfolio is built, it is priced with the proper market
implied volatilities, yielding its true market value, and then with a constant at-the-money
volatility. The difference between the two values is added to the BS price of the exotic
option, thus incorporating, via the above hedging procedure, the market smile into the
pricing. This add-on is usually weighted by the survival probability when a barrier option
is involved.
This is as far as market practice is concerned. We now provide two examples showing

11
 13
1w
2w
1m
2m
12.5 3m
6m
9m
1y
2y
12

11.5

11

10.5

10
10 20 30 40 50 60 70 80 90
Delta

Figure 3: The three-month implied volatility smiles starting at different forward times.

that exotic options prices implied by (7) do not significantly depart from those given by
the above procedure. This can be viewed as a further argument supporting the UVUR
model.
The exotic options we consider are two barrier options: an Up&Out call and a Down&Out
put. Valuations are based on the EUR/USD market data as of 31 March 2004 (shown in
Table 7), with the EUR/USD spot rate set at 1.2183. We first price the two options with
the BS model, we then calculate the related adjustments by the market’s rule of thumb
explained above, and finally compare the adjusted prices with those implied by the UVUR
model. In the UVUR model barrier options prices are consistently calculated according
to formula (10), that is we simply use a combination of BS barrier option formulae by
plugging, for each scenario, the integrated volatility corresponding to the claim’s expiry.7
Results are displayed in Table 8. The first option is a EUR call/USD put struck at
1.2250 with knock out at 1.3100, expiring in 6 months. The BS price is 0.0041 US$ and
the adjustment to this theoretical value is positive and equal to 0.0006 US$. The UVUR
model evaluates this option 0.0049 US$. The second option is a EUR put/USD call struck
at 1.2000 and knocked out at 1.0700, expiring in 3 months. The BS price is 0.0169 US$
and, in this case, the market adjustment is negative and equal to -0.0021 US$. The UVUR
price is again very close to that implied by the market’s practice. The model, therefore,
seems to be consistent with market’s adjustments and prices, at least in the EUR/USD
7
This formula is not exact since actual BS barrier option prices depend on the whole term structure
of the instantaneous volatility and not on its mean value only. However, such prices can not be expressed
in closed form and our approximation turns out to be extremely accurate in most FX market conditions.
A complete catalogue of alternative approximations for BS barrier option prices, in presence of a term
structure of volatility, can be found in Rapisarda (2003).

12
 σAT M σRR σV W B P d (0, T ) P f (0, T )
1W 13.50% 0.00% 0.19% 0.9997974 0.9996036
2W 11.80% 0.00% 0.19% 0.9995851 0.9992202
1M 11.95% 0.05% 0.19% 0.9991322 0.9983883
2M 11.55% 0.15% 0.21% 0.9981532 0.9966665
3M 11.50% 0.15% 0.21% 0.9972208 0.9951018
6M 11.30% 0.20% 0.23% 0.9941807 0.9902598
9M 11.23% 0.23% 0.23% 0.9906808 0.9855211
1Y 11.20% 0.25% 0.24% 0.9866905 0.9807808
2Y 11.10% 0.20% 0.25% 0.9626877 0.9550092

Table 7: Market data for EUR/USD as of 31st March 2004.

BS Value BS+Adj UVUR

Up&Out call 0.0041 0.0047 0.0049
Down&Out put 0.0169 0.0148 0.0150

Table 8: UVUR Model prices compared with BS and BS plus market adjustments.

market case.
In presence of steep skews as in the USD/JPY market, however, the accordance between
the market procedure and the UVUR model may worsen considerably. There are in fact
particular combinations of strikes and barrier levels such that the corrections implied by
the two approaches have opposite signs. One may wonder whether this is an indication that
the UVUR model misprices certain derivatives. The answer, however, seems to be negative
in general. In fact, using the Heston (1993) model as a reference, whenever the UVUR
price is significantly different than that implied by the market approach, the Heston price
is definitely more in accordance with the former than the latter. This is another argument
in favor of the UVUR model.
In the next section we show how to use the UVUR model also in the management of
an options book.

7 Hedging a book of exotic options

As pointed out by Brigo, Mercurio and Rapisarda (2004), model (7) can be efficiently used
for the valuation of a whole options book. This is essentially due to the possibility of
pricing analytically most derivatives in the FX market. Our practical experience is that
it takes a few seconds to value a book with 10000 options, half of which exotics, including
the time devoted to calibration. This is an impossible task to achieve with any known
stochastic volatility model.
The consistent valuation of his/her book is not, however, the only concern of an options

13
trader. Hedging is usually an even more important issue to address. In this section, we will
show how to hedge, by means of model (7), changes of a portfolio’s value due to changes
in market volatilities.
From a theoretical point of view, the UVUR model is characterized by market in-
completeness, due to the randomness of the asset’s volatility. In principle, therefore, a
contingent claim can be hedged by means of the underlying asset and a given option. In
practise, however, there are several sources of randomness that are not properly accounted
for in the theory. This is why traders prefer to implement alternative hedging strategies,
like those based on Vega bucketing, as we illustrate in the following.
We already noticed that, under (7), a Vega break down is possible thanks to the model
capability of exactly reproducing the fundamental volatility quotes. The sensitivity of a
given exotic to a given implied volatility is readily obtained by applying the following
procedure. One shifts such a volatility by a fixed amount ∆σ, say ten basis points. One
then fits the model to the tilted surface and calculate the price of the exotic, πN EW ,
corresponding to the newly calibrated parameters. Denoting by πIN I the initial price of
the exotic, its sensitivity to the given implied volatility is thus calculated as:
πN EW − πIN I
∆σ
For a better sensitivity we can also calculate the exotic price under a shift of −∆σ. However,
if ∆σ is small enough (even though not too small), the improvement tends to be negligible.
In practice, it can be more meaningful to hedge the typical movements of the market
implied volatility curves. To this end, we start from the three basic data for each maturity
(the ATM and the two 25∆ call and put volatilities), and calculate the exotic’s sensitivities
to: i) a parallel shift of the three volatilities; ii) a change in the difference between the two
25∆ wings; iii) an increase of the two wings with fixed ATM volatility.8 In this way we
should be able to capture the effect of a parallel, a twist and a convexity movements of the
implied volatility surface. Once these sensitivities are calculated, it is straightforward to
hedge the related exposure via plain vanilla options, namely the ATM calls or puts, 25∆
calls and 25∆ puts for each expiry.
A further approach that can be used for hedging is the classical parameter hedging. In
this case, one calculates the variations of the exotic derivative price with respect to the
parameters of the model, namely the forward volatilities and the foreign forward rates. We
assume that the parameter λ is constant.9
If we have a number n of hedging instruments equal to the number of parameters, we
can solve a linear system Ax = b, where b is a (n × 1) vector with the exotic’s sensitivities
obtained by an infinitesimal perturbation of the n parameters, and A is the (n × n) matrix
whose i-th row contains the variations of the n hedging instruments with respect the i-th
parameter. The instruments we use are, as before, the ATM puts, 25∆ calls and 25∆
8
This is actually equivalent to calculating the sensitivities with respect to the basic market quotes.
9
This can be justified by the fact that λ turns out to mainly accommodate the convexity of the volatility
surface, which, as measured by the butterfly, is typically very stable. Besides, the effect of a change in
convexity is well captured also by the difference between the volatilities in the two scenarios (when N = 2).

14
puts for each expiry. Since the model is able to perfectly fit the price of these hedging
instruments, we have a one to one relation between the sensitivities of the exotic with
respect to the model parameters, and its variations with respect to the hedging instruments.
More formally, denoting by π the exotic option’s price, by p the model parameters’ vector
and by R the market’s data vector, we have:
dπ ∂π ∂π ∂p
= +
dR ∂R ∂p ∂R
∂p
Exact calibration allows therefore an exact calculation of the matrix ∂R .
We now show how the barrier options of the previous section can be hedged in terms
of plain vanillas under both the scenarios and parameter hedging procedures, presenting
also a BS based hedging portfolio for both options. Using again the market data as of 31
March 2004, we assume that both exotics have a nominal of 100,000,000 US$ and calculate
the nominal values of the ATM puts, 25∆ calls and 25∆ puts that hedge them.
Table 9 shows the hedging portfolio suggested by the BS model: the hedging plain
vanilla options have the same expiry as the related barrier option and their quantities are
chosen so as to zero the overall Vega, Vanna and Volga.
In Table 10 we show the hedging quantities calculated according to the UVUR model
with the scenario approach. The expiry of the hedging plain vanilla options is once again
the same as that of the corresponding barrier options. It is noteworthy that both the sign
and order of magnitude of the hedging options are similar to those of the BS model.

25∆ put 25∆ call ATM put

Up&Out call 79,008,643 54,195,790 -127,556,533
Down&Out put -400,852,806 -197,348,566 496,163,095

Table 9: Quantities of plain vanilla options to hedge the barrier options according to the
BS model.

25∆ put 25∆ call ATM put

Up&Out call 76,409,972 42,089,000 -117,796,515
Down&Out put -338,476,135 -137,078,427 413,195,436

Table 10: Quantities of plain vanilla options to hedge the barrier options according to the
UVUR model with the scenario approach.

In the last two Tables 11 and 12 we show the results for the parametric approach.
In this case, the hedging portfolio is made of all the options expiring before or at the
exotic’s maturity, though the amounts are all negligible but the ones corresponding to the
maturity of the barrier option. Also in this case, signs and order of magnitude of the
hedging amounts seem to agree with those obtained under the BS model and the UVUR

15
model with a scenario approach. This should be considered as a further advantage of the
UVUR model, both in terms of market practice and ease of implementation.

25∆ put 25∆ call ATM put

1W 5 49 -34
2W 5 -4 5
1M 21 14 -30
2M -27 -28 43
3M 15 39 -37
6M 77,737,033 44,319,561 -116,151,192

Table 11: Quantities of plain vanilla options to hedge the six-month Up&Out call according
to the UVUR model with the parametric approach.

25∆ put 25∆ call ATM put

1W -11 244 -169
2W -150 -226 288
1M -34 78 -49
2M 24 -6 -19
3M -334,326,734 -145,863,908 397,433,268

Table 12: Quantities of plain vanilla options to hedge the three-month Down&Out put
according to the UVUR model with the parametric approach.

8 Conclusions
Asset price models where the instantaneous volatility is randomly drawn at (an infinitesimal
instant after) the initial time are getting some popularity due to their simplicity and
tractability. We mention, for instance, the recent works of Brigo, Mercurio and Rapisarda
(2004) and Gatarek (2003), who considered an application to the LIBOR market model.
Alternatives where subsequent draws are introduced have been proposed by Alexander,
Brintalos and Nogueira (2003) and Mercurio (2002).
At the same time, these models encounter some natural criticism because of their
very formulation, which seems to make little sense from the historical viewpoint. In this
article, however, we try to demonstrate the validity of the above uncertain volatility models,
focusing in particular on that proposed by Brigo, Mercurio and Rapisarda (2004). We verify
that such a model well behaves when applied to FX market data. Precisely, we show that
it leads to a very good fitting of market volatilities, implies realistic forward volatilities,
and allows for a fast and consistent valuation and hedge of a typical options book.

16
 Our tests on the model are indeed encouraging and may help in addressing the above
natural criticism. We in fact believe that a model should be judged not only in terms of
its assumptions but also in terms of its practical implications.

Appendix A: the price of an up-and-out call

The price at time t = 0 of an up-and-out call (UOC) with barrier level H > S0 , strike K
and maturity T under model (7) is (approximately) given by
N
( " Ã ! Ã !#
X ln S0
+ c 1 + 2c 2 ln S0
+ c 1 + 2c2
UOC0 =1{K<H} λi S0 ec1 +c2 +c3 Φ K
√ −Φ H
√
i=1
2c 2 2c2
" Ã ! Ã !#
ln SK0 + c1 ln SH0 + c1 S0 2
− Kec3 Φ √ −Φ √ − Hec3 +(β−1)(ln H +c1 )+(β−1) c2
2c2 2c2
" Ã ! Ã !#
ln SH0 + c1 + 2(β − 1)c2 ln SH0 K
2 + c1 + 2(β − 1)c2
· Φ √ −Φ √
2c2 2c2
" Ã ! Ã !# )
S0 S0 K
S0 2 ln + c 1 + 2βc 2 ln 2 + c 1 + 2βc 2
+ Kec3 +β(ln H +c1 )+β c2 Φ H
√ −Φ H
√ ,
2c2 2c2
(13)
where 1{A} denotes the indicator function of the set A, and
c1 = ci1 := Rid (0, T ) − Rif (0, T ) − 12 Vi2 (0, T )
c2 = ci2 := 12 Vi2 (0, T )
c2 = ci3 := −Rid (0, T )
RT d
i 0
[Ri (t, T ) − Rif (t, T ) − 12 Vi2 (t, T )]Vi2 (t, T ) dt
β = β := −2 RT 4
0
Vi (t, T ) dt
Z T
Rix (t, T ) := rix (s) ds, x ∈ {d, f },
t
Z T
Vi2 (t, T ) := σi2 (s) ds
t

For a thorough list of formulas we refer to Rapisarda (2003).10

References
[1] Alexander, C., Brintalos, G., and Nogueira, L. (2003) Short and Long Term Smile
Effects: The Binomial Normal Mixture Diffusion Model. ISMA Centre working paper.
10
These formulas, including the above (13), are only approximations, since no closed-form formula is
available for barrier option prices under the BS model with time-dependent coefficients.

17
[2] Black, F. and Scholes, M. (1973) The Pricing of Options and Corporate Liabilities.
Journal of Political Economy 81, 637-659.

[3] Brigo, D. and Mercurio, F. (2000) A mixed-up smile. Risk September, 123-126.

[4] Brigo, D., Mercurio, F., and Rapisarda, F. (2004) Smile at the uncertainty. Risk
17(5), 97-101.

[5] Castagna, A., and Mercurio, F. (2004) Consistent Pricing of FX Derivatives. Internal
report. Banca IMI, Milan.

[6] Gatarek, D. (2003) LIBOR market model with stochastic volatility. Deloitte&Touche.
Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=359001

[7] Heston, S. (1993) A Closed Form Solution for Options with Stochastic Volatility with
Applications to Bond and Currency Options. Review of Financial Studies 6, 327-343.

[8] Hull, J. and White, A. (1987) The Pricing of Options on Assets with Stochastic
Volatilities. Journal of Financial and Quantitative Analysis 3, 281-300.

[9] Mercurio, F. (2002) A multi-stage uncertain-volatility model. Internal report. Banca

IMI, Milan. Available at http://www.fabiomercurio.it/UncertainVol.pdf

[10] Rapisarda, F. (2003) Pricing barriers on underlyings with time-

dependent parameters. Banca IMI internal report. Available at
http://it.geocities.com/rapix/TimeDependentBarriers.pdf

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