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4 The model of Hoggard, Whalley & Wilmott (1992)

4 The model of Hoggard, Whalley & Wilmott (1992)

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Part Five advanced topics



where this has been evaluated at time t and asset value S. I have not given the details, but this

choice minimizes the risk of the portfolio, as measured by the variance, to leading order. After

a time step δt and rehedging, the number of assets we hold is

∂V

(S + δS, t + δt).

∂S

Note that this is evaluated at the new time and asset price. We can subtract the former from the

latter to find the number of assets we have traded to maintain a ‘hedged’ position. The number

of asset traded is therefore

ν=



∂V

∂V

(S + δS, t + δt) −

(S, t).

∂S

∂S



Since the time step and the asset move are both small we can apply Taylor’s theorem to expand

the first term on the right-hand side:

∂ 2V

∂V

∂V

∂ 2V

(S + δS, t + δt) =

(S, t) + δS 2 (S, t) + δt

(S, t) + · · · .

∂S

∂S

∂S

∂S∂t

Since δS = σ Sφ δt 1/2 + O(δt), the dominant term is that which is proportional to δS; this term

is O(δt 1/2 ) and the other terms are O(δt). To leading order the number of assets bought or

sold is

ν≈



∂ 2V

∂ 2V

(S,

t)

δS



σ Sφ δt 1/2 .

∂S 2

∂S 2



We don’t know beforehand how many shares will be traded, but we can calculate the expected

number, and hence the expected transaction costs. The expected transaction cost over a time

step is

E [κS|ν|] =



2

∂ 2V

κσ S 2

δt 1/2 ,

π

∂S 2



(48.2)





where the factor 2/π is the expected value of |φ|. We can now calculate the expected change

in the value of our portfolio from (48.1), including the usual Black–Scholes terms and also the

new cost term:

E[δ ] =



∂V

∂ 2V

2 ∂ 2V

+ 12 σ 2 S 2 2 − κσ S 2

∂t

∂S

π δt ∂S 2



δt.



(48.3)



Except for the modulus sign, the new, non-Black–Scholes, term in the above is of the same

form as the second S derivative that has appeared before; it is a gamma term, multiplied by

the square of the asset price, multiplied by a constant.

Now assuming that the holder of the option expects to make as much from his portfolio as if he

had put the money in the bank, then we can replace the E[δ ] in (48.3) with r(V − S(∂V /∂S)) δt



transaction costs Chapter 48



as before to yield an equation for the value of the option:



∂V

∂ 2V

2 ∂ 2V

+ 12 σ 2 S 2 2 − κσ S 2

∂t

∂S

π δt ∂S 2

∂V

+ rS

− rV = 0.

∂S



(48.4)



There is a nice financial interpretation of the term that is not

present in the usual Black–Scholes equation. The second derivative of the option price with

respect to the asset price, the gamma, = ∂ 2V /∂S 2 , is a measure of the degree of mishedging

of the hedged portfolio. The leading-order component of randomness is proportional to δS

and this has been eliminated by delta-hedging. But this delta hedging leaves behind a small

component of risk proportional to the gamma. The gamma of an option or portfolio of options

is related to the amount of rehedging that is expected to take place at the next rehedge and

hence to the expected transaction costs.

The equation is a non-linear parabolic partial differential equation, one of the first such in

finance. It is obviously also valid for a portfolio of derivative products. This is one of the first

times in this book that we distinguish between single options and a portfolio of options. But for

much of the rest of the book this distinction will be important. In the presence of transaction

costs, the value of a portfolio is not the same as the sum of the values of the individual

components. We can best see this by taking a very extreme example.

We have positions in two European call options with the same strike price and the same

expiry date and on the same underlying asset. One of these options is held long and the other

short. Our net position is therefore exactly zero because the two positions exactly cancel each

other out. But suppose that we do not notice the cancelation effect of the two opposite positions

and decide to hedge each of the options separately. Because of transaction costs we lose money

at each rehedge on both options. At expiry the two payoffs will still cancel, but we have a

negative net balance due to the accumulated costs of all the rehedges in the meantime. This

contrasts greatly with our net balance at expiry if we realize beforehand that our positions

cancel. In the latter case we never bother to rehedge, leaving us with no transaction costs and

a net balance of zero at expiry.

Now consider the effect of costs on a single vanilla option held long. We know that

∂ 2V

>0

∂S 2

for a single call or put held long in the absence of transaction costs. Postulate that this is true

for a single call or put when transaction costs are included. If this is the case then we can drop

the modulus sign from (48.4). Using the notation

σˇ 2 = σ 2 − 2κσ



2

.

π δt



(48.5)



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the equation for the value of the option is identical to the Black–Scholes value with the exception that the actual variance σ 2 is replaced by the modified variance σˇ 2 . Thus our assumption

that ∂ 2V /∂S 2 > 0 is true for a single vanilla option even in the presence of transaction costs.

The modified volatility will be recognized as the Leland volatility correction mentioned at the

start of this chapter.

For a short call or put option position we simply change all the signs in the above analysis

with the exception of the transaction cost term, which must always be a drain on the portfolio.

We then find that the call or put is valued using the new variance

σˆ 2 = σ 2 + 2κσ



2

.

π δt



(48.6)



Again this is the Leland volatility correction.

The results (48.5) and (48.6) show that a long position in a single call or put with costs

incorporated has an apparent volatility that is less than the actual volatility. When the asset

price rises the owner of the option must sell some assets to remain delta hedged. But then the

effect of the bid-offer spread on the underlying is to reduce the price at which the asset is sold.

The effective increase in the asset price is therefore less than the actual increase, being seen as

a reduced volatility. The converse is true for a short option position.

The above volatility adjustments are applicable when you have an option or a portfolio of

options having a gamma of one sign. If the gamma is always and everywhere positive use the

lower volatility value for a long position, and the higher value for a short position. If gamma

is always and everywhere negative, swap these values around.

For a single vanilla call or put, we can get some idea of the total transaction costs associated

with the above strategy by examining the difference between the value of an option with the

cost-modified volatility and that with the usual volatility; that is, the difference between the

Black–Scholes value and the value of the option taking into account the costs. Consider

V (S, t) − Vˆ (S, t),

where the hatted function is Black–Scholes with the modified volatility. Expanding this expression for small κ we find that it becomes

(σ − σˆ )



∂V

+ ···.

∂σ



This is proportional to the vega of the option. We know the formula for a European call option

and therefore we find the expected spread to be



2κSN (d1 ) (T − t)

,



2π δt

where N (d1 ) has its usual meaning.

The most important quantity appearing in the model is

K=



κ

.

σ δt 1/2



(48.7)



transaction costs Chapter 48



K is a non-dimensional quantity, meaning that it takes the same value whatever units are

used for measuring the parameters. If this parameter is very large, we write K

1, then the

transaction costs term is much greater than the underlying volatility. This means that costs are

high and that the chosen δt is too small. The portfolio is being rehedged too frequently. If the

transaction costs

√ are very large or the portfolio is rehedged very often then it is possible to

have κ > σ / 8 π δt. In this case the equation becomes forward parabolic for a long option

position. Since we are still prescribing final data, the equation is ill-posed. Although the asset

price may have risen, its effective value due to the addition of the costs will have actually

dropped. I discuss such ill posedness later.

If the parameter K is very small, we write K

1, then the costs have only a small effect on

the option value. Hence δt could be decreased to minimize risk. The portfolio is being rehedged

too infrequently.

We can see how to use this result in practice if we have data for the bid-offer spread, volatility

and time between rehedges for a variety of stocks. Plot the parameter κ/σ against the quantity

1/δt 1/2 for each stock. An example of this for a real portfolio is shown in Figure 48.1. In this

figure are also shown lines on which K is constant. To be consistent in our attitude towards

transaction costs across all stocks we might decide that a value of K = K is ideal. If this

0.025



A

C



0.02



B



0.015



G



H



κ/σ



I



D



E



F



J

K

L



0.01



N



O

Q



M

P



R

0.005



1/(dt)1/2



0

0



2



4



6



8



10



12



14



Figure 48.1 The parameter κ/σ against 1/δt1/2 for a selection of stocks. On each curve the

transaction cost parameter K is the same.



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is the bold line in the figure then options on those stocks above the line, such as A, are too

infrequently hedged, while those below, such as R, are hedged too often. Of course, this is a

very simple approach to optimizing a hedging strategy. A more sophisticated approach would

also take into account the advantage of increased hedging: The reduction of risk.



48.5 NON-SINGLE-SIGNED GAMMA

For an arbitrary portfolio of options, the gamma, ∂ 2V /∂S 2 , is not of one sign. If this is the case

then we cannot drop the modulus sign. Since the Hoggard–Whalley–Wilmott equation is non

linear we must in general solve it numerically.

In Figures 48.2 and 48.3 is shown the value of a long bull spread consisting of one long call

with E = 45 and one short call with E = 55 and the delta at six months before expiry for the

two cases, with and without transaction costs. The volatility is 20% and the interest rate 10%.

In this example K = 0.25. The bold curve shows the values in the presence of transaction costs

and the other curve in the absence of transaction costs. The latter is simply the Black–Scholes

value for the combination of the two options. The bold line approaches the other line as the

transaction costs decrease.

In Figures 48.4 and 48.5 is shown the value of a long butterfly spread and its delta, before

and at expiry. In this example the portfolio contains one long call with E = 45, two short calls

with E = 55 and another long call with E = 65. The results are with one month until expiry

for the two cases, with and without transaction costs. The volatility, the interest rate and K are

as in the previous example.

12



10

Value without costs

Value with costs



8



V



790



6



4



2



0

0



20



40



60



80



100



S



Figure 48.2 The value of a bull spread with (bold) and without transaction costs. The payoff is also

shown.



transaction costs Chapter 48



1.2



1



Delta without costs

Delta with costs



Delta



0.8



0.6



0.4



0.2



0

0



20



40



60



80



100



S



Figure 48.3 The delta for a bull spread prior to and at expiry with (bold) and without transaction

costs.

12



10

Value without costs

Value with costs



V



8



6



4



2



0

0



20



40



60



80



100



S



Figure 48.4 The value of a butterfly spread with (bold) and without transaction costs.



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Part Five advanced topics



Delta without costs

Delta with costs



0.8



0.3

Delta



792



−0.2



20



30



40



50



60



70



80



90



S



−0.7



−1.2



Figure 48.5 The delta for a butterfly spread with (bold) and without transaction costs.



48.6 THE MARGINAL EFFECT OF TRANSACTION COSTS

Suppose we hold a portfolio of options, let’s call its value P (S, t), and we want to add another

option to this portfolio. What will be the effect of transaction costs? Call the value of the new,

larger, portfolio P + V : What equation is satisfied by the marginal value V ? This is not just

the cost equation applied to V because of the nonlinearity of the problem. We can write

∂P

∂ 2P

2 ∂ 2P

∂P

+ rS

+ 12 σ 2 S 2 2 − κσ S 2

− rP = 0

∂t

∂S

π δt ∂S 2

∂S

since the original portfolio must satisfy the costs equation. But now the new portfolio also

satisfies this equation:

∂(P + V ) 1 2 2 ∂ 2 (P + V )

+ 2σ S

− κσ S 2

∂t

∂S 2

+ rS



2 ∂ 2 (P + V )

π δt

∂S 2



∂(P + V )

− r(P + V ) = 0.

∂S



Both of these equations are non linear. But if the size of the new option is much less than the

original portfolio we can linearize the latter equation to examine the equation satisfied by the

marginal value V . We find that

∂V

∂ 2P

∂ 2V

+ 12 σ 2 S 2 2 − sgn

∂t

∂S

∂S 2



κσ S 2



∂V

2 ∂ 2V

+ rS

− rV = 0.

2

π δt ∂S

∂S



transaction costs Chapter 48



This equation is now linear. The important point to note is that the volatility correction only

depends on the sign of the gamma of the original portfolio, P . If the gamma of the original

portfolio is positive then the addition of another contract with positive gamma only makes the

cost situation worse. However, the addition of a new contract with negative gamma will reduce

the level of transaction costs. The benefits of reducing gamma may even make it worthwhile

to buy/sell a contract for more/less than the Black–Scholes value, theoretically.



48.7



OTHER COST STRUCTURES



The above model can be extended to accommodate other transaction cost structures. Suppose

that the cost of buying or selling ν assets is κ(ν, S). We follow the analysis of Section 48.4

up to the point where we take expectations of the transaction cost of hedging. The number of

assets traded is still proportional to the gamma, and the expected cost of trading is

E κ σ S δt 1/2 φ, S



.



The option pricing equation then becomes

1

∂V

∂ 2V

∂V

+ 12 σ 2 S 2 2 + rS

− rV = E κ σ S δt 1/2 φ, S

∂t

∂S

∂S

δt



.



For example, suppose that trading in shares costs k1 + k2 ν + k3 νS, where k1 , k2 and k3 are

constants. This cost structure contains fixed costs (k1 ), a cost proportional to volume traded

(k2 ν), and a cost proportional to the value traded (k3 νS). The option value satisfies the non-linear

diffusion equation

k1

∂V

∂V

∂ 2V

+ 12 σ 2 S 2 2 + rS

− rV =

+

∂t

∂S

∂S

δt



48.8



2

∂ 2V

.

σ S(k2 + k3 S)

π δt

∂S 2



(48.8)



HEDGING TO A BANDWIDTH: THE MODEL

OF WHALLEY & WILMOTT (1993) AND

HENROTTE (1993)



We have so far seen how to model option prices when hedging takes place at fixed intervals

of time. Another commonly used strategy is to rehedge whenever the position becomes too

far out of line with the perfect hedge position. Prices are therefore monitored continuously but

hedging still has to take place discretely.

Due to the complexity of this problem and those that follow, I only give a brief sketch of

the ideas and results.

With V (S, t) as the option value, the perfect Black–Scholes hedge is given by

=



∂V

.

∂S



Suppose, however, that we are not perfectly hedged, that we hold −D of the underlying

asset but do not want to accept the extra cost of buying or selling to reposition our hedge. The



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risk, as measured by the variance over a time step δt of this imperfectly hedged position is, to

leading order,

σ 2S2 D −



∂V

∂S



2



δt.



I can make two observations about this expression. The first is simply to confirm that when

D = ∂V /∂S this variance is zero. The second observation is that a natural hedging strategy is

to bound the variance within a given tolerance and that this strategy is equivalent to restricting

D so that

∂V

(48.9)

σS D −

≤ H0 .

∂S

The parameter H0 is now a measure of the maximum expected risk in the portfolio. When

the perfect hedge (∂V /∂S) and the current hedge (D) move out of line so that (48.9) is violated,

then the position should be rebalanced. Equation (48.9) defines the bandwidth of the hedging

position.

The model of Whalley & Wilmott (1993) and Henrotte (1993) takes this as the hedging

strategy: The investor prescribes H0 and on rehedging rebalances to D = ∂V /∂S.

We find that the option value satisfies the non-linear diffusion equation





1

2

2

2 4 2

H

∂V

∂V

∂V

σ S

k1 + (k2 + k3 S) 0  ,

(48.10)

+ 12 σ 2 S 2 2 + rS

− rV =

∂t

∂S

∂S

H0

S

where is the option’s gamma and the parameters k1 , k2 and k3 are the cost parameters for

the cost structure introduced in Section 48.7. Note that again there is a non-linear correction to

the Black–Scholes equation that depends on the gamma.



48.9 UTILITY-BASED MODELS

48.9.1



The Model of Hodges & Neuberger (1989)



All of the above models for transaction costs take the hedging strategy as exogenously given.

That is, the investor chooses his strategy and then prices his option afterwards. Strategies like

this have been called local-in-time because they only worry about the state of an option at the

present moment in time. An alternative, first examined by Hodges & Neuberger (1989), is to

find a strategy that is in some sense optimal. These have been called global-in-time models

because they are concerned with what may happen over the rest of the life of the option.

The seminal work in this area, combining both utility theory and transaction costs, was by

Hodges & Neuberger (HN), with Davis, Panas & Zariphopoulou (DPZ) making improvements

to the underlying philosophy. HN explain that they assume that a financial agent holds a

portfolio that is already optimal in some sense but then has the opportunity to issue an option

and hedge the risk using the underlying. However, since rehedging is costly, they must define

their strategy in terms of a ‘loss function.’ They thus aim to maximize expected utility. This

entails the investor specifying a ‘utility function.’ The case considered in most detail by HN and

DPZ is of the exponential utility function. This has the nice property of constant risk aversion.

Mathematically, such a problem is one of stochastic control and the differential equations

involved are very similar to the Black–Scholes equation.



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