An Introduction to Financial Option Valuation_4 pot

We see that for this particular asset path, the call option stays out-of-the-money asset price below E until just after t = 1, andthen makes a number of excursions in/out-of-the-money be

More on hedging

O U T L I N E

• practical illustration of hedging

• behaviour of delta near expiry

• Long-Term Capital Management

9.1 Motivation

The hedging idea that was used to derive the Black–Scholes PDE forms the mostimportant concept in this book In this chapter, we therefore take time out to re-iterate the steps involved and develop the process into an algorithm that can beillustrated numerically

9.2 Discrete hedging

Having found the explicit formulas (8.19) and (8.24), we may differentiate with

respect to S to obtain the required asset holding A i in (8.10) This partial derivative

∂V/∂ S is called the delta of an option, and the hedging strategy that we discussed

is known as delta hedging Performing the differentiation leads to

Returning to the delta hedging process, we know from (8.7) that i+1, the value

of the portfolio at t i + δt, satisfies

 i+1= A i S i+1+ (1 + rδt)D i (9.3)

87

The asset holding is rebalanced to A i+1and in order to compensate, the cash

ac-count is altered to D i+1 Since no money enters or leaves the system, the new

portfolio value, A i+1S i+1+ D i+1, must equal i+1in (9.3), so

D i+1 = (1 + rδt)D i + (A i − A i+1)S i+1. (9.4)

We may summarize the overall hedging strategy as follows

Set A0= ∂V0/∂ S, D0= 1 (arbitrary), 0= A0S0+ D0

For each new time t = (i + 1)δt

Observe new asset price S i+1

Compute new portfolio value i+1 in (9.3)

Compute A i+1 =∂V i+1

∂ S Compute new cash holding D i+1 in (9.4)

New portfolio value is A i+1S i+1+ D i+1

end

More precisely, this strategy is discrete hedging as the rebalancing act is done

at times i δt Because we cannot let δt → 0 in practice, there will be some error in

the risk elimination

For the purpose of illustration, it is possible to simulate an asset path and plement discrete hedging To write down the resulting algorithm, we use{ξ i} todenote samples from anN(0, 1) pseudo-random number generator that are used in

im-simulating the asset path, and we letδt = T/N.

end

To describe the next set of experiments, it is convenient to use some financial

jargon At time t, a European call option is said to be

9.3 Delta at expiry 89order for a positive payoff to ensue At-the-money defines the boundary betweenin- and out-of-the-money.

Computational example Here we implement the discrete hedging simulation

above for a European call option with S0= 1, E = 1.5, µ = 0.055, r = 0.05,

T = 5 and δt = 10−2, so N = 500 The upper plot in Figure 9.1 displays theparticular discrete asset path (t i , S i ), for t i = iδt, that arose The strike price

E is shown as a dashed line We see that for this particular asset path, the call option stays out-of-the-money (asset price below E) until just after t = 1, andthen makes a number of excursions in/out-of-the-money before giving a verysmall payoff at expiry The upper-middle plot shows the deltas, (t i , ∂C i /∂ S),

along the asset path This shows the time-varying amount of asset held in theportfolio The lower-middle plot gives the cash level(t i , D i ) and the solid curve

in the lower plot gives the portfolio value(t i ,  i ) The idea behind delta hedging

is to guarantee that the portfolio C −  grows at the risk-free interest rate It

follows that

(S(t), t) = C(S(t), t) − (C(S0, 0) − (S0, 0)) e r t (9.5)

should hold To test this, we computed the right-hand side of (9.5) at each time t i,

using the Black–Scholes formula (8.19) to compute C (S i , t i ) Every tenth value

has been plotted as a circle in the lower picture.1The circles appear to lie on top

of the  i curve, so (9.5) is approximated well The discrepancy in (9.5) at theexpiry date,

C(S(T ), T ) − (S(T ), T ) − (C(S0, 0) − (S0, 0)) e r T


, (9.6)was found to be 0.0364 Reducing δt to 10−4 (and hence computing a differentasset path), we found that this discrepancy was lowered to 0.0029. ♦

Computational example In Figure 9.2 we repeat the computation in Figure 9.1

with E set to the value 2 5 In this case the option finishes out-of-the-money.

Again we observe from the lower picture that (9.5) is close to being exact ♦

9.3 Delta at expiry

Looking carefully at Figures 9.1 and 9.2 we see that

• in the first experiment, where the option expires in-the-money, the delta approaches the value 1 at expiry, whereas

1 Plotting every value would make the picture too cluttered.

dis-• in the second experiment, where the option expires out-of-the-money, the delta proaches the value 0 at expiry.

ap-This is no accident Using the characterization (9.1), some analysis shows that

see Exercise 9.3 Hence, the delta always finishes at 1 for options that expire

in-the-money and 0 for options that expire out-of-the-in-the-money If S (t) ≈ E for times close

to expiry, then the delta is liable to swing wildly between values at≈ 1 (when S(t) goes above E) and ≈ 0 (when S(t) dips below E) Our next experiment illustrates

this effect

Computational example Here we repeat the computation that produced

Figures 9.1 and 9.2 with the strike price reset to E = 1.9, so that the option

frequently jumps in/out-of-the-money near expiry Figure 9.3 shows that the responding delta value lurches dramatically as expiry is approached ♦

The delta behaviour near expiry that was observed in Figures 9.1 to 9.3, and is

encapsulated in (9.7), has a simple financial interpretation For t ≈ T there is little

time left for the asset value to change – if it is currently in/out-of-the-money then itwill probably remain in/out-of-the-money In particular, if the call option is in-the-money then any upward or downward movement in the asset corresponds almostdirectly to the same upward or downward movement in the payoff In other words,the call option and the asset are very highly correlated – they share the same risk.Since the portfolio is designed to replicate the risk in the option, it follows that itwill hold approximately 1 unit of asset, so i ≈ 1 Conversely, if the call option isout-of-the-money close to expiry then the payoff is very likely to be zero whateverhappens to the asset – there is no risk, so we should not be holding any asset.The analogous results to (9.7) for a European put option are

Up-9.4 Large-scale test

We finish with an experiment that looks at the success of discrete hedging over alarge number of sample paths, and also illustrates that the option value is indepen-dent of the drift parameter,µ, in the asset price model.

5, r = 0.05 and σ = 0.3, with T = 3 We computed 500 discrete asset paths

with time-spacingsδt = 10−2 The upper picture in Figure 9.4 plots S (T ) on the

horizontal axis against

(S(T ), T ) + (P(S0, 0) − (S0, 0)) e r T (9.9)

on the vertical axis for the case µ = 0.2 There are 500 such points, one for each asset path We computed P (S0, 0) in (9.9) from the Black–Scholes formula

(8.24) If the discrete hedging is successful, then an analogous identity to (9.5)

holds for P (S(t), t) In particular, it holds at expiry, so (9.9) should agree with

the put payoff max(E − S(T ), 0) This ‘hockey stick’ payoff curve is

superim-posed as a dashed line We see that the dots lie close to the dashed line, andhence the discrete hedging algorithm behaves as predicted The lower picture in

9.5 Long-Term Capital Management 93

µ = 0.4

Fig 9.4 Large-scale discrete hedging example for a European put Dots sent normalized final payoff (9.9) for 500 asset paths Exact hockey stick payoff is superimposed as a dashed line Upper picture,µ = 0.2 Lower picture, µ = 0.4.

repre-Figure 9.4 shows the same computations withµ changed to 0.4 This illustrates

the phenomenon that the option value does not depend uponµ. ♦

9.5 Long-Term Capital Management

There are many instances of academics with an expertise in mathematical financeturning their hands to real-life trading The most high-profile and, ultimately,sobering example involves Long-Term Capital Management (LTCM) This was

a hedge fund that invested money supplied by its partners and a limited number ofwealthy clients Two of the partners, closely involved in day-to-day trading strate-gies, were Robert Merton and Myron Scholes – founding fathers of the ‘rocketscience’ of option valuation theory The fund, set up in 1994, was extremely suc-cessful at raising capital and for a period of around four years produced impres-sively high returns Although sometimes referred to as an arbitrage unit, LTCM

typically scoured the international markets looking for low risk opportunities to make relatively small percentage gains The fund used leverage – investing bor-

rowed money – to scale up these tiny margins into large profits One commentatorlikened their trades to ‘picking up nickels in front of bulldozers’ (Lowenstein,

2001, page 102) At the peak of the fund’s success, Merton and Scholes received

their Nobel Prizes However, in mid-1998 a combination of extreme events in themarket plunged LTCM into deep trouble One of the key difficulties they then

faced was illiquidity LTCM became desperate to offload a vast range of

com-plicated portfolios, but the small set of potential buyers were, quite reasonably,holding out in the expectation that prices would drop further (The assumption ofliquidity – there always being a ready supply of buyers and sellers – is implicit inthe Black–Scholes theory.) The bulldozers were moving in The decline of LTCMand the enormity of its potential debts were brought to the attention of The Fed-eral Reserve Bank of New York (the Fed), a major component of the US FederalReserve System Quite remarkably, the Fed became concerned that bankruptcy ofLTCM could create such a hole that the overall stability of the market was at threat.Very rapidly, the Fed managed to persuade a consortium of major banks and in-vestment houses to bail out LTCM in order to prevent the very real possibility of atotal meltdown of the financial system.1

Overall, a dollar invested in LTCM grew to a height of around $2.85, butdropped sharply to a paltry 23 cents, and the partners lost personal fortunes Afast-paced and highly informative account of the LTCM debacle, with input from

a number of first-hand witnesses, is given in (Lowenstein, 2001)

an excellent means to alleviate their exposure to risk, and another large group whosee options as a great way to speculate on the market On the other side there is acomplementary group of well-connected players, with the resources to manipulatecomplicated portfolios and negotiate relatively small transaction costs, who arewilling to accept the Black–Scholes value plus a small premium

E X E R C I S E S

9.1.  Show from (9.1) and (9.2) that ∂C/∂ S > 0 and ∂ P/∂ S < 0.

1 Lowenstein (Lowenstein, 2001, page 198) quotes Sandy Warner from J P Morgan: ‘Boys, we’re going to a picnic and the tickets cost $250 million’.

9.6 Notes 95

%CH09 Program for Chapter 9

%

% Illustrates delta hedging by computing an approximate

% replicating portfolio for a European call

%

% Portfolio is ‘asset’ units of asset and an amount ‘cash’ of cash

% Plot actual and theoretical portfolio values

S = zeros(N,1); asset = zeros(N,1); cash = zeros(N,1);

portfolio = zeros(N,1); Value = zeros(N,1);

9.2.  By making reference to the limit definition

give an intuitive reason why∂C/∂ S ≥ 0 Do the same for ∂ P/∂ S ≤ 0.

9.3.  Using the expression (9.1), confirm the limiting behaviour for

∂C(S, t)/∂ S displayed in (9.7).

9.4.  Using the expression (9.2), confirm the limiting behaviour for

∂ P(S, t)/∂ S displayed in (9.8).

9.5.  Give a financial argument that explains why ∂ P(S, t)/∂ S → −1 at expiry

for an in-the-money put option and∂ P(S, t)/∂ S → 0 at expiry for an

out-of-the-money put option

9.7 Program of Chapter 9 and walkthrough

Our program ch09 implements a discrete hedging simulation and produces a picture like the lower

plots in Figures 9.1–9.3 It is listed in Figure 9.5 Here, S, asset, Value and cash are N by 1 arrays whose i th entries store the asset price, asset holding, Black–Scholes option value and cash holding at

time t(i), respectively After initializing parameters, we set up a for loop that updates the portfolio

as described in Section 9.2 The Black–Scholes function ch08 from Chapter 8 is used to find the option value and the delta.

On exiting the loop we superimpose the left- and right-hand sides of (9.5), plotting at every fifth time point.

The professors were brilliant at reducing a trade to pluses and minuses;

they could strip a ham sandwich to its component risks;

but they could barely carry on a normal conversation.

R O G E R L O W E N S T E I N (Lowenstein, 2001) After closing about 200 000 option transactions

(that is separate option tickets)

over 12 years and studying about 70 000 risk management reports,

I felt that I needed to sit down and reflect on the thousands

of mishedges I had committed.

N A S S I M T A L E B (Taleb, 1977)

9.7 Program of Chapter 9 and walkthrough 97

It is probably safe to say that

the derivatives industry would be stuck in the psychedelic 60s,

and many talented mathematicians would still be

teaching freshman algebra for $20,000 a year

had Black, Scholes, and Merton not made their contribution.

DON M CHANCE, ‘Rethinking Implied Volatility’ Financial Engineering News, January/

February 2003.

• Examining the signs of the derivatives gives insights into the underlying formulas.

• The derivative ∂V/∂ S is needed in the delta hedging process.

• The derivative ∂V/∂σ comes into play in Chapter 14, where we compute the implied

By differentiating C in (8.19), using (8.20) and (8.21), it is possible to find

explicit expressions for these quantities Before launching into this process wemake note of two useful facts First, it follows from (3.18) that

10.4 Black–Scholes PDE solution 101

As before, (10.1) allows us to cancel terms, and we find that

ρ = (T − t)Ee −r(T −t) N (d2). (10.4)Similar analysis shows that

10.3 Interpreting the Greeks

It is possible to interpret some of the Greek formulas from a financial viewpointand to check that they agree with intuition

First we recall that the limiting behaviour of delta was characterized and preted in Section 9.3 We also know from Exercise 9.1 that > 0 up to expiry.

inter-This makes sense, because an increase in the asset price increases the likely profit

at expiry

From (10.4) we see that ρ > 0 before expiry To explain this we note that creasing the interest rate is equivalent to lowering the exercise price E (The value

in-of a fixed amount E at some fixed time in the future becomes less if the interest

rate increases.) This makes a payoff more likely, which increases the value of theoption

The expression (10.5) shows that  < 0 This property could also be deduced

directly from the general, asset-model-independent argument in Section 2.6 cerning the monotonicity of the time-zero call option value with respect to theexpiry date, see Exercise 10.5

con-The vega in (10.6) is always positive before expiry This can be understood byconsidering that an increase in volatility leads to a wider spread of asset prices.However, assets moving deeper out-of-the-money have no effect on the optionprice (the payoff remains zero) while assets moving deeper into-the-money lead to

a greater payoff Because of this asymmetry, increasingσ has a net positive effect.

We return to vega in Chapter 14

10.4 Black–Scholes PDE solution

Having worked out the partial derivatives, we are in a position to confirm that

C (S, t) in (8.19) satisfies the Black–Scholes PDE (8.15) Using our expressions