DYNAMIC HEDGING STRATEGIES Dynamic Hedging Strategies In this article, the authors use the Black-Scholes option pricing model to simulate hedging strategies for portfolios of derivatives and other assets. by Simon Benninga and Zvi Wiener dynamic hedging strategy typically involves two 1. A SIMPLE EXAMPLE positions: To start off, consider the following example, which we A have adapted from Hull (1997): A ®nancial institution has Í A static position in a security or a commitment by a sold a European call option for $300,000. The call is writ- ®rm. For example: ten on 100,000 shares of a non-dividend paying stock with the following parameters: ± A ®nancial institution has written a call on a stock or a portfolio; this call expires some time in the future Current stock price = $49 and is held by a counterparty which has the choice Strike price = X = $50 of either exercising it or not. Stock volatility = 20% Risk-free interest rate r =5%. ± A U.S. ®rm has committed itself to sell 1000 widgets Option time to maturity T = 20 weeks at some de®ned time in the future; the receipts will Stock expected return = 13% be in German marks. The Black-Scholes price of this option is slightly over Í An offsetting position in a ®nancial contract. Typically, $240,000 and can be calculated using the Mathematica this counter-balancing position is adjusted when market program de®ned in our previous article: conditions change; hence the name dynamic hedging strategy: In[1]:= Clear[snormal, d1, d2, bsCall]; snormal[x_]:= [ [ ]] + ; To hedge its written call, the issuing ®rm decides to buy 1/2*Erf x/Sqrt 2. 0.5 d1[s_, x_, sigma_, T_, r_]:= shares of the underlying stock or portfolio. The number (Log[s/x]+(r + sigmaˆ2/2)*T)/ of shares purchased at time t will depend on the price (sigma*Sqrt[T]); of the underlying stock at t and on the amount of time d2[s_, x_, sigma_, T_, r_]:= remaining until the expiration of the call. Another way of d1[s, x, sigma, T, r] - sigma*Sqrt[T]; [ ]:= viewing this is that the amount of stock held against the bsCall s_, x_, sigma_, T_, r_ s*snormal[d1[s, x, sigma, T, r]] - call position depends on the probability that the option x*Exp[-(r*T)]* will be exercised. snormal[d2[s, x, sigma, T, r]]; To hedge its anticipated receipt of German marks, the bsPut[s_, x_, sigma_, T_, r_]:= [ ]+ ®rm might enter into a forward contract. This would be bsCall s, x, sigma, T, r x*Exp[-(r*T)] -s; a static hedge position. On the other hand, it might buy bsCall[49, 50, 0.2, 20/52, 0.05]*100000. a put option on Deutschmarks (DM), which would give it the right to sell DM for a given dollar price; this is Out[1]= 240053. still a static hedge position, though somewhat more so- It follows that the ®nancial institution has earned ap- phisticated. A third possibility would be to dynamically proximately $60,000 from writing the call. However, un- simulate a put on the DM position, replicating it by hold- less the ®nancial institution hedges its obligation in order ing amounts of dollars and DM (these amounts changing to offset the effects of price changes in the future, it could with market conditions). stand to lose much money at the call's expiration. For ex- In this article we consider some simple dynamic hedg- ample, if the price of the stock at the option's expiration ing strategies and show how to simulate them in Mathe- date is $60, and if the call is not hedged, then the ®nan- matica. Our discussion will lead us naturally to the con- cial institution will lose $1,000,000 (=100,000*(60-50)) at sideration of Value-at-Risk (VaR), a measure of possible the option's expiration. future losses which has become increasingly popular. As we shall show, however, this possible future loss can Vol. 7 No. 1 1998 Mathematicain Education and Research 1 DYNAMIC HEDGING STRATEGIES be offset by a judicious hedging strategy. In the succeed- debt[numPer]=debt[numPer-1]*Exp[r*length] - ing sections we consider a number of hedging strategies. stockPosition[numPer-1]*price[numPer]+ numOptions*Max[price[numPer] - strike,0]; debt[numPer] 2. A STOP-LOSS STRATEGY ]; [ ] One strategy which could be followed is to purchase the In this simple program: price t simulates a series shares only if the calls are in the money. This strategy of random prices, starting with initPrice. stockPo- looks something like the following: Initially, the bank sells sition is a function which is either 100 (if the current [] the calls for $300,000. Since the current share price of $49 stock price is greater than the strike price) or 0. debt is less than the call exercise price of $50, the calls are out is a function which is negative if the ®nancial institution of the money. The bank then invests the $300,000 in the has money invested in riskless bonds and positive if it 5% bonds for one week (the current hedging period). At has borrowings (so that at the terminal date negative debt the end of the week there are two possibilities: means pro®ts for the ®rm). The program returns the terminal debt of the ®rm [ ] Í If current stock price is < $50, reinvest the proceeds debt numPer . The parameter nn of the stopLoss from the $300,000 (now worth $300,000*Exp[0.05/20] = function is used to set the random seed either to be ®xed $300,751 ) into bonds, letting them earn a further 5% (when nn is different from zero) or to be chosen by the annually for another week. program. If we wish to be able to repeat the same results in multiple runs we use nn different from zero. Running Í If the current stock price is > $50, buy 100,000 shares, this simulation 100 times produces the following result: fully covering the call. Pay for these shares by selling In[2]:= (* number of periods *) the $300,751 of bonds and borrowing the rest of the numPer=20; money (at 5% annually). (*length of each period *) length = 1/52; (* initial price of stock *) At the end of the second week: initPrice = 49; sigma = 0.2; Í If the current stock price is > $50, continue to hold onto r = 0.05; the 100,000 shares, re®nancing the net debt for another mu = 0.15; ( ) week at 5%. * number of options written * numOptions = 100; strike = 50; Í If current stock price is < $50, sell all the shares and (* financial institution gets put the proceeds into bonds earning 5% annually. for writing calls *) receivedForCalls = 300; We can write a Mathematica program which simulates tbl=Table[stopLoss[n],{n,1,100}]; {Mean[tbl],StandardDeviation[tbl]} this strategy: Out[2]= {-38.7918, 183.951} Needs["Statistics‘Master‘"] This means that±for the particular series of random Clear[stopLoss] numbers generated with random seeds 1,...,100 and with numPer=20; (* number of periods *) weekly rebalancing of the position±the average gain of length = 1/52; (*length of each period *) the bank is $38,792 (with standard deviation of $183,951). initPrice = 49; (* initial price of stock *) sigma = 0.2;r = 0.05;mu = 0.15; It is interesting to plot the results of all 100 of these sim- numOptions = 100; (* number of options written *) ulations: = ; strike 50 In[3]:= ListPlot[tbl, PlotJoined -> True, (* financial institution gets PlotRange -> All]; for writing calls *) receivedForCalls = 300; 400 stopLoss[nn_]:= Module[{price,stockPosition}, 200 If[nn!=0, SeedRandom[nn],SeedRandom[]]; price[0]=initPrice; price[t_]:=price[t]= 20 40 60 80 100 price[t-1]* Exp[(mu-sigmaˆ2/2)*length + -200 sigma*Sqrt[length]* Random[NormalDistribution[0,1]]]//N; stockPosition[t_]:= If[price[t]<strike,0,100]; debt[0]=stockPosition[0]-receivedForCalls; The results of the simulation are, of course, sensitive debt[t_]:=debt[t-1]*Exp[r*length]+ to the random numbers generated. For example, if we re- (stockPosition[t]-stockPosition[t-1])*price[t]; place tbl=Table[stopLoss[n+30],{n,1,100}] by 2 Mathematicain Education and Research Vol. 7 No. 1 1998 DYNAMIC HEDGING STRATEGIES tbl=Table[stopLoss[n+30],{n,1,100}],weget Analytic Delta as output {-50.4517,183.548}. The analytic expression for the delta of an option (its sensitivity to the changes in the underlying price) is well 3. A SIMPLE EXAMPLE OF DELTA HEDGING known. We use Mathematica to derive this formula. First In this section, we hedge this position by using a delta we wish to explain why the naive D[bsCall[s, x, hedge (in Section 4 we shall explain why this hedge is T, r, sigma],s] does not work. In the de®nition of better than the stop-loss strategy illustrated in the previous bsCall we have used two numeric values, 2. and 0.5. section). In a delta hedge, we hedge the call position by As soon as a numeric value appears the whole function ∂C purchasing ∂S shares, where C is the call price. In the is numeric and not analytic, and it cannot be simpli®ed. next section we shall illustrate a dynamic delta hedge, but A slight variation on the original de®nition allow us to in this chapter we concentrate on a static hedge. To hedge differentiate bsCall and bsPut: the position, we buy ∂C = 52,160 shares. The hedge ratio ∂S In[7]:= Clear[snormal,d1,d2,bsCall]; was calculated using Mathematica: snormal[x_]:= Erf[x/Sqrt[2]]/2 + 1/2; In[4]:= Clear[delta] d1[s_, x_, sigma_, T_, r_]:= delta[s_, x_, sigma_, T_, r_]:= (Log[s/x]+(r+sigmaˆ2/2)*T)/(sigma*Sqrt[T]); snormal[d1[s, x, sigma, T, r]]; d2[s_, x_, sigma_, T_, r_]:= delta[49, 50, 0.2, 20/52, 0.05]*100000 d1[s, x, sigma, T, r]-sigma*Sqrt[T]; bsCall[s_, x_, sigma_, T_,r_]:= Out[4]= 52160.5 s*snormal[d1[s,x,sigma,T,r]]- Note that we have used an analytical equation for x*Exp[-r*T]*snormal[d2[s,x,sigma,T,r]]; delta, instead of using the derivative: delta[s_, x_, bsPut[s_,x_,sigma_,T_,r_]:= T_, r_, sigma_]:=D[bsCall[s1,x,T,r,sigma], bsCall[s,x,sigma,T,r]+x*Exp[-r*T] -s; s1] /.