# An Introduction To Computational Finance Without Agonizing Pain

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An Introduction to Computational FinanceWithout Agonizing Painc Peter Forsyth 2022P.A. Forsyth May 30, 2022Contents1 The First Option Trade42 The2.12.22.32.42.52.6Black-Scholes EquationBackground . . . . . . . . . . . . . . . . . . . .Definitions . . . . . . . . . . . . . . . . . . . . .A Simple Example: The Two State Tree . . . .A hedging strategy . . . . . . . . . . . . . . . .Brownian Motion . . . . . . . . . . . . . . . . .Geometric Brownian motion with drift . . . . .2.6.1 Ito’s Lemma . . . . . . . . . . . . . . .2.6.2 Some uses of Ito’s Lemma . . . . . . . .2.6.3 Some more uses of Ito’s Lemma . . . . .2.6.4 More on GBM with constant coefficients2.6.5 Integration by Parts . . . . . . . . . . .2.7 The Black-Scholes Analysis . . . . . . . . . . .2.8 Hedging in Continuous Time . . . . . . . . . .2.9 The option price . . . . . . . . . . . . . . . . .2.10 American early exercise . . . . . . . . . . . . .3 The Risk Neutral World4 Monte Carlo Methods4.1 Monte Carlo Error Estimators . . . .4.2 Random Numbers and Monte Carlo4.3 The Box-Muller Algorithm . . . . .4.3.1 An improved Box Muller . .4.4 Speeding up Monte Carlo . . . . . .4.5 Estimating the mean and variance .4455661214151517181819202021. Cheriton School of Computer Science,University of Waterloo, Waterloo, Ontario, Canada,paforsyt@uwaterloo.ca, www.scicom.uwaterloo.ca/ paforsyt, tel: (519) 888-4567x34415, fax: (519) 885-12081.N2L.232525272830313G1,

4.64.74.84.95 The5.15.25.35.45.55.6Low Discrepancy Sequences . . . . . . . . . . .Correlated Random Numbers . . . . . . . . . .Integration of Stochastic Differential Equations4.8.1 The Brownian Bridge . . . . . . . . . .4.8.2 Strong and Weak Convergence . . . . .Matlab and Monte Carlo Simulation . . . . . .Binomial Model: OverviewA Binomial Model Based on the Risk Neutral Walk . . .A No-arbitrage Lattice . . . . . . . . . . . . . . . . . . .A Drifting Lattice . . . . . . . . . . . . . . . . . . . . .5.3.1 Numerical Comparison: No-arbitrage Lattice andSmoothing the Payoff . . . . . . . . . . . . . . . . . . .5.4.1 Richardson extrapolation . . . . . . . . . . . . .Matlab Implementation . . . . . . . . . . . . . . . . . .5.5.1 American Case . . . . . . . . . . . . . . . . . . .5.5.2 Discrete Fixed Amount Dividends . . . . . . . .5.5.3 Discrete Dividend Example . . . . . . . . . . . .Dynamic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Drifting. . . . . . . . . . . . . . . . . . . . . . . . . . . . .323335353739. . . . . . . . . . . . .Lattice. . . . . . . . . . . . . . . . . . . . . . . . . . . . .4242444648495353555657586 More on Ito’s Lemma597 Derivative Contracts on non-traded Assets7.1 Derivative Contracts . . . . . . . . . . . . .7.2 A Forward Contract . . . . . . . . . . . . .7.2.1 Convenience Yield . . . . . . . . . .7.2.2 Volatility of Forward Prices . . . . .and Real. . . . . . . . . . . . . . . . . . . . .Options. . . . . . . . . . . . . . . . . . . . .6263666767.68686972737475777778. . . . . . . . . . .Diffusion. . . . . . . . . . . . . . . . . . . . . . . . . .7981838487888989918 Discrete Hedging8.1 Delta Hedging . . . . . . . . . . . . . . . . . . . . . . . . . . .8.2 Gamma Hedging . . . . . . . . . . . . . . . . . . . . . . . . .8.3 Vega Hedging . . . . . . . . . . . . . . . . . . . . . . . . . . .8.4 A Stop-Loss Strategy . . . . . . . . . . . . . . . . . . . . . . .8.4.1 Profit and Loss: probability density, VAR and CVAR8.4.2 Another way of computing CVAR . . . . . . . . . . .8.5 Collateralized deals . . . . . . . . . . . . . . . . . . . . . . . .8.5.1 Hedger . . . . . . . . . . . . . . . . . . . . . . . . . . .8.5.2 Buyer B . . . . . . . . . . . . . . . . . . . . . . . . . .9 Jump Diffusion9.1 The Poisson Process . . . . . . . . . .9.2 The Jump Diffusion Pricing Equation9.3 An Alternate Derivation of the Pricing9.4 Simulating Jump Diffusion . . . . . . .9.4.1 Compensated Drift . . . . . . .9.4.2 Contingent Claims Pricing . . .9.5 Matlab Code: Jump Diffusion . . . . .9.6 Poisson Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Equation for Jump. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10 Regime Switching.922

11 Mean Variance Portfolio Optimization11.1 Special Cases . . . . . . . . . . . . . .11.2 The Portfolio Allocation Problem . . .11.3 Adding a Risk-free asset . . . . . . . .11.4 Criticism . . . . . . . . . . . . . . . .11.5 Individual Securities . . . . . . . . . .93. 95. 95. 98. 100. 10012 Some Investing Facts12.1 Stocks for the Long Run? . . . . . . . . . . . . . . . . . . .12.1.1 GBM is Risky . . . . . . . . . . . . . . . . . . . . . .12.2 Volatility Pumping . . . . . . . . . . . . . . . . . . . . . . .12.2.1 Constant Proportions Strategy . . . . . . . . . . . .12.2.2 Leveraged Two Times Bull/Bear ETFs . . . . . . .12.3 More on Volatility Pumping . . . . . . . . . . . . . . . . . .12.3.1 Constant Proportion Portfolio Insurance . . . . . . .12.3.2 Covered Call Writing . . . . . . . . . . . . . . . . . .12.3.3 Stop Loss, Start Gain . . . . . . . . . . . . . . . . .12.4 Target Date: Ineffectiveness of glide path strategies . . . . .12.4.1 Extension to jump diffusion case . . . . . . . . . . .12.4.2 Dollar cost averaging . . . . . . . . . . . . . . . . . .12.5 Bootstrap Resampling . . . . . . . . . . . . . . . . . . . . .12.5.1 Data and Investment Portfolio . . . . . . . . . . . .12.5.2 Investment scenario . . . . . . . . . . . . . . . . . .12.5.3 Deterministic Strategies . . . . . . . . . . . . . . . .12.5.4 Criteria for Success . . . . . . . . . . . . . . . . . . .12.5.5 Bootstrap results . . . . . . . . . . . . . . . . . . . .12.6 Maximizing Sharpe ratios . . . . . . . . . . . . . . . . . . .12.6.1 Numerical Examples . . . . . . . . . . . . . . . . . .12.6.2 Deficiencies of mean-variance (Sharpe ratio) criteria12.7 Capitalization Weight vs. Equal Weighted Indexes . . . . .12.7.1 Stochastic Dominance . . . . . . . . . . . . . . . . .12.7.2 Expected Shortfall . . . . . . . . . . . . . . . . . . .12.7.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . .12.7.4 Scenario . . . . . . . . . . . . . . . . . . . . . . . . .12.7.5 Bootstrap results . . . . . . . . . . . . . . . . . . . .12.7.6 What about taxes and distributions? . . . . . . . . .12.7.7 Summary: equal weight vs. cap weight . . . . . . . 2512512612612913113213213513513513613913913 Further Reading14013.1 General Interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14013.2 More Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14013.3 More Technical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1403

Stock Price 22Option Price 1Stock Price 20Stock Price 18Option Price 0Figure 2.1: A simple case where the stock value can either be 22 or 18, with a European call option, K 21.2.2DefinitionsLet’s consider some simple European put/call options. At some time T in the future (the expiry or exercisedate) the holder has the right, but not the obligation, to Buy an asset at a prescribed price K (the exercise or strike price). This is a call option. Sell the asset at a prescribed price K (the exercise or strike price). This is a put option.At expiry time T , we know with certainty what the value of the option is, in terms of the price of theunderlying asset S,Payoff max(S K, 0) for a callPayoff max(K S, 0) for a put(2.1)Note that the payoff from an option is always non-negative, since the holder has a right but not an obligation.This contrasts with a forward contract, where the holder must buy or sell at a prescribed price.2.3A Simple Example: The Two State TreeThis example is taken from Options, futures, and other derivatives, by John Hull. Suppose the value of astock is currently 20. It is known that at the end of three months, the stock price will be either 22 or 18.We assume that the stock pays no dividends, and we would like to value a European call option to buy thestock in three months for 21. This option can have only two possible values in three months: if the stockprice is 22, the option is worth 1, if the stock price is 18, the option is worth zero. This is illustrated inFigure 2.1.In order to price this option, we can set up an imaginary portfolio consisting of the option and the stock,in such a way that there is no uncertainty about the value of the portfolio at the end of three months. Sincethe portfolio has no risk, the return earned by this portfolio must be the risk-free rate.Consider a portfolio consisting of a long (positive) position of δ shares of stock, and short (negative) onecall option. We will compute δ so that the portfolio is riskless. If the stock moves up to 22 or goes downto 18, then the value of the portfolio isValue if stock goes up 22δ 1Value if stock goes down 18δ 05(2.2)

So, if we choose δ .25, then the value of the portfolio isValue if stock goes up 22δ 1 4.50Value if stock goes down 18δ 0 4.50(2.3)So, regardless of whether the stock moves up or down, the value of the portfolio is 4.50. A risk-free portfoliomust earn the risk free rate. Suppose the current risk-free rate is 12%, then the value of the portfolio todaymust be the present value of 4.50, or4.50 e .12 .25 4.367The value of the stock today is 20. Let the value of the option be V . The value of the portfolio is20 .25 V 2.44.367V .633A hedging strategySo, if we sell the above option (we hold a short position in the option), then we can hedge this position inthe following way. Today, we sell the option for .633, borrow 4.367 from the bank at the risk free rate (thismeans that we have to pay the bank back 4.50 in three months), which gives us 5.00 in cash. Then, webuy .25 shares at 20.00 (the current price of the stock). In three months time, one of two things happens The stock goes up to 22, our stock holding is now worth 5.50, we pay the option holder 1.00, whichleaves us with 4.50, just enough to pay off the bank loan. The stock goes down to 18.00. The call option is worthless. The value of the stock holding is now 4.50, which is just enough to pay off the bank loan.Consequently, in this simple situation, we see that the theoretical price of the option is the cost for the sellerto set up portfolio, which will precisely pay off the option holder and any bank loans required to set up thehedge, at the expiry of the option. In other words, this is price which a hedger requires to ensure that thereis always just enough money at the end to net out at zero gain or loss. If the market price of the optionwas higher than this value, the seller could sell at the higher price and lock in an instantaneous risk-freegain. Alternatively, if the market price of the option was lower than the theoretical, or fair market value, itwould be possible to lock in a risk-free gain by selling the portfolio short. Any such arbitrage opportunitiesare rapidly exploited in the market, so that for most investors, we can assume that such opportunities arenot possible (the no arbitrage condition), and therefore that the market price of the option should be thetheoretical price.Note that this hedge works regardless of whether or not the stock goes up or down. Once we set up thishedge, we don’t have a care in the world. The value of the option is also independent of the probability thatthe stock goes up to 22 or down to 18. This is somewhat counterintuitive.2.5Brownian MotionBefore we consider a model for stock price movements, let’s consider the idea of Brownian motion with drift.Suppose X is a random variable, and in time t t dt, X X dX, wheredX αdt σdZwhere αdt is the drift term, σ is the volatility, and dZ is a random term. The dZ term has the form dZ φ dt6(2.4)(2.5)

where φ is a random variable drawn from a normal distribution with mean zero and variance one (φ N (0, 1),i.e. φ is normally distributed).If E is the expectation operator, thenE(φ2 ) 1 .E(φ) 0(2.6)Now in a time interval dt, we haveE(dX) E(αdt) E(σdZ) αdt ,(2.7)and the variance of dX, denoted by V ar(dX) isV ar(dX) E([dX E(dX)]2 ) E([σdZ]2 ) σ 2 dt .(2.8)Let’s look at a discrete model to understand this process more completely. Suppose that we have adiscrete lattice of points. Let X X0 at t 0. Suppose that at t t,X0 X0 h ;with probability pX0 X0 h ;with probability q(2.9)where p q 1. Assume that X follows a Markov process, i.e. the probability distribution in the future depends only on where it isnow. The probability of an up or down move is independent of what happened in the past. X can move only up or down h.At any lattice point X0 i h, the probability of an up move is p, and the probability of a down move is q.The probabilities of reaching any particular lattice point for the first three moves are shown in Figure 2.2.Each move takes place in the time interval t t t.Let X be the change in X over the interval t t t. ThenE( X) 2(p q) h p( h)2 q( h)2E([ X] ) ( h)2 ,(2.10)so that the variance of X is (over t t t)V ar( X) E([ X]2 ) [E( X)]2 ( h)2 (p q)2 ( h)2 4pq( h)2 .(2.11)Now, suppose we consider the distribution of X after n moves, so that t n t. The probability of j upmoves, and (n j) down moves (P (n, j)) isP (n, j) n!pj q n jj!(n j)!7(2.12)

p3X 0 3 hp2X 0 2 hX 0 hp3p2qX02pqX 0 - h3pq2qX 0 - 2 hq2X 0 - 3 h3qFigure 2.2: Probabilities of reaching the discrete lattice points for the first three moves.which is just a binomial distribution. Now, if Xn is the value of X after n steps on the lattice, thenE(Xn X0 )V ar(Xn X0 ) nE( X) nV ar( X) ,(2.13)which follows from the properties of a binomial distribution, (each up or down move is independent ofprevious moves). Consequently, from equations (2.10, 2.11, 2.13) we obtainE(Xn X0 ) n(p q) ht(p q) h tV ar(Xn X0 ) n4pq( h)2t4pq( h)2 t(2.14)Now, we would like to take the limit at t 0 in such a way that the mean and variance of X, after afinite time t is independent of t, and we would like to recoverdX αdt σdZE(dX) αdtV ar(dX) σ 2 dt(2.15) as t 0. Now, since 0 p, q 1, we need to choose h Const t. Otherwise, from equation (2.14)we get that V ar(Xn X0 ) is either 0 or infinite after a finite time. (Stock variances do not have either ofthese properties, so this is obviously not a very interesting case).8

Let’s choose h σ t, which gives (from equation (2.14))σt(p q) t t4pqσ 2E(Xn X0 ) V ar(Xn X0 )Now, for E(Xn X0 ) to be independent of t as t 0, we must have (p q) Const. t(2.16)(2.17)If we choosep qα tσ (2.18)we getp q 1[1 21[1 2α t]σα t]σ(2.19)Now, putting together equations (2.16-2.19) givesE(Xn X0 )V ar(Xn X0 ) αtα2 t)σ2; t 0 . tσ 2 (1 tσ 2(2.20)Now, let’s imagine that X(tn ) X(t0 ) Xn X0 is very small, so that Xn X0 ' dX and tn t0 ' dt, sothat equation (2.20) becomesE(dX)V ar(dX) α dt σ 2 dt .(2.21)which agrees with equations (2.7-2.8). Hence, in the limit as t 0, we can interpret the random walk forX on the lattice (with these parameters) as the solution to the stochastic differential equation (SDE)dX α dt σ dZ dZ φ dt.(2.22)Consider the case where α 0, σ 1, so that dX dZ ' Z(ti ) Z(ti 1 ) Zi Zi 1 Xi Xi 1 .Now we can writeZ tXdZ lim(Zi 1 Zi ) (Zn Z0 ) .(2.23)0 t 0iFrom equation (2.20) (α 0, σ 1) we haveE(Zn Z0 )V ar(Zn Z0 ) 0 t.(2.24)Now, if n is large ( t 0), recall that the binomial distribution (2.12) tends to a normal distribution. Fromequation (2.24), we have that the mean of this distribution is zero, with variance t, so that(Zn Z0 ) N (0, t)Z tdZ .09(2.25)

RtIn other words, after a finite time t, 0 dZ is normally distributed with mean zero and variance t (the limitof a binomial distribution is a normal distribution). Recall that have that Zi Zi 1 t with probability p and Zi Zi 1 t with probability q.Note that (Zi Zi 1 )2 t, with certainty, so that we can write(Zi Zi 1 )2 ' (dZ)2 t .(2.26)To summarize We can interpret the SDEdX α dt σ dZ dZ φ dt.(2.27)as the limit of a discrete random walk on a lattice as the timestep tends to zero. V ar(dZ) dt, otherwise, after any finite time, the V ar(Xn X0 ) is either zero or infinite. We can integrate the term dZ to obtainZtdZ Z(t) Z(0)0 N (0, t) .(2.28)Going back to our lattice example, note that the total distance traveled over any finite interval of timebecomes infinite,E( X ) h(2.29)so that the the total distance traveled in n steps isn h t h ttσ t(2.30)which goes to infinity as t 0. Similarly, x t .(2.31)Consequently, Brownian motion is very jagged at every timescale. These paths are not differentiable, i.e.does not exist, so we cannot speak ofE(dx)dtdxdt(2.32)but we can possibly defineE(dx).dt(2.33)We can verify that taking the limit as t 0 on the discrete lattice converges to the normal density.Consider the data in Table 2.1. The random walk on the lattice was simulated using a Monte Carlo approach.Starting at X0 , the particle was moved up with probability p (2.19), and down with probability (1 p).A random number was used to determine the actual move. At the next node, this was repeated, until weobtain the position of X after n steps, Xn . This is repeated many times. We can then determine the meanand variance of these outcomes (see Table 2.2). The mean and variance of eX have also been included, sincethis is relevant for the case of Geometric Brownian Motion, which will be studied in the next Section. Ahistogram of the outcomes is shown in Figure 2.5.The Matlab M file used to generate the walk on the lattice is given in Algorithm 2.34.10

TσαXinitNumber of simulationsNumber of timesteps1.0.2.10050000400Table 2.1: Data used in simulation of discrete walk on a lattice.VariableX(T)eX(T )Mean0.10093.22813Standard Deviation0.200351.1286Table 2.2: Test results: discrete lattice walk, data in Table 2.1.Probability Density: Discrete Walk on a Lattice2.52Normal Density1.510.50 2 1.5 1 0.500.511.52XFigure 2.3: Normalized histogram of discrete lattice walk simulations. Normal density with mean .1, standarddeviation .2 also shown.11

Vectorized M file For Lattice Walkfunction [X new] walk sim( N sim,N,.mu, T, sigma, X init)%% N simnumber of simulations% Nnumber of timesteps% X init initial value%Texpiry time%sigmavolatility%mudrift%%lattice factors%%delt T/N;% timestep sizeup sigma*sqrt(delt);down - sigma*sqrt(delt);p 1./2.*( 1. mu/sigma*sqrt( delt ) );X new zeros(N sim,1);X new(1:N sim,1) X init;ptest zeros(N sim, 1);for i 1:N % timestep loop% now, for each timestep, generate info for% all simulationsptest(:,1) rand(N sim,1);ptest(:,1) (ptest(:,1) p);% 1 if up move% 0 if downmoveX new(:,1) X new(:,1) ptest(:,1)*up (1.-ptest(:,1))*down;% end of generation of all data for all simulations% for this timestepend % timestep loop(2.34)2.6Geometric Brownian motion with driftOf course, the actual path followed by stock is more complex than the simple situation described above.More realistically, we assume that the relative changes in stock prices (the returns) follow Brownian motionwith drift. We suppose that in an infinitesimal time dt, the stock price S changes to S dS, wheredS µdt σdZSwhere µ is the drift rate, σ is the volatility, and dZ is the increment of a Wiener process, dZ φ dt(2.35)(2.36)where φ N (0, 1). Equations (2.35) and (2.36) are called geometric Brownian motion with drift. So,superimposed on the upward (relative) drift is a (relative) random walk. The degree of randomness is given12

10001000900900Low Volatility Caseσ .20 per year800700Asset Price ( )Asset Price ( )700600500400Risk FreeReturn3006005004002001001000246810Risk FreeReturn3002000High Volatility Caseσ .40 per year80001202Time (years)4681012Time (years)Figure 2.4: Realizations of asset price following geometric Brownian motion. Left: low volatility case; right:high volatility case. Risk-free rate of return r .05.by the volatility σ. Figure 2.4 gives an illustration of ten realizations of this random process for two differentvalues of the volatility. In this case, we assume that the drift rate µ equals the risk free rate.Note thatE(dS) E(σSdZ µSdt) µSdtsince E(dZ) 0(2.37)and that the variance of dS isV ar[dS] E(dS 2 ) [E(dS)]2 E(σ 2 S 2 dZ 2 ) σ 2 S 2 dt(2.38)so that σ is a measure of the degree of randomness of the stock price movement.Equation (2.35) is a stochastic differential equation. The normal rules of calculus don’t apply, since forexampledZdt1 φ dt as dt 0 .The study of these sorts of equations uses results from stochastic calculus. However, for our purposes, weneed only one result, which is Ito’s Lemma (see Derivatives: the theory and practice of financial engineering,by P. Wilmott). Suppose we have some function G G(S, t), where S follows the stochastic process equation(2.35), then, in small time increment dt, G G dG, where G G σ 2 S 2 2 G G dt σSdZ(2.39)dG µS2 S2 S t SAn informal derivation of this result is given in the following section.13

2.6.1Ito’s LemmaWe give an informal derivation of Ito’s lemma (2.39). Suppose we have a variable S which followsdS a(S, t)dt b(S, t)dZ(2.40)where dZ is the increment of a Weiner process.Now sincedZ 2 φ2 dt(2.41)where φ is a random variable drawn from a normal distribution with mean zero and unit variance, we havethat, if E is the expectation operator, thenE(φ2 ) 1E(φ) 0(2.42)so that the expected value of dZ 2 isE(dZ 2 ) dt(2.43)Now, it can be shown (see Section 6) that in the limit as dt 0, we have that φ2 dt becomes non-stochastic,so that with probability onedZ 2 dt as dt 0(2.44)Now, suppose we have some function G G(S, t), thendS 2 .2(2.45) (adt b dZ)2 a2 dt2 ab dZdt b2 dZ 2(2.46)dG GS dS Gt dt GSSNow (from (2.40) )(dS)2 Since dZ O( dt) and dZ 2 dt, equation (2.46) becomes(dS)2 b2 dZ 2 O((dt)3/2 )(2.47)(dS)2 b2 dt as dt 0(2.48)orNow, equations(2.40,2.45,2.48) givedG GS dS Gt dt GSSdS 2 .2 GS (a dt b dZ) dt(Gt GSS GS b dZ (aGS GSSb2)2b2 Gt )dt2(2.49)So, we have the result that ifdS a(S, t)dt b(S, t)dZ(2.50)and if G G(S, t), thenb2 Gt )dt2Equation (2.39) can be deduced by setting a µS and b σS in equation (2.51).dG GS b dZ (a GS GSS14(2.51)

2.6.2Some uses of Ito’s LemmaSuppose we havedS µdt σdZ .(2.52)If µ, σ Const., then this can be integrated (from t 0 to t t) exactly to giveS(t) S(0) µt σ(Z(t) Z(0))(2.53)and from equation (2.28)Z(t) Z(0) N (0, t)(2.54)Note that when we say that we solve a stochastic differential equation exactly, this means that we havean expression for the distribution of S(T ).Suppose instead we use the more usual geometric Brownian motiondS µSdt σSdZ(2.55)Let F (S) log S, and use Ito’s LemmadF FS SσdZ (FS µS FSS (µ σ2 S 2 Ft )dt2σ2)dt σdZ ,2(2.56)so that we can integrate this to getF (t) F (0) (µ σ2)t σ(Z(t) Z(0))2(2.57)σ2)t σ(Z(t) Z(0))] .2(2.58)or, since S eF ,S(t) S(0) exp[(µ Unfortunately, these cases are about the only situations where we can exactly integrate the SDE (constantσ, µ).2.6.3Some more uses of Ito’s LemmaWe can often use Ito’s Lemma and some algebraic tricks to determine some properties of distributions. LetdX a(X, t) dt b(X, t) dZ ,(2.59)then if G G(X), thendG b2aGX Gt GXX dt GX b dZ .2(2.60)If E[X] X̄, then (b(X, t) and dZ are independent)E[dX] d E[S] dX̄ E[a dt] E[b] E[dZ] E[a dt] ,15(2.61)

so thatd X̄ E[a] ādt Z ta dt .X̄ E(2.62)0Let Ḡ E[(X X̄)2 ] var(X), thendḠ E [dG] E[2(X X̄)a 2(X X̄)ā b2 ] dt E[2b(X X̄)]E[dZ] E[b2 dt] E[2(X X̄)(a ā) dt] ,(2.63)which means thatt Z Z t2(a ā)(X X̄) dt .b dt E2Ḡ var(X) E(2.64)00In a particular case, we can sometimes get more useful expressions. IfdS µS dt σS d

Stock Price 20 Stock Price 22 Option Price 1 Stock Price 18 Option Price 0 Figure 2.1: A simple case where the stock value can either be 22 or 18, with a European call option, K

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