Risk Neutral Valuation, the Black-

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1 Risk Neutral Valuation, the Black- Scholes Model and Monte Carlo Stephen M Schaefer London Business School Credit Risk Elective Summer 01 C = SN( d )-PV( X ) N( ) N he Black-Scholes formula 1 d (.) : cumulative standard normal distribution ln( S / PV ( X )) 1 d1 = + σ and d = d1 σ σ Objectives: to understand he Black-Scholes formula in terms of risk-neutral valuation How to use the risk-neutral approach to value assets using Monte Carlo (next week: the binomial method) Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo

2 Valuing Options using Risk Neutral Probabilities In a complete market we can calculate: unique risk neutral probabilities (and A-D prices) the no-arbitrage price of an option as the risk-neutral expected pay-off, discounted at the riskless rate 1 C = 1 + r f E ˆ( payoff ) s= 1 Payoff on call S 1 = ˆ π smax( S X,0), for a call 1+ r f ˆ π : RN probability of state s s Black-Scholes formula can be obtained in exactly this way Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 3 Definition: Lognormal Distribution If a random variable x has a normal distribution with mean µ and standard deviation σ then: 1 x e has a lognormal distribution with mean e µ + σ probability den nsity stock price Returns mean (mu) 10% SD (sig) 40% Current value S_0 100 ime Period (years) 4 Mean S_0*Exp(mu +.5 *sig^) Current value * Exp(mu) A lognormal distribution (like the normal) has two parameters can take these as mean µ and standard deviation σ (of x ) Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 4

3 Assumptions of the Black-Scholes Model he continuously compounded rate of return (CCR) on the underlying stock over a length of time has a normal distribution (if the expected return is constant over time) C S 1 R = = µ σ + σ u ln ( ) S 0 µ is the expected value of the CCR calculated from the expected stock price (the expected return over a very short period dt ) σ is the volatility of the CCR per year, so σ is the volatility of CCR over the period of length u is a normally distributed random variable with a mean of zero and standard deviation equal to one. Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 5 Assumptions of the Black-Scholes Model Since the continuously compounded rate of return (CCR) has a normal distribution the stock price itself (S ) is lognormal C R ( µ σ ) + σ u S = S e = S e and the mean and variance of R 1 µ σ σ C C ( ) ( ) and var ( ) E R = R = From the properties of the lognormal distribution this means that the expected future value of the stock price is: C 1 ( ) var C 1 1 C E R + ( R ) ( µ σ ) + σ E S = S E e = S e = S e = S e R ( ) ( ) So the expected stock price just grows at the rate µ CORREC! C are: µ Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 6

4 Why does setting the expected value of the continuously compounded return equal to the true (continuously compounded) expected return give the wrong answer? he reason is that the future stock price is 1.75 a non-linear (and convex) function of the continuously compounded return his means that the expected stock price 1.35 increases with the variance of the A return). B 1.15 herefore, if we set the expected value of the continuously compounded return 0.95 C equal to the correct expected return (e.g., given by the CAPM 0% say) the 0.75 expected value of the stock price gives a continuously compounded return that is 0.55 higher than 0% (point A versus point B Continuously Compounded Stock Return (X) is the figure) o make the expected price of the stock equal the point B (and therefore give an expected return equal to the correct value) we must reduce the expected value of the continuously compounded return by an amount that depends on the variance. his is what the term (-½ * σ * ) does in the formulae given on the previous slide Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 7 Stock Price = Exp(x) S -60% -50% -40% -30% -0% -10% 0% 10% 0% 30% 40% 50% 60% D he Black-Scholes heory A lognormal distribution is a continuous distribution the number of possible states is infinite (in the binomial case it was just two per period) In the Black-Scholes model the number of assets is just two (the underlying stock and borrowing / lending) exactly as in the binomial example Black and Scholes big, surprising, deep, Nobelprize-winning result is that, under their assumptions, the market is complete Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 8

5 Risk Neutral Probabilities and A-D Prices in Black-Scholes Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 9 Risk Neutral Distribution in the Black-Scholes Model In Black-Scholes, risk neutral distribution: is also lognormal has same volatility parameter (σ) probability density BU mean parameter (µ) is changed so that the expected return on the stock is the riskless interest rate (exactly as in the binomial case) v (like u) is also a normally distributed random variable risk neutral distribution natural distribution Lognormal: E(return on stock) = riskless rate (10%) Lognormal: E(return on stock) = 40% stock price C ( RN ) 1 R = ( r σ ) + σ v If underlying asset has positive risk premium this means that the risk-neutral distribution is shifted to the left Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 10

6 A-D prices in Black-Scholes In the binomial case the A-D price is just the corresponding risk-neutral probability discounted at the riskless rate: s 1 = ˆ π s (1 + r ) ˆ π : RN probability of state s q : A D price for state s s q In B-S, because the distribution of the asset price is continuous, we have a distribution of A-D prices o calculate the distribution of A-D prices in the B-S case we just discount the risk-neutral distribution at the riskless interest rate (as in the binomial case). s f Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 11 A-D Prices in Black-Scholes probability den nsity / state prices risk neutral distribution (RND) A-D prices = RND / (1+r_f) A-D Price density means: price of A-D security that pays 1 if stock price is between 1 and 14 (say) is equal to area under curve between 1 and stock price Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 1

7 he Black-Scholes theory vs. the Black- Scholes formula he Black-Scholes theory their key result is that (under their assumptions) the market is complete and that we can calculate the risk-neutral distribution of the underlying asset. he Black-Scholes formula is the result we get when we apply the theory to the particular problem of valuing European puts and calls. his is much narrower. here are many, many cases when we can apply the theory without being able to use the formula. Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 13 Calculating Black-Scholes price as risk neutral expected payoff discounted at riskless rate Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 14

8 Calculating Option Values in the Black-Scholes Model using Risk Neutral Probabilities o value an option (or any asset) in EIHER the binomial model OR the Black-Scholes model we: calculate the expected cash flow using the risk-neutral probabilities probability density Risk-Neutral Distribution of Stock Price Call Option Payoff X = discount at the riskless stock price rate of interest E.g., for a European call option with exercise price X = 10 we calculate the expected value of the cash flow at maturity using the R-N distribution and discount at the riskless rate Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo tion payoff at maturity opt Calculating the Black-Scholes value of a call he payoff at maturity is zero for S < X and (S-X) for S X Using the RN distribution the discounted expected payoff is*: rf C = e (,0) ˆ Max S X f ( S ) ds { 0 Payoff at RN density of S = 0 + e S X f ˆ ( S ) ds rf { ( ) Payoff when S X is zero X rf ˆ rf ( ) ˆ = e S f S ds e X f ( S ) ds X X rf = e S ˆ f ( S ) ds PV ( X )prob RN ( S X ) X Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 16

9 Calculating the Black-Scholes value of a call the risk-neutral expected payoff on the call discounted at the riskless rate, i.e., the call price is therefore: rf C = e S fˆ( S ) ds PV ( X )prob RN ( S X ) X Evaluating this expression using the lognormal risk-neutral distribution for the Black-Scholes model we obtain the Black- Scholes formula C = SN( d ) PV ( X ) N( d ) 1 N(.) : cumulative standard normal distribution ln( S / PV ( X )) 1 d1 = + σ and d = d1 σ σ Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 17 Calculating Black-Scholes Value by Adding up payoff x RN probability Working out RN probs. and average payoff on call for stock price intervals of 0.1 and then calculating RN expected payoff as sum of av. Payoff x prob. we find call value of vs from Black-Scholes formula: 1 l n ( S / S ) ( ) j 0 r σ f u = j σ u j is N(0,1) shock to continuously compounded return corresponding to stock price S j. *r f is continuously compounded S = 49 X = 50 r f = 5% p.a.* = 0.5 years Volatility=0% p.a. RN_prob RN Prob RNP * Av j S_j u_j S<=S_j S_j-1 <= S = <=S_j Payoff Av_Payoff Payoff Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 18

10 Interpreting the Black-Scholes Formula PV of RN expected proceeds from exercise PV of RN expected cost of exercise C = SN ( d ) - PV( X ) N ( d ) discounted price of bond RN probability RN expected paying exercise of exercise value of stock price when S > X j Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 19 Interpreting the Merton Formula for the Value of Credit Risky Debt In the same way, the Merton formula for the value of credit risky debt can be interpreted as the sum of: the value of the payment (V) in default he value of the payment of the face value (B) in nodefault Bond Payoff at Maturiy default ` no default Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 0 1 value of assets of firm at maturity ( million) D = VN ( d1) PV ( B) 13 N ( d) 13 value in default Riskless PV Risk-neutral prob of no-default

11 Numerical Methods: Monte Carlo Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 1 We need numerical methods when we cannot find a formula for the option value In some cases we can find a formula for the value of an option (e.g., the Black-Scholes formula) BU often, though we continue to use the Black- Scholes theory (and the Black-Scholes risk-neutral distribution) there is no formula for the option price Example: true for almost all American options (except in cases such as call on non-dividend paying stock where American and European options are worth the same) Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo

12 Monte-Carlo Standard technique to calculate the expected value of some function f(x) of a random variable x: How it works: 1. Generate random numbers drawn from the distribution of the the random variable x ( drawings ). For each drawing (x i ) calculate f(x i ) 3. hen simply take the average value of the f(x i ) s Wide variety of important problems (pricing, risk assessment etc.) can be solved using Monte Carlo. Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 3 Option Valuation with Monte-Carlo implementing Monte Carlo option pricing 1. make random drawings from the risk-neutral distribution of the stock price at the maturity of the option. for each stock price calculate the payoff on the option 3. average these payoffs (this gives the risk neutral expected payoff on the option) 4. discount the risk neutral expected payoff at the riskless rate. Key step: will explain how we do this Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 4

13 How to calculate a random drawing of the stock price under the risk-neutral distribution th Step 1: For j trial generate drawing from normally distribution with mean of zero and standard deviation of one ( v ). j Step : Calculate drawing from risk-neutral distribution of stock price as: S = S e 0 ( r 1 σ ) + σ v j Excel will give you the random variables (the v s) and then, for each S j we simply work out the payoff on the option, average the payoffs and discount at the riskless rate Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 5 Monte-Carlo Example first 4 samples: 3-month call Ex. Price = 100, S 0 = 100, σ = 30%, r f = 10% Norm Dist (0,1) random variable v j Stock Price at Maturity S j, * Option Payoff Max(S j, 100,0) Cumulative average Option Payoff Cumulative average discounted at r f * Note: S% 1 r f σ + σ v j = S e j, 0 Spreadsheet available on Portal Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 6

14 Approximating the Risk-Neutral Distribution with Monte-Carlo bility density proba stock price Monte-Carlo (5000 sam ples) Ris k-neutral Distribution of Stock Price Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 7 Calculating the Option Price with Monte-Carlo Monte-Carlo Price (cumulative average) Black-Scholes Price option price number of Monte-Carlo samples Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 8

15 Implementing Monte Carlo software package allows you to carry out Monte-Carlo in Excel even more simply. You will essential in the exercises on basket credit derivatives later in the course and so it is worthwhile finding out now how to use it. Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 9 Summary In Black-Scholes theory market for options (and effectively all claims on underlying asset) is complete his means we can calculate unique A-D prices and risk neutral probabilities We can read the Black-Scholes formula as: RN expected payoff on option discounted at riskless rate OR Cost of replicating portfolio In many cases (e.g., most American options) there is no formula for the option price and we need to use a numerical approach idea: calculate the RN expected payoff and discount at the riskless rate Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 30

16 Key Concepts Market completeness in B-S Lognormal distribution RN distribution in B-S A-D prices in B-S B-S formula as discounted RN expected payoff Delta (hedge ratio) Delta hedging strategy Monte Carlo valuation options Risk Neutral Valuation, the Black-Scholes Model and Monte Carlo 31