Engenharia Financeira é uma área multidisciplinar construída a partir das finanças e economia, matemática, estatística e métodos computacionais. A ênfase de EF & GR Parte I será no uso de modelos estocásticos simples para precificar derivativos ou títulos derivados "derivative securities" de várias classes de ativos, incluindo ações, renda fixa, crédito e securitização de financiamentos habitacionais ou garantias hipotecárias. Nós também consideraremos o papel desempenhado por algumas dessas classes de ativos durante a crise financeira. Uma notável característica deste curso será um módulo de entrevista com Emanuel Derman, o renomado "quant" e autor do best-seller "My Life as a Quant". Esperamos que os estudantes que concluírem o curso comecem a entender a ciência sofisticada ou "rocket science" por trás da engenharia financeira, mas, talvez mais importante, esperamos que eles também entendam as limitações dessa teoria na prática e por que modelos financeiros deveriam sempre ser tratados com um saudável grau de ceticismo. O curso seguinte EF & GR Parte II continuará a desenvolver os modelos de precificação de derivativos, mas também focará na alocação de ativos e otimização de portfólio tanto quanto outras aplicações da engenharia financeira, tais como opções reais, commodities e derivativos de energia e algoritmos de negociações.
An Example: Pricing a European Put on a Futures Contract
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Engenharia Financeira e Gestão de Riscos Parte I
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Option Pricing in the Multi-Period Binomial Model
Derivatives pricing in the binomial model including European and American options; handling dividends; pricing forwards and futures; convergence of the binomial model to Black-Scholes.
Conheça os instrutores
Martin HaughCo-Director, Center for Financial Engineering
Industrial Engineering & Operations Research
Garud IyengarProfessor
Industrial Engineering and Operations Research Department
>> In this module we're going to go through an example very quickly where we
price a European put on a futures contract.
Up until now we've seen how to price European and American options on the
underlying security. Very often in fact though we want to price
options on futures contracts, which are written on the underlying security.
So in fact many of the most liquid options are options on futures contracts.
They include for example the S & P 500 index in the US, the Eurostoxx 50 index in
the Euro zone, the FTSE 100 from the UK and the Nikei 225 from Japan.
In these cases, the underlying security is not actually traded, that is because if
you wanted to actually trade the S & P 500 we would need to trade 500 different
stocks. Now it would be very expensive and time
consuming. So generally we don't trade these indices,
we trade futures on these indices. So what we're going to do is we're going
to price a European put on a futures contract.
So consider the following parameters, we're going to assume the initial stock
price or in this case the initial index price is 100.
We'll assume n equals 10 periods. We will assume a risk free rate of 2%, a
dividend yield of 1% and a sigma of 20%. We're going to assume that the futures
expiration and the option expiration coincide and are equal to 0.5 years.
In practice this is what typically happens.
But of course, for more theory, we could if we like, still price options where the
futures expiration was greater than the option expiration.
We'll be able to obtain the futures price lattice on our spreadsheet, by using the
fact that s n equals f n, and then using the fact for t less than n, f t is equal
to the expected value of f t plus 1, using the risk mutual probabilities.
When we do that, we'll see that we get a put option value of 5.21.
So let's go to the spreadsheet and see what we get.
So here are our initial parameters. We've an initial price of 100, t equals
0.5 years or 6 months. Sigma equals 20%.
We're assuming 10 periods and or, we have our r and dividend yield as well.
So u, d and q are calculated as we showed in an earlier module where we used the
binomial model to approximate geometric boundary in motion and the Black Shoals
Model. So our futures parameters, we're assuming
an expiration of t equals 10. So 10 time periods corresponding with 6
months, or half a year. Over here we can choose what sort of
option we want to price. 1 for a call, minus 1 for a put.
We're assuming a strike of 100, and the expiration, again, is going to be 10
periods. So the option expiration coincides with
the futures expiration. It's going to be a European option.
So here we have our lattice, or stock-price lattice.
In each time period, the stock price grows by a factor of u, or it falls by a factor
of d. If we scroll down, we have the futures
lattice. And in this case, we can see, well, by
definition, f n equals s n at time n. So we see that the futures prices in here
are time t equals 10, agree with the stock prices up here, at time t equals 10.
We then work backwards in this lattice, calculating the futures prices using that
formula we showed on this last slide. Okay.
So now, we have our futures price lattice. We can now price our options, by simply
calculating the payoff of the option at time t equals 10, the expiration.
So in this case it's a put option with strike 100.
So when the stock price is greater than 100, the payoff is 0.
When it is less than 100, we get a payoff of 100 minus the stock price.
So that gives us these values here, and then we can calculate the option by
working backwards in the binomial lattice as we've seen before.
In this case, we find the put option value is equal to $5.212.
It's worth making a point about how these models are used in practice.
In practice, we don't need a model to price liquid options.
Market forces, i.e., supply and demand, actually determines the price of options.
In this case, this amounts to determining , or the implied volatility.
And that is because, if you recall, so the price of a call options c 0 is equal to
the expected value at time 0, using our risk-neutral probabilities of e to the
minus r t times s t minus k, the positive part, or if you like this is, equal to,
the maximum, of s t minus k and 0. Where under the BlackScholes Model s t
equals s 0 e to the r minus sigma squared over 2 times t.
Plus sigma, w t. W t here is your standard Brownian motion.
So w T is a random variable that's got a normal distribution between 0 and variance
t. Anyway, given all of this we recognize
that c 0 is equal to some function of the initial stock price, the risk free rate,
the dividend yield, and I should have included the dividend yield up there.
Sigma, the time to maturity, and k. And in fact we actually know all of these
quantities. We can see them all in the market place.
We know what s 0 is, we know what r is we know what the dividend yield is, we know
what the maturity of the option is and we know what the strike of the option is.
The only thing we don't know is sigma. However in the case of liquid options, we
can actually observe c 0 the price of the option in the market place.
As I said supply and demand is what really sets the price of the liquid options in
the market place. So we can see c 0 and then we can use this
equation to back out the one unknown, which is sigma.
And then this is often called the implied volatility.
And we'll be returning to this later in the course as well.
So at this point you might ask if that's the case, if supply and demand sets the
price of options, why do we need a model? Well we need models for two reasons.
One is to help us price what are called exotic or more illiquid derivative
securities whose prices are not available in the marketplace.
We can also use models to hedge options. And I'll return to hedging later in the
course as well.
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