Valuing a Call Option with an Event-Triggered Sale Rule

# Call option with rule to sell at a certain price if an event occurs


# Call option with rule to sell at a certain price if an event occurs












I want to value a special type of call option on a stock. It's like a regular european vanilla call, but with the added rule that if a certain event occurs (that is approx 10% probability) then they must sell the option for the whichever is lowest of (i) Current option price (ii) Option price it was bought for.

This rule makes the option less valuable. I wonder how much less?

I'm familiar with black scholes, GBM and monte carlo pricing of options.

Tools I have: Excel and R.

Edit 1: The event is independent of the stock price. The mean daily return is 2% and the yearly volatility is 29%.

Edit 2: The option matures in 3.25 years. Assume the event takes place with 10% probability on year 1 and year 2 and year 3.

## Answer by Ivan (score 1)

https://quant.stackexchange.com/a/38605

Pretty complex, but here's a way to simplify: this option is effectively a standard maturity $T$ European call option $C_T$ minus a compound call on that call option with strike $k_c = PV_{t_0}(C_T)$ that is exercised only if your event $E$ occurs at a time $\tau \leq T$ i.e. $CoC_\tau.1_{\tau \leq T}$ where $CoC_\tau=Max(PV_\tau(C_T)-k_c,0)$.

So "all" you need to do to value the PV adjustment $A_{PV}$ is dig up a compound call option approximation (e.g. in Haug's Complete Guide to Option Pricing Formulas) and plug that into a formula of the form (assuming independence between $E$ and $S$, and in discrete time formulation for practicality -you can do this at daily or weekly or monthly points in practice).

$A_{PV} = \Sigma_{i=1...N}PV_{t_0}(CoC_{t_i}).p(\tau = t_i)$

And your product should be worth $C_T - A_{PV}$

## Answer by Attack68 (score 0)

https://quant.stackexchange.com/a/38033

Off the top of my head this seems like a solution to the equation:

E[Adj Option Price] = P(no event) * Option Price + P(event) * E[loss on event]

Then you need to calculate those items either numerically or analytically. if the probability of event is variable you also have to take the expectation over the RHS if it impacts the expected loss on event non-linearly. This might happen if you are modelling the event with, say a Poisson distribution.

## Answer by jacob (score 0)

https://quant.stackexchange.com/a/38035

Idea of pricing method: Take historical stock prices, estimate $\hat \mu$ and $\hat \sigma$. Specify strike and 3 years maturity for a plain vanilla call option to get the price $P_0$. Put these parameters in a Geometric Brownian Motion (GBM) to get $M$ different stock paths. For example M=1000. Maturity is 3 years. Put these price paths into the matrix `S.mat` which has M rows and T=250*3=750 columns.

Let's split up time intwo three steps: year 1, 2 and 3 which stops at T=250, T=500 and T=750 respectively.

There are M different paths and each one has their own list of stock prices. For example path $p=1$ has the following list: $(S^1_{t=0}, S^1_{t=1}, ..., S^1_{t=750})$ and it can be found in the first row of `S.mat`. Taking the `stdev()` of each path -- each row in the matrix -- gives a list of volatilities: $(\hat \sigma^1, \hat \sigma^2, ...., \hat \sigma^M)$.

After 1 year we have a list of stock prices that day: $(S^1_{t=250}, S^2_{t=250}, ..., S^M_{t=250})$. This is the 250th column of `S.mat`.

Now we can use black scholes:

- We have a list of current stock prices (the 250th column of `S.mat`).

- We have a list of volatilities (the stdev of each row in `S.mat`).

- So we can get a list of M number of black scholes prices out of these two lists. All we need to do is to specify $K$ and time left (it is 2.25 years left). Each volatility $\hat \sigma^p$ in that list gives an option price at day 250: $P^1_{t=250}, P^2_{t=250}, ... P^p_{t=250}$. The mean of this list is $\bar P_{t=250}$.

The rule can be stated as: if an event U occurs, the owner of the option is forces to sell it for the least of $P_0$ and whatever the price of the option is that day, which in expectation is $\bar P_{t=250}$. This option has some real value and some time value.